mechanisms predictive modeling: Topics by WorldWideScience.org

Sample records for mechanisms predictive modeling

Prediction of mechanical properties for hexagonal boron nitride nanosheets using molecular mechanics model

Energy Technology Data Exchange (ETDEWEB)

Natsuki, Toshiaki [Shinshu University, Faculty of Textile Science and Technology, Ueda (Japan); Shinshu University, Institute of Carbon Science and Technology, Nagano (Japan); Natsuki, Jun [Shinshu University, Institute of Carbon Science and Technology, Nagano (Japan)

2017-04-15

Mechanical behaviors of nanomaterials are not easy to be evaluated in the laboratory because of their extremely small size and difficulty controlling. Thus, a suitable model for the estimation of the mechanical properties for nanomaterials becomes very important. In this study, the elastic properties of boron nitride (BN) nanosheets, including the elastic modulus, the shear modulus, and the Poisson's ratio, are predicted using a molecular mechanics model. The molecular mechanics force filed is established to directly incorporate the Morse potential function into the constitutive model of nanostructures. According to the molecular mechanics model, the chirality effect of hexagonal BN nanosheets on the elastic modulus is investigated through a closed-form solution. The simulated result shows that BN nanosheets exhibit an isotropic elastic property. The present analysis yields a set of very simple formulas and is able to be served as a good approximation on the mechanical properties for the BN nanosheets. (orig.)
Prediction of mechanical properties for hexagonal boron nitride nanosheets using molecular mechanics model

International Nuclear Information System (INIS)

Natsuki, Toshiaki; Natsuki, Jun

2017-01-01

Mechanical behaviors of nanomaterials are not easy to be evaluated in the laboratory because of their extremely small size and difficulty controlling. Thus, a suitable model for the estimation of the mechanical properties for nanomaterials becomes very important. In this study, the elastic properties of boron nitride (BN) nanosheets, including the elastic modulus, the shear modulus, and the Poisson's ratio, are predicted using a molecular mechanics model. The molecular mechanics force filed is established to directly incorporate the Morse potential function into the constitutive model of nanostructures. According to the molecular mechanics model, the chirality effect of hexagonal BN nanosheets on the elastic modulus is investigated through a closed-form solution. The simulated result shows that BN nanosheets exhibit an isotropic elastic property. The present analysis yields a set of very simple formulas and is able to be served as a good approximation on the mechanical properties for the BN nanosheets. (orig.)
Degradation model and application in life prediction of rotating-mechanism

International Nuclear Information System (INIS)

Zhou Yuhui

2009-01-01

The degradation data can provide additional information beyond that provided by the failure observations, both sets of observations need to be considered when doing inference on the statistical parameters of the product and system lifetime distributions. By the degradation model showing the wear out failure, the predicted results of mechanism life is more accurate. Strength is one of the important capabilities of the rotating mechanism. In this paper, the degradation data of strength are described as a stochastic process model. Accelerated tests expose the products to greater environmental stress levels so that we can obtain lifetime and degradation measurements in a more timely fashion. Using the Best Linear Unbiased Estimation (BLUE) Method, the parameters under the degradation path were estimated from the accelerated life test (ALT) data of the rotating mechanism. Based on solving the singularity of degradation equation, at any time the reliability is estimated by the using the strength-stress interference theory. So we can predict the life of the rotating mechanism. (authors)
Quality prediction modeling for sintered ores based on mechanism models of sintering and extreme learning machine based error compensation

Science.gov (United States)

Tiebin, Wu; Yunlian, Liu; Xinjun, Li; Yi, Yu; Bin, Zhang

2018-06-01

Aiming at the difficulty in quality prediction of sintered ores, a hybrid prediction model is established based on mechanism models of sintering and time-weighted error compensation on the basis of the extreme learning machine (ELM). At first, mechanism models of drum index, total iron, and alkalinity are constructed according to the chemical reaction mechanism and conservation of matter in the sintering process. As the process is simplified in the mechanism models, these models are not able to describe high nonlinearity. Therefore, errors are inevitable. For this reason, the time-weighted ELM based error compensation model is established. Simulation results verify that the hybrid model has a high accuracy and can meet the requirement for industrial applications.
Predictive modeling capabilities from incident powder and laser to mechanical properties for laser directed energy deposition

Science.gov (United States)

Shin, Yung C.; Bailey, Neil; Katinas, Christopher; Tan, Wenda

2018-01-01

This paper presents an overview of vertically integrated comprehensive predictive modeling capabilities for directed energy deposition processes, which have been developed at Purdue University. The overall predictive models consist of vertically integrated several modules, including powder flow model, molten pool model, microstructure prediction model and residual stress model, which can be used for predicting mechanical properties of additively manufactured parts by directed energy deposition processes with blown powder as well as other additive manufacturing processes. Critical governing equations of each model and how various modules are connected are illustrated. Various illustrative results along with corresponding experimental validation results are presented to illustrate the capabilities and fidelity of the models. The good correlations with experimental results prove the integrated models can be used to design the metal additive manufacturing processes and predict the resultant microstructure and mechanical properties.
Predictive modeling capabilities from incident powder and laser to mechanical properties for laser directed energy deposition

Science.gov (United States)

Shin, Yung C.; Bailey, Neil; Katinas, Christopher; Tan, Wenda

2018-05-01

This paper presents an overview of vertically integrated comprehensive predictive modeling capabilities for directed energy deposition processes, which have been developed at Purdue University. The overall predictive models consist of vertically integrated several modules, including powder flow model, molten pool model, microstructure prediction model and residual stress model, which can be used for predicting mechanical properties of additively manufactured parts by directed energy deposition processes with blown powder as well as other additive manufacturing processes. Critical governing equations of each model and how various modules are connected are illustrated. Various illustrative results along with corresponding experimental validation results are presented to illustrate the capabilities and fidelity of the models. The good correlations with experimental results prove the integrated models can be used to design the metal additive manufacturing processes and predict the resultant microstructure and mechanical properties.
Predicting the lung compliance of mechanically ventilated patients via statistical modeling

International Nuclear Information System (INIS)

Ganzert, Steven; Kramer, Stefan; Guttmann, Josef

2012-01-01

To avoid ventilator associated lung injury (VALI) during mechanical ventilation, the ventilator is adjusted with reference to the volume distensibility or ‘compliance’ of the lung. For lung-protective ventilation, the lung should be inflated at its maximum compliance, i.e. when during inspiration a maximal intrapulmonary volume change is achieved by a minimal change of pressure. To accomplish this, one of the main parameters is the adjusted positive end-expiratory pressure (PEEP). As changing the ventilator settings usually produces an effect on patient's lung mechanics with a considerable time delay, the prediction of the compliance change associated with a planned change of PEEP could assist the physician at the bedside. This study introduces a machine learning approach to predict the nonlinear lung compliance for the individual patient by Gaussian processes, a probabilistic modeling technique. Experiments are based on time series data obtained from patients suffering from acute respiratory distress syndrome (ARDS). With a high hit ratio of up to 93%, the learned models could predict whether an increase/decrease of PEEP would lead to an increase/decrease of the compliance. However, the prediction of the complete pressure–volume relation for an individual patient has to be improved. We conclude that the approach is well suitable for the given problem domain but that an individualized feature selection should be applied for a precise prediction of individual pressure–volume curves. (paper)
Model Predictive Vibration Control Efficient Constrained MPC Vibration Control for Lightly Damped Mechanical Structures

CERN Document Server

Takács, Gergely

2012-01-01

Real-time model predictive controller (MPC) implementation in active vibration control (AVC) is often rendered difficult by fast sampling speeds and extensive actuator-deformation asymmetry. If the control of lightly damped mechanical structures is assumed, the region of attraction containing the set of allowable initial conditions requires a large prediction horizon, making the already computationally demanding on-line process even more complex. Model Predictive Vibration Control provides insight into the predictive control of lightly damped vibrating structures by exploring computationally efficient algorithms which are capable of low frequency vibration control with guaranteed stability and constraint feasibility. In addition to a theoretical primer on active vibration damping and model predictive control, Model Predictive Vibration Control provides a guide through the necessary steps in understanding the founding ideas of predictive control applied in AVC such as: · the implementation of ...
Predictive Modeling of Mechanical Properties of Welded Joints Based on Dynamic Fuzzy RBF Neural Network

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ZHANG Yongzhi

2016-10-01

Full Text Available A dynamic fuzzy RBF neural network model was built to predict the mechanical properties of welded joints, and the purpose of the model was to overcome the shortcomings of static neural networks including structural identification, dynamic sample training and learning algorithm. The structure and parameters of the model are no longer head of default, dynamic adaptive adjustment in the training, suitable for dynamic sample data for learning, learning algorithm introduces hierarchical learning and fuzzy rule pruning strategy, to accelerate the training speed of model and make the model more compact. Simulation of the model was carried out by using three kinds of thickness and different process TC4 titanium alloy TIG welding test data. The results show that the model has higher prediction accuracy, which is suitable for predicting the mechanical properties of welded joints, and has opened up a new way for the on-line control of the welding process.
Prediction of Sliding Friction Coefficient Based on a Novel Hybrid Molecular-Mechanical Model.

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Zhang, Xiaogang; Zhang, Yali; Wang, Jianmei; Sheng, Chenxing; Li, Zhixiong

2018-08-01

Sliding friction is a complex phenomenon which arises from the mechanical and molecular interactions of asperities when examined in a microscale. To reveal and further understand the effects of micro scaled mechanical and molecular components of friction coefficient on overall frictional behavior, a hybrid molecular-mechanical model is developed to investigate the effects of main factors, including different loads and surface roughness values, on the sliding friction coefficient in a boundary lubrication condition. Numerical modelling was conducted using a deterministic contact model and based on the molecular-mechanical theory of friction. In the contact model, with given external loads and surface topographies, the pressure distribution, real contact area, and elastic/plastic deformation of each single asperity contact were calculated. Then asperity friction coefficient was predicted by the sum of mechanical and molecular components of friction coefficient. The mechanical component was mainly determined by the contact width and elastic/plastic deformation, and the molecular component was estimated as a function of the contact area and interfacial shear stress. Numerical results were compared with experimental results and a good agreement was obtained. The model was then used to predict friction coefficients in different operating and surface conditions. Numerical results explain why applied load has a minimum effect on the friction coefficients. They also provide insight into the effect of surface roughness on the mechanical and molecular components of friction coefficients. It is revealed that the mechanical component dominates the friction coefficient when the surface roughness is large (Rq > 0.2 μm), while the friction coefficient is mainly determined by the molecular component when the surface is relatively smooth (Rq < 0.2 μm). Furthermore, optimal roughness values for minimizing the friction coefficient are recommended.
Theoretical models to predict the mechanical behavior of thick composite tubes

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Volnei Tita

2012-02-01

Full Text Available This paper shows theoretical models (analytical formulations to predict the mechanical behavior of thick composite tubes and how some parameters can influence this behavior. Thus, firstly, it was developed the analytical formulations for a pressurized tube made of composite material with a single thick ply and only one lamination angle. For this case, the stress distribution and the displacement fields are investigated as function of different lamination angles and reinforcement volume fractions. The results obtained by the theoretical model are physic consistent and coherent with the literature information. After that, the previous formulations are extended in order to predict the mechanical behavior of a thick laminated tube. Both analytical formulations are implemented as a computational tool via Matlab code. The results obtained by the computational tool are compared to the finite element analyses, and the stress distribution is considered coherent. Moreover, the engineering computational tool is used to perform failure analysis, using different types of failure criteria, which identifies the damaged ply and the mode of failure.
A model for predicting pellet-cladding interaction induced fuel rod failure, based on nonlinear fracture mechanics

International Nuclear Information System (INIS)

Jernkvist, L.O.

1993-01-01

A model for predicting pellet-cladding mechanical interaction induced fuel rod failure, suitable for implementation in finite element fuel-performance codes, is presented. Cladding failure is predicted by explicitly modelling the propagation of radial cracks under varying load conditions. Propagation is assumed to be due to either iodine induced stress corrosion cracking or ductile fracture. Nonlinear fracture mechanics concepts are utilized in modelling these two mechanisms of crack growth. The novelty of this approach is that the development of cracks, which may ultimately lead to fuel rod failure, can be treated as a dynamic and time-dependent process. The influence of cyclic loading, ramp rates and material creep on the failure mechanism can thereby be investigated. Results of numerical calculations, in which the failure model has been used to study the dependence of cladding creep rate on crack propagation velocity, are presented. (author)
Coupled hygrothermal, electrochemical, and mechanical modelling for deterioration prediction in reinforced cementitious materials

DEFF Research Database (Denmark)

Michel, Alexander; Geiker, Mette Rica; Lepech, M.

2017-01-01

In this paper a coupled hygrothermal, electrochemical, and mechanical modelling approach for the deterioration prediction in cementitious materials is briefly outlined. Deterioration prediction is thereby based on coupled modelling of (i) chemical processes including among others transport of hea......, i.e. information, such as such as corrosion current density, damage state of concrete cover, etc., are constantly exchanged between the models....... and matter as well as phase assemblage on the nano and micro scale, (ii) corrosion of steel including electrochemical processes at the reinforcement surface, and (iii) material performance including corrosion- and load-induced damages on the meso and macro scale. The individual FEM models are fully coupled...
Prediction of high airway pressure using a non-linear autoregressive model of pulmonary mechanics.

Science.gov (United States)

Langdon, Ruby; Docherty, Paul D; Schranz, Christoph; Chase, J Geoffrey

2017-11-02

For mechanically ventilated patients with acute respiratory distress syndrome (ARDS), suboptimal PEEP levels can cause ventilator induced lung injury (VILI). In particular, high PEEP and high peak inspiratory pressures (PIP) can cause over distension of alveoli that is associated with VILI. However, PEEP must also be sufficient to maintain recruitment in ARDS lungs. A lung model that accurately and precisely predicts the outcome of an increase in PEEP may allow dangerous high PIP to be avoided, and reduce the incidence of VILI. Sixteen pressure-flow data sets were collected from nine mechanically ventilated ARDs patients that underwent one or more recruitment manoeuvres. A nonlinear autoregressive (NARX) model was identified on one or more adjacent PEEP steps, and extrapolated to predict PIP at 2, 4, and 6 cmH 2 O PEEP horizons. The analysis considered whether the predicted and measured PIP exceeded a threshold of 40 cmH 2 O. A direct comparison of the method was made using the first order model of pulmonary mechanics (FOM(I)). Additionally, a further, more clinically appropriate method for the FOM was tested, in which the FOM was trained on a single PEEP prior to prediction (FOM(II)). The NARX model exhibited very high sensitivity (> 0.96) in all cases, and a high specificity (> 0.88). While both FOM methods had a high specificity (> 0.96), the sensitivity was much lower, with a mean of 0.68 for FOM(I), and 0.82 for FOM(II). Clinically, false negatives are more harmful than false positives, as a high PIP may result in distension and VILI. Thus, the NARX model may be more effective than the FOM in allowing clinicians to reduce the risk of applying a PEEP that results in dangerously high airway pressures.
Automatic generation of predictive dynamic models reveals nuclear phosphorylation as the key Msn2 control mechanism.

Science.gov (United States)

Sunnåker, Mikael; Zamora-Sillero, Elias; Dechant, Reinhard; Ludwig, Christina; Busetto, Alberto Giovanni; Wagner, Andreas; Stelling, Joerg

2013-05-28

Predictive dynamical models are critical for the analysis of complex biological systems. However, methods to systematically develop and discriminate among systems biology models are still lacking. We describe a computational method that incorporates all hypothetical mechanisms about the architecture of a biological system into a single model and automatically generates a set of simpler models compatible with observational data. As a proof of principle, we analyzed the dynamic control of the transcription factor Msn2 in Saccharomyces cerevisiae, specifically the short-term mechanisms mediating the cells' recovery after release from starvation stress. Our method determined that 12 of 192 possible models were compatible with available Msn2 localization data. Iterations between model predictions and rationally designed phosphoproteomics and imaging experiments identified a single-circuit topology with a relative probability of 99% among the 192 models. Model analysis revealed that the coupling of dynamic phenomena in Msn2 phosphorylation and transport could lead to efficient stress response signaling by establishing a rate-of-change sensor. Similar principles could apply to mammalian stress response pathways. Systematic construction of dynamic models may yield detailed insight into nonobvious molecular mechanisms.
Theoretical model for the hydrogen-material interaction as a basis for prediction of the material mechanical properties

International Nuclear Information System (INIS)

Indeitsev, D.A.; Polyanskiy, V.A.; Sukhanov, A.A.; Belyaev, A.A.

2009-01-01

The natural law concentration of hydrogen inside the materials has a distribution over the different binding energies. This distribution is changing under the mechanical tension. The model of interaction of the small hydrogen concentration with materials provides one with an instrument for modeling the materials fatigue and destruction, as well as the prediction of material properties during exploitation. The well-known models are of the phenomenological nature. However if one takes into account the physical mechanism then one obtains an accurate model and the instrument for the reliable prediction. The two-continuum model of the solid material is a substantiation for the present study. This model describes the interaction between the low concentration of hydrogen and the material. The redistribution of the hydrogen between the different binding energy levels is taken into account, too
Thermo-mechanical simulations of early-age concrete cracking with durability predictions

Science.gov (United States)

Havlásek, Petr; Šmilauer, Vít; Hájková, Karolina; Baquerizo, Luis

2017-09-01

Concrete performance is strongly affected by mix design, thermal boundary conditions, its evolving mechanical properties, and internal/external restraints with consequences to possible cracking with impaired durability. Thermo-mechanical simulations are able to capture those relevant phenomena and boundary conditions for predicting temperature, strains, stresses or cracking in reinforced concrete structures. In this paper, we propose a weakly coupled thermo-mechanical model for early age concrete with an affinity-based hydration model for thermal part, taking into account concrete mix design, cement type and thermal boundary conditions. The mechanical part uses B3/B4 model for concrete creep and shrinkage with isotropic damage model for cracking, able to predict a crack width. All models have been implemented in an open-source OOFEM software package. Validations of thermo-mechanical simulations will be presented on several massive concrete structures, showing excellent temperature predictions. Likewise, strain validation demonstrates good predictions on a restrained reinforced concrete wall and concrete beam. Durability predictions stem from induction time of reinforcement corrosion, caused by carbonation and/or chloride ingress influenced by crack width. Reinforcement corrosion in concrete struts of a bridge will serve for validation.
Impact of a new condensed toluene mechanism on air quality model predictions in the US

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G. Sarwar

2011-03-01

Full Text Available A new condensed toluene mechanism is incorporated into the Community Multiscale Air Quality Modeling system. Model simulations are performed using the CB05 chemical mechanism containing the existing (base and the new toluene mechanism for the western and eastern US for a summer month. With current estimates of tropospheric emission burden, the new toluene mechanism increases monthly mean daily maximum 8-h ozone by 1.0–3.0 ppbv in Los Angeles, Portland, Seattle, Chicago, Cleveland, northeastern US, and Detroit compared to that with the base toluene chemistry. It reduces model mean bias for ozone at elevated observed ozone concentrations. While the new mechanism increases predicted ozone, it does not enhance ozone production efficiency. A sensitivity study suggests that it can further enhance ozone if elevated toluene emissions are present. While it increases in-cloud secondary organic aerosol substantially, its impact on total fine particle mass concentration is small.
Failure Predictions for VHTR Core Components using a Probabilistic Contiuum Damage Mechanics Model

Energy Technology Data Exchange (ETDEWEB)

Fok, Alex

2013-10-30

The proposed work addresses the key research need for the development of constitutive models and overall failure models for graphite and high temperature structural materials, with the long-term goal being to maximize the design life of the Next Generation Nuclear Plant (NGNP). To this end, the capability of a Continuum Damage Mechanics (CDM) model, which has been used successfully for modeling fracture of virgin graphite, will be extended as a predictive and design tool for the core components of the very high- temperature reactor (VHTR). Specifically, irradiation and environmental effects pertinent to the VHTR will be incorporated into the model to allow fracture of graphite and ceramic components under in-reactor conditions to be modeled explicitly using the finite element method. The model uses a combined stress-based and fracture mechanics-based failure criterion, so it can simulate both the initiation and propagation of cracks. Modern imaging techniques, such as x-ray computed tomography and digital image correlation, will be used during material testing to help define the baseline material damage parameters. Monte Carlo analysis will be performed to address inherent variations in material properties, the aim being to reduce the arbitrariness and uncertainties associated with the current statistical approach. The results can potentially contribute to the current development of American Society of Mechanical Engineers (ASME) codes for the design and construction of VHTR core components.
Achilles tendons from decorin- and biglycan-null mouse models have inferior mechanical and structural properties predicted by an image-based empirical damage model.

Science.gov (United States)

Gordon, J A; Freedman, B R; Zuskov, A; Iozzo, R V; Birk, D E; Soslowsky, L J

2015-07-16

Achilles tendons are a common source of pain and injury, and their pathology may originate from aberrant structure function relationships. Small leucine rich proteoglycans (SLRPs) influence mechanical and structural properties in a tendon-specific manner. However, their roles in the Achilles tendon have not been defined. The objective of this study was to evaluate the mechanical and structural differences observed in mouse Achilles tendons lacking class I SLRPs; either decorin or biglycan. In addition, empirical modeling techniques based on mechanical and image-based measures were employed. Achilles tendons from decorin-null (Dcn(-/-)) and biglycan-null (Bgn(-/-)) C57BL/6 female mice (N=102) were used. Each tendon underwent a dynamic mechanical testing protocol including simultaneous polarized light image capture to evaluate both structural and mechanical properties of each Achilles tendon. An empirical damage model was adapted for application to genetic variation and for use with image based structural properties to predict tendon dynamic mechanical properties. We found that Achilles tendons lacking decorin and biglycan had inferior mechanical and structural properties that were age dependent; and that simple empirical models, based on previously described damage models, were predictive of Achilles tendon dynamic modulus in both decorin- and biglycan-null mice. Copyright © 2015 Elsevier Ltd. All rights reserved.

An Analytical Model for Fatigue Life Prediction Based on Fracture Mechanics and Crack Closure

DEFF Research Database (Denmark)

Ibsø, Jan Behrend; Agerskov, Henning

1996-01-01

test specimens are compared with fatigue life predictions using a fracture mechanics approach. In the calculation of the fatigue life, the influence of the welding residual stresses and crack closure on the fatigue crack growth is considered. A description of the crack closure model for analytical...... of the analytical fatigue lives. Both the analytical and experimental results obtained show that the Miner rule may give quite unconservative predictions of the fatigue life for the types of stochastic loading studied....... determination of the fatigue life is included. Furthermore, the results obtained in studies of the various parameters that have an influence on the fatigue life, are given. A very good agreement between experimental and analytical results is obtained, when the crack closure model is used in determination...
An Analytical Model for Fatigue Life Prediction Based on Fracture Mechanics and Crack Closure

DEFF Research Database (Denmark)

Ibsø, Jan Behrend; Agerskov, Henning

1996-01-01

test specimens are compared with fatigue life predictions using a fracture mechanics approach. In the calculation of the fatigue life, the influence of the welding residual stresses and crack closure on the fatigue crack growth is considered. A description of the crack closure model for analytical...... determination of the fatigue life is included. Furthermore, the results obtained in studies of the various parameters that have an influence on the fatigue life, are given. A very good agreement between experimental and analytical results is obtained, when the crack closure model is used in determination...... of the analytical fatigue lives. Both the analytical and experimental results obtained show that the Miner rule may give quite unconservative predictions of the fatigue life for the types of stochastic loading studied....
Predictive Modeling of a Paradigm Mechanical Cooling Tower Model: II. Optimal Best-Estimate Results with Reduced Predicted Uncertainties

Directory of Open Access Journals (Sweden)

Ruixian Fang

2016-09-01

Full Text Available This work uses the adjoint sensitivity model of the counter-flow cooling tower derived in the accompanying PART I to obtain the expressions and relative numerical rankings of the sensitivities, to all model parameters, of the following model responses: (i outlet air temperature; (ii outlet water temperature; (iii outlet water mass flow rate; and (iv air outlet relative humidity. These sensitivities are subsequently used within the “predictive modeling for coupled multi-physics systems” (PM_CMPS methodology to obtain explicit formulas for the predicted optimal nominal values for the model responses and parameters, along with reduced predicted standard deviations for the predicted model parameters and responses. These explicit formulas embody the assimilation of experimental data and the “calibration” of the model’s parameters. The results presented in this work demonstrate that the PM_CMPS methodology reduces the predicted standard deviations to values that are smaller than either the computed or the experimentally measured ones, even for responses (e.g., the outlet water flow rate for which no measurements are available. These improvements stem from the global characteristics of the PM_CMPS methodology, which combines all of the available information simultaneously in phase-space, as opposed to combining it sequentially, as in current data assimilation procedures.
Mechanics Model of Plug Welding

Science.gov (United States)

Zuo, Q. K.; Nunes, A. C., Jr.

2015-01-01

An analytical model has been developed for the mechanics of friction plug welding. The model accounts for coupling of plastic deformation (material flow) and thermal response (plastic heating). The model predictions of the torque, energy, and pull force on the plug were compared to the data of a recent experiment, and the agreements between predictions and data are encouraging.
Predicting the Mechanical Properties of Viscose/Lycra Knitted Fabrics Using Fuzzy Technique

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Ismail Hossain

2016-01-01

Full Text Available The main objective of this research is to predict the mechanical properties of viscose/lycra plain knitted fabrics by using fuzzy expert system. In this study, a fuzzy prediction model has been built based on knitting stitch length, yarn count, and yarn tenacity as input variables and fabric mechanical properties specially bursting strength as an output variable. The factors affecting the bursting strength of viscose knitted fabrics are very nonlinear. Hence, it is very challenging for scientists and engineers to create an exact model efficiently by mathematical or statistical model. Alternatively, developing a prediction model via ANN and ANFIS techniques is also difficult and time consuming process due to a large volume of trial data. In this context, fuzzy expert system (FES is the promising modeling tool in a quality modeling as FES can map effectively in nonlinear domain with minimum experimental data. The model derived in the present study has been validated by experimental data. The mean absolute error and coefficient of determination between the actual bursting strength and that predicted by the fuzzy model were found to be 2.60% and 0.961, respectively. The results showed that the developed fuzzy model can be applied effectively for the prediction of fabric mechanical properties.
Biofilm Formation Mechanisms of Pseudomonas aeruginosa Predicted via Genome-Scale Kinetic Models of Bacterial Metabolism

Science.gov (United States)

2016-03-15

RESEARCH ARTICLE Biofilm Formation Mechanisms of Pseudomonas aeruginosa Predicted via Genome-Scale Kinetic Models of Bacterial Metabolism Francisco G...jaques.reifman.civ@mail.mil Abstract A hallmark of Pseudomonas aeruginosa is its ability to establish biofilm -based infections that are difficult to...eradicate. Biofilms are less susceptible to host inflammatory and immune responses and have higher antibiotic tolerance than free-living planktonic
Technical report on micro-mechanical versus conventional modelling in non-linear fracture mechanics

International Nuclear Information System (INIS)

2001-07-01

While conventional fracture mechanics is capable of predicting crack growth behaviour if sufficient experimental observations are available, micro-mechanical modelling can both increase the accuracy of these predictions and model phenomena that are inaccessible by the conventional theory such as the ductile-cleavage temperature transition. A common argument against micro-mechanical modelling is that it is too complicated for use in routine engineering applications. This is both a computational and an educational problem. That micro-mechanical modelling is unnecessarily complicated is certainly true in many situations. The on-going development of micro-mechanical models, computational algorithms and computer speed will however most probably diminish the computational problem rather rapidly. Compare for instance the rate of development of computational methods for structural analysis. Meanwhile micro-mechanical modelling may serve as a tool by which more simplified engineering methods can be validated. The process of receiving a wide acceptance of the new methods is probably much slower. This involves many steps. First the research community must be in reasonable agreement on the methods and their use. Then the methods have to be implemented into computer software and into code procedures. The development and acceptance of conventional fracture mechanics may serve as an historical example of the time required before a new methodology has received a wide usage. The CSNI Working Group on Integrity and Ageing (IAGE) decided to carry out a report on micro-mechanical modeling to promote this promising and valuable technique. The report presents a comparison with non-linear fracture mechanics and highlights key aspects that could lead to a better knowledge and accurate predictions. Content: - 1. Introduction; - 2. Concepts of non-linear fracture mechanics with point crack tip modelling; - 3. Micro-mechanical models for cleavage fracture; - 4, Micro-mechanical modelling of
Non-Fourier based thermal-mechanical tissue damage prediction for thermal ablation.

Science.gov (United States)

Li, Xin; Zhong, Yongmin; Smith, Julian; Gu, Chengfan

2017-01-02

Prediction of tissue damage under thermal loads plays important role for thermal ablation planning. A new methodology is presented in this paper by combing non-Fourier bio-heat transfer, constitutive elastic mechanics as well as non-rigid motion of dynamics to predict and analyze thermal distribution, thermal-induced mechanical deformation and thermal-mechanical damage of soft tissues under thermal loads. Simulations and comparison analysis demonstrate that the proposed methodology based on the non-Fourier bio-heat transfer can account for the thermal-induced mechanical behaviors of soft tissues and predict tissue thermal damage more accurately than classical Fourier bio-heat transfer based model.
A coupled diffusion-fluid pressure model to predict cell density distribution for cells encapsulated in a porous hydrogel scaffold under mechanical loading.

Science.gov (United States)

Zhao, Feihu; Vaughan, Ted J; Mc Garrigle, Myles J; McNamara, Laoise M

2017-10-01

Tissue formation within tissue engineering (TE) scaffolds is preceded by growth of the cells throughout the scaffold volume and attachment of cells to the scaffold substrate. It is known that mechanical stimulation, in the form of fluid perfusion or mechanical strain, enhances cell differentiation and overall tissue formation. However, due to the complex multi-physics environment of cells within TE scaffolds, cell transport under mechanical stimulation is not fully understood. Therefore, in this study, we have developed a coupled multiphysics model to predict cell density distribution in a TE scaffold. In this model, cell transport is modelled as a thermal conduction process, which is driven by the pore fluid pressure under applied loading. As a case study, the model is investigated to predict the cell density patterns of pre-osteoblasts MC3T3-e1 cells under a range of different loading regimes, to obtain an understanding of desirable mechanical stimulation that will enhance cell density distribution within TE scaffolds. The results of this study have demonstrated that fluid perfusion can result in a higher cell density in the scaffold region closed to the outlet, while cell density distribution under mechanical compression was similar with static condition. More importantly, the study provides a novel computational approach to predict cell distribution in TE scaffolds under mechanical loading. Copyright © 2017 Elsevier Ltd. All rights reserved.
Computational modeling predicts the ionic mechanism of late-onset responses in Unipolar Brush Cells

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Sathyaa eSubramaniyam

2014-08-01

Full Text Available Unipolar Brush Cells (UBCs have been suggested to have a strong impact on cerebellar granular layer functioning, yet the corresponding cellular mechanisms remain poorly understood. UBCs have recently been reported to generate, in addition to early-onset glutamatergic synaptic responses, a late-onset response (LOR composed of a slow depolarizing ramp followed by a spike burst (Locatelli et al., 2013. The LOR activates as a consequence of synaptic activity and involves an intracellular cascade modulating H- and TRP-current gating. In order to assess the LOR mechanisms, we have developed a UBC multi-compartmental model (including soma, dendrite, initial segment and axon incorporating biologically realistic representations of ionic currents and a generic coupling mechanism regulating TRP and H channel gating. The model finely reproduced UBC responses to current injection, including a low-threshold spike sustained by CaLVA currents, a persistent discharge sustained by CaHVA currents, and a rebound burst following hyperpolarization sustained by H- and CaLVA-currents. Moreover, the model predicted that H- and TRP-current regulation was necessary and sufficient to generate the LOR and its dependence on the intensity and duration of mossy fiber activity. Therefore, the model showed that, using a basic set of ionic channels, UBCs generate a rich repertoire of delayed bursts, which could take part to the formation of tunable delay-lines in the local microcircuit.
Computational modeling predicts the ionic mechanism of late-onset responses in unipolar brush cells.

Science.gov (United States)

Subramaniyam, Sathyaa; Solinas, Sergio; Perin, Paola; Locatelli, Francesca; Masetto, Sergio; D'Angelo, Egidio

2014-01-01

Unipolar Brush Cells (UBCs) have been suggested to play a critical role in cerebellar functioning, yet the corresponding cellular mechanisms remain poorly understood. UBCs have recently been reported to generate, in addition to early-onset glutamate receptor-dependent synaptic responses, a late-onset response (LOR) composed of a slow depolarizing ramp followed by a spike burst (Locatelli et al., 2013). The LOR activates as a consequence of synaptic activity and involves an intracellular cascade modulating H- and TRP-current gating. In order to assess the LOR mechanisms, we have developed a UBC multi-compartmental model (including soma, dendrite, initial segment, and axon) incorporating biologically realistic representations of ionic currents and a cytoplasmic coupling mechanism regulating TRP and H channel gating. The model finely reproduced UBC responses to current injection, including a burst triggered by a low-threshold spike (LTS) sustained by CaLVA currents, a persistent discharge sustained by CaHVA currents, and a rebound burst following hyperpolarization sustained by H- and CaLVA-currents. Moreover, the model predicted that H- and TRP-current regulation was necessary and sufficient to generate the LOR and its dependence on the intensity and duration of mossy fiber activity. Therefore, the model showed that, using a basic set of ionic channels, UBCs generate a rich repertoire of bursts, which could effectively implement tunable delay-lines in the local microcircuit.
Prediction of mechanical properties of a warm compacted molybdenum prealloy using artificial neural network and adaptive neuro-fuzzy models

International Nuclear Information System (INIS)

Zare, Mansour; Vahdati Khaki, Jalil

2012-01-01

Highlights: ► ANNs and ANFIS fairly predicted UTS and YS of warm compacted molybdenum prealloy. ► Effects of composition, temperature, compaction pressure on output were studied. ► ANFIS model was in better agreement with experimental data from published article. ► Sintering temperature had the most significant effect on UTS and YS. -- Abstract: Predictive models using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were successfully developed to predict yield strength and ultimate tensile strength of warm compacted 0.85 wt.% molybdenum prealloy samples. To construct these models, 48 different experimental data were gathered from the literature. A portion of the data set was randomly chosen to train both ANN with back propagation (BP) learning algorithm and ANFIS model with Gaussian membership function and the rest was implemented to verify the performance of the trained network against the unseen data. The generalization capability of the networks was also evaluated by applying new input data within the domain covered by the training pattern. To compare the obtained results, coefficient of determination (R 2 ), root mean squared error (RMSE) and average absolute error (AAE) indexes were chosen and calculated for both of the models. The results showed that artificial neural network and adaptive neuro-fuzzy system were both potentially strong for prediction of the mechanical properties of warm compacted 0.85 wt.% molybdenum prealloy; however, the proposed ANFIS showed better performance than the ANN model. Also, the ANFIS model was subjected to a sensitivity analysis to find the significant inputs affecting mechanical properties of the samples.
A Mechanism-Based Model for the Prediction of the Metabolic Sites of Steroids Mediated by Cytochrome P450 3A4

Directory of Open Access Journals (Sweden)

Zi-Ru Dai

2015-06-01

Full Text Available Early prediction of xenobiotic metabolism is essential for drug discovery and development. As the most important human drug-metabolizing enzyme, cytochrome P450 3A4 has a large active cavity and metabolizes a broad spectrum of substrates. The poor substrate specificity of CYP3A4 makes it a huge challenge to predict the metabolic site(s on its substrates. This study aimed to develop a mechanism-based prediction model based on two key parameters, including the binding conformation and the reaction activity of ligands, which could reveal the process of real metabolic reaction(s and the site(s of modification. The newly established model was applied to predict the metabolic site(s of steroids; a class of CYP3A4-preferred substrates. 38 steroids and 12 non-steroids were randomly divided into training and test sets. Two major metabolic reactions, including aliphatic hydroxylation and N-dealkylation, were involved in this study. At least one of the top three predicted metabolic sites was validated by the experimental data. The overall accuracy for the training and test were 82.14% and 86.36%, respectively. In summary, a mechanism-based prediction model was established for the first time, which could be used to predict the metabolic site(s of CYP3A4 on steroids with high predictive accuracy.
Prediction of mechanical properties of Al alloys with change of cooling rate

Directory of Open Access Journals (Sweden)

Quan-Zhi Dong

2012-11-01

Full Text Available The solidification process significantly affects the mechanical properties and there are lots of factors that affect the solidification process. Much progress has been made in the research on the effect of solidification on mechanical properties. Among them, the PF (Phase Field model and CA (Cellular Automata model are widely used as simulation methods which can predict nucleation and its growth, and the size and morphology of the grains during solidification. Although they can give accurate calculation results, it needs too much computational memory and calculation time. So it is difficult to apply the simulation to the real production process. In this study, a more practical simulation approach which can predict the mechanical properties of real aluminum alloys is proposed, by identifying through experiment the relationship between cooling rate and SDAS (Secondary Dendrite Arm Spacing and mechanical properties. The experimentally measured values and the values predicted by simulation have relatively small differences and the mechanical properties of a variety of Al alloys are expected to be predicted before casting through use of the simulation.
Draft forces prediction model for standard single tines by using principles of soil mechanics and soil profile evaluation

Directory of Open Access Journals (Sweden)

Amer Khalid Ahmed Al-Neama

2017-06-01

Full Text Available This paper explains a model to predict the draft force acting on varying standard single tines by using principles of soil mechanics and soil profile evaluation. Draft force (Fd measurements were made with four standard single tines comprising Heavy Duty, Double Heart, Double Heart with Wings and Duck Foot. Tine widths were 6.5, 13.5, 45 and 40 cm, respectively. The test was conducted in a soil bin with sandy loam soil. The effects of forward speeds and working depths on draft forces were investigated under controlled lab conditions. Results were evaluated based on a prediction model. A good correlation between measured and predicted Fd values for all tines with an average absolute variation less than 15 % was found.
A molecular-mechanics based finite element model for strength prediction of single wall carbon nanotubes

International Nuclear Information System (INIS)

Meo, M.; Rossi, M.

2007-01-01

The aim of this work was to develop a finite element model based on molecular mechanics to predict the ultimate strength and strain of single wallet carbon nanotubes (SWCNT). The interactions between atoms was modelled by combining the use of non-linear elastic and torsional elastic spring. In particular, with this approach, it was tried to combine the molecular mechanics approach with finite element method without providing any not-physical data on the interactions between the carbon atoms, i.e. the CC-bond inertia moment or Young's modulus definition. Mechanical properties as Young's modulus, ultimate strength and strain for several CNTs were calculated. Further, a stress-strain curve for large deformation (up to 70%) is reported for a nanotube Zig-Zag (9,0). The results showed that good agreement with the experimental and numerical results of several authors was obtained. A comparison of the mechanical properties of nanotubes with same diameter and different chirality was carried out. Finally, the influence of the presence of defects on the strength and strain of a SWNT was also evaluated. In particular, the stress-strain curve a nanotube with one-vacancy defect was evaluated and compared with the curve of a pristine one, showing a reduction of the ultimate strength and strain for the defected nanotube. The FE model proposed demonstrate to be a reliable tool to simulate mechanical behaviour of carbon nanotubes both in the linear elastic field and the non-linear elastic field
Externally predictive quantitative modeling of supercooled liquid vapor pressure of polychlorinated-naphthalenes through electron-correlation based quantum-mechanical descriptors.

Science.gov (United States)

Vikas; Chayawan

2014-01-01

For predicting physico-chemical properties related to environmental fate of molecules, quantitative structure-property relationships (QSPRs) are valuable tools in environmental chemistry. For developing a QSPR, molecular descriptors computed through quantum-mechanical methods are generally employed. The accuracy of a quantum-mechanical method, however, rests on the amount of electron-correlation estimated by the method. In this work, single-descriptor QSPRs for supercooled liquid vapor pressure of chloronaphthalenes and polychlorinated-naphthalenes are developed using molecular descriptors based on the electron-correlation contribution of the quantum-mechanical descriptor. The quantum-mechanical descriptors for which the electron-correlation contribution is analyzed include total-energy, mean polarizability, dipole moment, frontier orbital (HOMO/LUMO) energy, and density-functional theory (DFT) based descriptors, namely, absolute electronegativity, chemical hardness, and electrophilicity index. A total of 40 single-descriptor QSPRs were developed using molecular descriptors computed with advanced semi-empirical (SE) methods, namely, RM1, PM7, and ab intio methods, namely, Hartree-Fock and DFT. The developed QSPRs are validated using state-of-the-art external validation procedures employing an external prediction set. From the comparison of external predictivity of the models, it is observed that the single-descriptor QSPRs developed using total energy and correlation energy are found to be far more robust and predictive than those developed using commonly employed descriptors such as HOMO/LUMO energy and dipole moment. The work proposes that if real external predictivity of a QSPR model is desired to be explored, particularly, in terms of intra-molecular interactions, correlation-energy serves as a more appropriate descriptor than the polarizability. However, for developing QSPRs, computationally inexpensive advanced SE methods such as PM7 can be more reliable than
Validating predictions made by a thermo-mechanical model of melt segregation in sub-volcanic systems

Science.gov (United States)

Roele, Katarina; Jackson, Matthew; Morgan, Joanna

2014-05-01

A quantitative understanding of the spatial and temporal evolution of melt distribution in the crust is crucial in providing insights into the development of sub-volcanic crustal stratigraphy and composition. This work aims to relate numerical models that describe the base of volcanic systems with geophysical observations. Recent modelling has shown that the repetitive emplacement of mantle-derived basaltic sills, at the base of the lower crust, acts as a heat source for anatectic melt generation, buoyancy-driven melt segregation and mobilisation. These processes form the lowermost architecture of complex sub-volcanic networks as upward migrating melt produces high melt fraction layers. These 'porosity waves' are separated by zones with high compaction rates and have distinctive polybaric chemical signatures that suggest mixed crust and mantle origins. A thermo-mechanical model produced by Solano et al in 2012 has been used to predict the temperatures and melt fractions of successive high porosity layers within the crust. This model was used as it accounts for the dynamic evolution of melt during segregation and migration through the crust; a significant process that has been neglected in previous models. The results were used to input starting compositions for each of the layers into the rhyolite-MELTS thermodynamic simulation. MELTS then determined the approximate bulk composition of the layers once they had cooled and solidified. The mean seismic wave velocities of the polymineralic layers were then calculated using the relevant Voight-Reuss-Hill mixture rules, whilst accounting for the pressure and temperature dependence of seismic wave velocity. The predicted results were then compared with real examples of reflectivity for areas including the UK, where lower crustal layering is observed. A comparison between the impedance contrasts at compositional boundaries is presented as it confirms the extent to which modelling is able to make predictions that are
A Continuum Damage Mechanics Model to Predict Kink-Band Propagation Using Deformation Gradient Tensor Decomposition

Science.gov (United States)

Bergan, Andrew C.; Leone, Frank A., Jr.

2016-01-01

A new model is proposed that represents the kinematics of kink-band formation and propagation within the framework of a mesoscale continuum damage mechanics (CDM) model. The model uses the recently proposed deformation gradient decomposition approach to represent a kink band as a displacement jump via a cohesive interface that is embedded in an elastic bulk material. The model is capable of representing the combination of matrix failure in the frame of a misaligned fiber and instability due to shear nonlinearity. In contrast to conventional linear or bilinear strain softening laws used in most mesoscale CDM models for longitudinal compression, the constitutive response of the proposed model includes features predicted by detailed micromechanical models. These features include: 1) the rotational kinematics of the kink band, 2) an instability when the peak load is reached, and 3) a nonzero plateau stress under large strains.
Life prediction methodology for thermal-mechanical fatigue and elevated temperature creep design

Science.gov (United States)

Annigeri, Ravindra

Nickel-based superalloys are used for hot section components of gas turbine engines. Life prediction techniques are necessary to assess service damage in superalloy components resulting from thermal-mechanical fatigue (TMF) and elevated temperature creep. A new TMF life model based on continuum damage mechanics has been developed and applied to IN 738 LC substrate material with and without coating. The model also characterizes TMF failure in bulk NiCoCrAlY overlay and NiAl aluminide coatings. The inputs to the TMF life model are mechanical strain range, hold time, peak cycle temperatures and maximum stress measured from the stabilized or mid-life hysteresis loops. A viscoplastic model is used to predict the stress-strain hysteresis loops. A flow rule used in the viscoplastic model characterizes the inelastic strain rate as a function of the applied stress and a set of three internal stress variables known as back stress, drag stress and limit stress. Test results show that the viscoplastic model can reasonably predict time-dependent stress-strain response of the coated material and stress relaxation during hold times. In addition to the TMF life prediction methodology, a model has been developed to characterize the uniaxial and multiaxial creep behavior. An effective stress defined as the applied stress minus the back stress is used to characterize the creep recovery and primary creep behavior. The back stress has terms representing strain hardening, dynamic recovery and thermal recovery. Whenever the back stress is greater than the applied stress, the model predicts a negative creep rate observed during multiple stress and multiple temperature cyclic tests. The model also predicted the rupture time and the remaining life that are important for life assessment. The model has been applied to IN 738 LC, Mar-M247, bulk NiCoCrAlY overlay coating and 316 austenitic stainless steel. The proposed model predicts creep response with a reasonable accuracy for wide range of

Predicted phototoxicities of carbon nano-material by quantum mechanical calculations

Science.gov (United States)

The purpose of this research is to develop a predictive model for the phototoxicity potential of carbon nanomaterials (fullerenols and single-walled carbon nanotubes). This model is based on the quantum mechanical (ab initio) calculations on these carbon-based materials and compa...
Prediction of mechanical properties of composites of HDPE/HA/EAA.

Science.gov (United States)

Albano, C; Perera, R; Cataño, L; Karam, A; González, G

2011-04-01

In this investigation, the behavior of the mechanical properties of composites of high-density polyethylene/hydroxyapatite (HDPE/HA) with and without ethylene-acrylic acid copolymer (EAA) as possible compatibilizer, was studied. Different mathematical models were used to predict their Young's modulus, tensile strength and elongation at break. A comparison with the experimental results shows that the theoretical models of Guth and Kerner modified can be used to predict the Young's modulus. On the other hand, the values obtained by the Verbeek model do not show a good agreement with the experimental data, since different factors that influence the mechanical properties are considered in this model such as: aspect ratio of the reinforcement, interfacial adhesion, porosity and binder content. TEM analysis confirms the discrepancies obtained between the experimental Young's modulus values and those predicted by the Verbeek model. The values of "P", "a" and "σ(A)" suggest that an interaction among the carboxylic groups of the copolymer and the hydroxyl groups of hydroxyapatite might be present. In composites with 20 and 30 wt% of filler, this interaction does not improve the Young's modulus values, since the deviations of the Verbeek model are significant. Copyright © 2010 Elsevier Ltd. All rights reserved.
A mechanical-electrical finite element method model for predicting contact resistance between bipolar plate and gas diffusion layer in PEM fuel cells

Energy Technology Data Exchange (ETDEWEB)

Lai, Xinmin; Liu, Dong' an; Peng, Linfa [State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240 (China); Ni, Jun [Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109-2125 (United States)

2008-07-15

Contact resistance between the bipolar plate (BPP) and the gas diffusion layer (GDL) plays a significant role on the power loss in a proton exchange membrane (PEM) fuel cell. There are two types of contact behavior at the interface of the BPP and GDL, which are the mechanical one and the electrical one. Furthermore, the electrical contact behavior is dependent on the mechanical one. Thus, prediction of the contact resistance is a coupled mechanical-electrical problem. The current FEM models for contact resistance estimation can only simulate the mechanical contact behavior and moreover they are based on the assumption that the contact surface is equipotential, which is not the case in a real BPP/GDL assembly due to the round corner and margin of the BPP. In this study, a mechanical-electrical FEM model was developed to predict the contact resistance between the BPP and GDL based on the experimental interfacial contact resistivity. At first, the interfacial contact resistivity was obtained by experimentally measuring the contact resistance between the GDL and a flat graphite plate of the same material and processing conditions as the BPP. Then, with the interfacial contact resistivity, the mechanical and electrical contact behaviors were defined and the potential distribution of the BPP/GDL assembly was analyzed using the mechanical-electrical FEM model. At last, the contact resistance was calculated according to the potential drop and the current of the contact surface. The numerical results were validated by comparing with those of the model reported previously. The influence of the round corner of the BPP on the contact resistance was also studied and it is found that there exists an optimal round corner that can minimize the contact resistance. This model is beneficial in understanding the mechanical and electrical contact behaviors between the BPP and GDL, and can be used to predict the contact resistance in a new BPP/GDL assembly. (author)
Failure analysis and seal life prediction for contacting mechanical seals

Science.gov (United States)

Sun, J. J.; He, X. Y.; Wei, L.; Feng, X.

2008-11-01

Fault tree analysis method was applied to quantitatively investigate the causes of the leakage failure of mechanical seals. It is pointed out that the change of the surface topography is the main reasons causing the leakage of mechanical seals under the condition of constant preloads. Based on the fractal geometry theory, the relationship between the surface topography and working time were investigated by experiments, and the effects of unit load acting on seal face on leakage path in a mechanical seal were analyzed. The model of predicting seal life of mechanical seals was established on the basis of the relationship between the surface topography and working time and allowable leakage. The seal life of 108 mechanical seal operating at the system of diesel fuel storage and transportation was predicted and the problem of the condition monitoring for the long-period operation of mechanical seal was discussed by this method. The research results indicate that the method of predicting seal life of mechanical seals is feasible, and also is foundation to make scheduled maintenance time and to achieve safe-reliability and low-cost operation for industrial devices.
Predictive Models of Li-ion Battery Lifetime (Presentation)

Energy Technology Data Exchange (ETDEWEB)

Smith, K.; Wood, E.; Santhanagopalan, S.; Kim, G.; Shi, Y.; Pesaran, A.

2014-09-01

Predictive models of Li-ion battery reliability must consider a multiplicity of electrochemical, thermal and mechanical degradation modes experienced by batteries in application environments. Complicating matters, Li-ion batteries can experience several path dependent degradation trajectories dependent on storage and cycling history of the application environment. Rates of degradation are controlled by factors such as temperature history, electrochemical operating window, and charge/discharge rate. Lacking accurate models and tests, lifetime uncertainty must be absorbed by overdesign and warranty costs. Degradation models are needed that predict lifetime more accurately and with less test data. Models should also provide engineering feedback for next generation battery designs. This presentation reviews both multi-dimensional physical models and simpler, lumped surrogate models of battery electrochemical and mechanical degradation. Models are compared with cell- and pack-level aging data from commercial Li-ion chemistries. The analysis elucidates the relative importance of electrochemical and mechanical stress-induced degradation mechanisms in real-world operating environments. Opportunities for extending the lifetime of commercial battery systems are explored.
Model-free and model-based reward prediction errors in EEG.

Science.gov (United States)

Sambrook, Thomas D; Hardwick, Ben; Wills, Andy J; Goslin, Jeremy

2018-05-24

Learning theorists posit two reinforcement learning systems: model-free and model-based. Model-based learning incorporates knowledge about structure and contingencies in the world to assign candidate actions with an expected value. Model-free learning is ignorant of the world's structure; instead, actions hold a value based on prior reinforcement, with this value updated by expectancy violation in the form of a reward prediction error. Because they use such different learning mechanisms, it has been previously assumed that model-based and model-free learning are computationally dissociated in the brain. However, recent fMRI evidence suggests that the brain may compute reward prediction errors to both model-free and model-based estimates of value, signalling the possibility that these systems interact. Because of its poor temporal resolution, fMRI risks confounding reward prediction errors with other feedback-related neural activity. In the present study, EEG was used to show the presence of both model-based and model-free reward prediction errors and their place in a temporal sequence of events including state prediction errors and action value updates. This demonstration of model-based prediction errors questions a long-held assumption that model-free and model-based learning are dissociated in the brain. Copyright © 2018 Elsevier Inc. All rights reserved.
Comparison of pause predictions of two sequence-dependent transcription models

International Nuclear Information System (INIS)

Bai, Lu; Wang, Michelle D

2010-01-01

Two recent theoretical models, Bai et al (2004, 2007) and Tadigotla et al (2006), formulated thermodynamic explanations of sequence-dependent transcription pausing by RNA polymerase (RNAP). The two models differ in some basic assumptions and therefore make different yet overlapping predictions for pause locations, and different predictions on pause kinetics and mechanisms. Here we present a comprehensive comparison of the two models. We show that while they have comparable predictive power of pause locations at low NTP concentrations, the Bai et al model is more accurate than Tadigotla et al at higher NTP concentrations. The pausing kinetics predicted by Bai et al is also consistent with time-course transcription reactions, while Tadigotla et al is unsuited for this type of kinetic prediction. More importantly, the two models in general predict different pausing mechanisms even for the same pausing sites, and the Bai et al model provides an explanation more consistent with recent single molecule observations
Brain mechanisms in religion and spirituality: An integrative predictive processing framework.

Science.gov (United States)

van Elk, Michiel; Aleman, André

2017-02-01

We present the theory of predictive processing as a unifying framework to account for the neurocognitive basis of religion and spirituality. Our model is substantiated by discussing four different brain mechanisms that play a key role in religion and spirituality: temporal brain areas are associated with religious visions and ecstatic experiences; multisensory brain areas and the default mode network are involved in self-transcendent experiences; the Theory of Mind-network is associated with prayer experiences and over attribution of intentionality; top-down mechanisms instantiated in the anterior cingulate cortex and the medial prefrontal cortex could be involved in acquiring and maintaining intuitive supernatural beliefs. We compare the predictive processing model with two-systems accounts of religion and spirituality, by highlighting the central role of prediction error monitoring. We conclude by presenting novel predictions for future research and by discussing the philosophical and theological implications of neuroscientific research on religion and spirituality. Copyright © 2016 Elsevier Ltd. All rights reserved.
A 3D coupled hydro-mechanical granular model for the prediction of hot tearing formation

International Nuclear Information System (INIS)

Sistaninia, M; Drezet, J-M; Rappaz, M; Phillion, A B

2012-01-01

A new 3D coupled hydro-mechanical granular model that simulates hot tearing formation in metallic alloys is presented. The hydro-mechanical model consists of four separate 3D modules. (I) The Solidification Module (SM) is used for generating the initial solid-liquid geometry. Based on a Voronoi tessellation of randomly distributed nucleation centers, this module computes solidification within each polyhedron using a finite element based solute diffusion calculation for each element within the tessellation. (II) The Fluid Flow Module (FFM) calculates the solidification shrinkage and deformation-induced pressure drop within the intergranular liquid. (III) The Semi-solid Deformation Module (SDM) is used to simulate deformation of the granular structure via a combined finite element / discrete element method. In this module, deformation of the solid grains is modeled using an elasto-viscoplastic constitutive law. (IV) The Failure Module (FM) is used to simulate crack initiation and propagation with the fracture criterion estimated from the overpressure required to overcome the capillary forces at the liquid-gas interface. The FFM, SDM, and FM are coupled processes since solid deformation, intergranular flow, and crack initiation are deeply linked together. The granular model predictions have been validated against bulk data measured experimentally and calculated with averaging techniques.
Properties, Mechanisms and Predictability of Eddies in the Red Sea

KAUST Repository

Zhan, Peng

2018-01-01

of Red Sea eddies, including their temporal and spatial properties, their energy budget, the mechanisms of their evolution, and their predictability. Remote sensing data, in-situ observations, the oceanic general circulation model, and data assimilation
The comparison of two continuum damage mechanics-based material models for formability prediction of AA6082 under hot stamping conditions

Science.gov (United States)

Shao, Z.; Li, N.; Lin, J.

2017-09-01

The hot stamping and cold die quenching process has experienced tremendous development in order to obtain shapes of structural components with great complexity in automotive applications. Prediction of the formability of a metal sheet is significant for practical applications of forming components in the automotive industry. Since microstructural evolution in an alloy at elevated temperature has a large effect on formability, continuum damage mechanics (CDM)-based material models can be used to characterise the behaviour of metals when a forming process is conducted at elevated temperatures. In this paper, two sets of unified multi-axial constitutive equations based on material’s stress states and strain states, respectively, were calibrated and used to effectively predict the thermo-mechanical response and forming limits of alloys under complex hot stamping conditions. In order to determine and calibrate the two material models, formability tests of AA6082 using a developed novel biaxial testing system were conducted at various temperatures and strain rates under hot stamping conditions. The determined unified constitutive equations from experimental data are presented in this paper. It is found that both of the stress-state based and strain-state based material models can predict the formability of AA6082 under hot stamping conditions.
A Prediction Mechanism of Energy Consumption in Residential Buildings Using Hidden Markov Model

Directory of Open Access Journals (Sweden)

Israr Ullah

2018-02-01

Full Text Available Internet of Things (IoT is considered as one of the future disruptive technologies, which has the potential to bring positive change in human lifestyle and uplift living standards. Many IoT-based applications have been designed in various fields, e.g., security, health, education, manufacturing, transportation, etc. IoT has transformed conventional homes into Smart homes. By attaching small IoT devices to various appliances, we cannot only monitor but also control indoor environment as per user demand. Intelligent IoT devices can also be used for optimal energy utilization by operating the associated equipment only when it is needed. In this paper, we have proposed a Hidden Markov Model based algorithm to predict energy consumption in Korean residential buildings using data collected through smart meters. We have used energy consumption data collected from four multi-storied buildings located in Seoul, South Korea for model validation and results analysis. Proposed model prediction results are compared with three well-known prediction algorithms i.e., Support Vector Machine (SVM, Artificial Neural Network (ANN and Classification and Regression Trees (CART. Comparative analysis shows that our proposed model achieves 2.96 % better than ANN results in terms of root mean square error metric, 6.09 % better than SVM and 9.03 % better than CART results. To further establish and validate prediction results of our proposed model, we have performed temporal granularity analysis. For this purpose, we have evaluated our proposed model for hourly, daily and weekly data aggregation. Prediction accuracy in terms of root mean square error metric for hourly, daily and weekly data is 2.62, 1.54 and 0.46, respectively. This shows that our model prediction accuracy improves for coarse grain data. Higher prediction accuracy gives us confidence to further explore its application in building control systems for achieving better energy efficiency.
Thermo-mechanical fatigue behaviour and life prediction of C-1023 ...

African Journals Online (AJOL)

user

Mechanical strain-cycles to failure curves for 890-1055 ºC temperature range. ... could be ascribed to creep strain accumulation at high temperatures coinciding ... It is common in engineering applications to rely on life prediction models based ...
Application of artificial intelligence methods for prediction of steel mechanical properties

Directory of Open Access Journals (Sweden)

Z. Jančíková

2008-10-01

Full Text Available The target of the contribution is to outline possibilities of applying artificial neural networks for the prediction of mechanical steel properties after heat treatment and to judge their perspective use in this field. The achieved models enable the prediction of final mechanical material properties on the basis of decisive parameters influencing these properties. By applying artificial intelligence methods in combination with mathematic-physical analysis methods it will be possible to create facilities for designing a system of the continuous rationalization of existing and also newly developing industrial technologies.
Leak-off mechanism and pressure prediction for shallow sediments in deepwater drilling

Science.gov (United States)

Tan, Qiang; Deng, Jingen; Sun, Jin; Liu, Wei; Yu, Baohua

2018-02-01

Deepwater sediments are prone to loss circulation in drilling due to a low overburden gradient. How to predict the magnitude of leak-off pressure more accurately is an important issue in the protection of drilling safety and the reduction of drilling cost in deep water. Starting from the mechanical properties of a shallow formation and based on the basic theory of rock-soil mechanics, the stress distribution around a borehole was analyzed. It was found that the rock or soil on a borehole is in the plastic yield state before the effective tensile stress is generated, and the effective tangential and vertical stresses increase as the drilling fluid density increases; thus, tensile failure will not occur on the borehole wall. Based on the results of stress calculation, two mechanisms and leak-off pressure prediction models for shallow sediments in deepwater drilling were put forward, and the calculated values of these models were compared with the measured value of shallow leak-off pressure in actual drilling. The results show that the MHPS (minimum horizontal principle stress) model and the FIF (fracturing in formation) model can predict the lower and upper limits of leak-off pressure. The PLC (permeable lost circulation) model can comprehensively analyze the factors influencing permeable leakage and provide a theoretical basis for leak-off prevention and plugging in deepwater drilling.
Autoregressive Prediction with Rolling Mechanism for Time Series Forecasting with Small Sample Size

Directory of Open Access Journals (Sweden)

Zhihua Wang

2014-01-01

Full Text Available Reasonable prediction makes significant practical sense to stochastic and unstable time series analysis with small or limited sample size. Motivated by the rolling idea in grey theory and the practical relevance of very short-term forecasting or 1-step-ahead prediction, a novel autoregressive (AR prediction approach with rolling mechanism is proposed. In the modeling procedure, a new developed AR equation, which can be used to model nonstationary time series, is constructed in each prediction step. Meanwhile, the data window, for the next step ahead forecasting, rolls on by adding the most recent derived prediction result while deleting the first value of the former used sample data set. This rolling mechanism is an efficient technique for its advantages of improved forecasting accuracy, applicability in the case of limited and unstable data situations, and requirement of little computational effort. The general performance, influence of sample size, nonlinearity dynamic mechanism, and significance of the observed trends, as well as innovation variance, are illustrated and verified with Monte Carlo simulations. The proposed methodology is then applied to several practical data sets, including multiple building settlement sequences and two economic series.
Prediction of mechanical properties in friction stir welds of pure copper

International Nuclear Information System (INIS)

Heidarzadeh, A.; Saeid, T.

2013-01-01

Highlights: • Range of parameters for defect-free friction stir welded pure copper was reached. • Models were developed for predicting UTS, TE and hardness of pure copper joints. • Analysis of variance was used to validate the developed models. • Effect of welding parameters on mechanical behavior of welded joints was explored. • The microstructure and fracture surface of welded joints were investigated. - Abstract: This research was carried out to predict the mechanical properties of friction stir welded pure copper joints. Response surface methodology based on a central composite rotatable design with three parameters, five levels, and 20 runs, was used to conduct the experiments and to develop the mathematical regression model by using of Design-Expert software. The three welding parameters considered were rotational speed, welding speed, and axial force. Analysis of variance was applied to validate the predicted models. Microstructural characterization and fractography of joints were examined using optical and scanning electron microscopes. Also, the effects of the welding parameters on mechanical properties of friction stir welded joints were analyzed in detail. The results showed that the developed models were reasonably accurate. The increase in welding parameters resulted in increasing of tensile strength of the joints up to a maximum value. Elongation percent of the joints increased with increase of rotational speed and axial force, but decreased by increasing of welding speed, continuously. In addition, hardness of the joints decreased with increase of rotational speed and axial force, but increased by increasing of welding speed. The joints welded at higher heat input conditions revealed more ductility fracture mode
From basic physics to mechanisms of toxicity: the ``liquid drop'' approach applied to develop predictive classification models for toxicity of metal oxide nanoparticles

Science.gov (United States)

Sizochenko, Natalia; Rasulev, Bakhtiyor; Gajewicz, Agnieszka; Kuz'min, Victor; Puzyn, Tomasz; Leszczynski, Jerzy

2014-10-01

Many metal oxide nanoparticles are able to cause persistent stress to live organisms, including humans, when discharged to the environment. To understand the mechanism of metal oxide nanoparticles' toxicity and reduce the number of experiments, the development of predictive toxicity models is important. In this study, performed on a series of nanoparticles, the comparative quantitative-structure activity relationship (nano-QSAR) analyses of their toxicity towards E. coli and HaCaT cells were established. A new approach for representation of nanoparticles' structure is presented. For description of the supramolecular structure of nanoparticles the ``liquid drop'' model was applied. It is expected that a novel, proposed approach could be of general use for predictions related to nanomaterials. In addition, in our study fragmental simplex descriptors and several ligand-metal binding characteristics were calculated. The developed nano-QSAR models were validated and reliably predict the toxicity of all studied metal oxide nanoparticles. Based on the comparative analysis of contributed properties in both models the LDM-based descriptors were revealed to have an almost similar level of contribution to toxicity in both cases, while other parameters (van der Waals interactions, electronegativity and metal-ligand binding characteristics) have unequal contribution levels. In addition, the models developed here suggest different mechanisms of nanotoxicity for these two types of cells.Many metal oxide nanoparticles are able to cause persistent stress to live organisms, including humans, when discharged to the environment. To understand the mechanism of metal oxide nanoparticles' toxicity and reduce the number of experiments, the development of predictive toxicity models is important. In this study, performed on a series of nanoparticles, the comparative quantitative-structure activity relationship (nano-QSAR) analyses of their toxicity towards E. coli and HaCaT cells were
Microstructure distribution and mechanical properties prediction of boron alloy during hot forming using FE simulation

International Nuclear Information System (INIS)

Cui Junjia; Lei Chengxi; Xing Zhongwen; Li Chunfeng

2012-01-01

Highlights: ► We model microstructural evolution during hot forming using a metallo-thermo-mechanical model. ► The effect of water-cooled on temperature distribution of blank and tools was investigated. ► The effect of process parameters on microstructure and mechanical properties were investigated. ► FE results were compared to experimental results and the errors of mechanical properties were in a reasonable scope. - Abstract: As a theoretical tool predicting microstructural evolution of boron alloy, the finite element (FE) method has received considerable attention in recent years. In this work, we focus on the boron alloy under non-isothermal hot forming conditions and establish a fully coupled metallo-thermo-mechanical model taking account of cooling and oxide. Based on the proposed model, we investigate the phase transformation and predict the hardness during the hot forming process via FE simulation. In addition, according to the hardness, the tensile strength during non-isothermal forming is predicted. Supporting the feasibility of the proposed model is the experiments where BR1500HS alloy is hot-worked at various conditions that derive a promising agreement of microstructures, hardness, and tensile strength to the simulation data.
Mental models accurately predict emotion transitions.

Science.gov (United States)

Thornton, Mark A; Tamir, Diana I

2017-06-06

Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.

Mental models accurately predict emotion transitions

Science.gov (United States)

Thornton, Mark A.; Tamir, Diana I.

2017-01-01

Successful social interactions depend on people’s ability to predict others’ future actions and emotions. People possess many mechanisms for perceiving others’ current emotional states, but how might they use this information to predict others’ future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others’ emotional dynamics. People could then use these mental models of emotion transitions to predict others’ future emotions from currently observable emotions. To test this hypothesis, studies 1–3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants’ ratings of emotion transitions predicted others’ experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation—valence, social impact, rationality, and human mind—inform participants’ mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants’ accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone. PMID:28533373
Modelling Cochlear Mechanics

Directory of Open Access Journals (Sweden)

Guangjian Ni

2014-01-01

Full Text Available The cochlea plays a crucial role in mammal hearing. The basic function of the cochlea is to map sounds of different frequencies onto corresponding characteristic positions on the basilar membrane (BM. Sounds enter the fluid-filled cochlea and cause deflection of the BM due to pressure differences between the cochlear fluid chambers. These deflections travel along the cochlea, increasing in amplitude, until a frequency-dependent characteristic position and then decay away rapidly. The hair cells can detect these deflections and encode them as neural signals. Modelling the mechanics of the cochlea is of help in interpreting experimental observations and also can provide predictions of the results of experiments that cannot currently be performed due to technical limitations. This paper focuses on reviewing the numerical modelling of the mechanical and electrical processes in the cochlea, which include fluid coupling, micromechanics, the cochlear amplifier, nonlinearity, and electrical coupling.
ONKALO rock mechanics model (RMM). Version 2.3

Energy Technology Data Exchange (ETDEWEB)

Haekkinen, T.; Merjama, S.; Moenkkoenen, H. [WSP Finland, Helsinki (Finland)

2014-07-15

The Rock Mechanics Model of the ONKALO rock volume includes the most important rock mechanics features and parameters at the Olkiluoto site. The main objective of the model is to be a tool to predict rock properties, rock quality and hence provide an estimate for the rock stability of the potential repository at Olkiluoto. The model includes a database of rock mechanics raw data and a block model in which the rock mechanics parameters are estimated through block volumes based on spatial rock mechanics raw data. In this version 2.3, special emphasis was placed on refining the estimation of the block model. The model was divided into rock mechanics domains which were used as constraints during the block model estimation. During the modelling process, a display profile and toolbar were developed for the GEOVIA Surpac software to improve visualisation and access to the rock mechanics data for the Olkiluoto area. (orig.)
Does predictability matter? Effects of cue predictability on neurocognitive mechanisms underlying prospective memory.

Science.gov (United States)

Cona, Giorgia; Arcara, Giorgio; Tarantino, Vincenza; Bisiacchi, Patrizia S

2015-01-01

Prospective memory (PM) represents the ability to successfully realize intentions when the appropriate moment or cue occurs. In this study, we used event-related potentials (ERPs) to explore the impact of cue predictability on the cognitive and neural mechanisms supporting PM. Participants performed an ongoing task and, simultaneously, had to remember to execute a pre-specified action when they encountered the PM cues. The occurrence of the PM cues was predictable (being signaled by a warning cue) for some participants and was completely unpredictable for others. In the predictable cue condition, the behavioral and ERP correlates of strategic monitoring were observed mainly in the ongoing trials wherein the PM cue was expected. In the unpredictable cue condition they were instead shown throughout the whole PM block. This pattern of results suggests that, in the predictable cue condition, participants engaged monitoring only when subjected to a context wherein the PM cue was expected, and disengaged monitoring when the PM cue was not expected. Conversely, participants in the unpredictable cue condition distributed their resources for strategic monitoring in more continuous manner. The findings of this study support the most recent views-the "Dynamic Multiprocess Framework" and the "Attention to Delayed Intention" (AtoDI) model-confirming that strategic monitoring is a flexible mechanism that is recruited mainly when a PM cue is expected and that may interact with bottom-up spontaneous processes.
Prediction of vibration characteristics of a planar mechanism having imperfect joints using neural network

International Nuclear Information System (INIS)

Erkaya, Selcuk

2012-01-01

Clearance is inevitable in the joints of mechanisms due primarily to the design, manufacturing and assembly processes or a wear effect. Excessive value of joint clearance plays a crucial role and has a significant effect on the kinematic and dynamic performances of the mechanism. In this study, effects of joint clearances on bearing vibrations of mechanism are investigated. An experimental test rig is set up, and a planar slider-crank mechanism having two imperfect joints with radial clearance is used as a model mechanism. Three accelerometers are positioned at different points to measure the bearing vibrations during the mechanism motion. For the different running speeds and clearance sizes, this work provides a neural model to predict and estimate the bearing vibrations of the mechanical systems having imperfect joints. The results show that radial basis function (RBF) neural network has a superior performance for predicting and estimating the vibration characteristics of the mechanical system
Mathematical modeling and computational prediction of cancer drug resistance.

Science.gov (United States)

Sun, Xiaoqiang; Hu, Bin

2017-06-23

Diverse forms of resistance to anticancer drugs can lead to the failure of chemotherapy. Drug resistance is one of the most intractable issues for successfully treating cancer in current clinical practice. Effective clinical approaches that could counter drug resistance by restoring the sensitivity of tumors to the targeted agents are urgently needed. As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing. In this review, we first briefly summarize the current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms. Subsequently, we list several currently available databases and Web-based tools related to drug sensitivity and resistance. Then, we focus primarily on introducing some state-of-the-art computational methods used in drug resistance studies, including mechanism-based mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic-pharmacodynamic model, etc.) and data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.). Finally, we discuss several further questions and future directions for the use of
Mechanism and prediction of burnout

International Nuclear Information System (INIS)

Hewitt, G.F.

1977-01-01

The lecture begins by discussing the definitions of burnout and the various parametric effects as seen from the results for burnout measurements in uniformly heated round tubes. The correlations which are developed from these measurements and their applications to the case of non-uniform axial distribution of heat flux is then discussed in general terms as an illustration of the importance of knowing more about the nature and mechanism of the burnout. The next section of the lecture is concerned with summarizing broadly the various possible mechanisms in both the sub-cooled region and the quality region. It transpires that, for tubes of reasonable length, the normal first occurrence of burnout is in the annular flow regime. A discussion of burnout mechanisms in this regime then follows, with descriptions of the various experimental techniques evolved to study the mechanism. The final section of the lecture is concerned with prediction methods for burnout in annular flow and the application of these methods to prediction of burnout in round tubes, annuli and rod bundles, with a variety of fluids
Prediction of Mechanical Properties of LDPE-TPS Nanocomposites Using Adaptive Neuro-Fuzzy Inference System

Directory of Open Access Journals (Sweden)

Maryam Sabetzadeh

2012-12-01

Full Text Available The changes in the behaviour of mechanical properties of low densitypolyethylene-thermoplastic corn starch (LDPE-TPCS nanocompositeswere studied by an adaptive neuro-fuzzy interference system. LDPE-TPCScomposites containing different quantities of nanoclay (Cloisite®15A, 0.5-3wt. % were prepared by extrusion process. In practice, it is difficult to carry out several experiments to identify the relationship between the extrusion process parameters and mechanical properties of the nanocomposites. In this paper, an adaptive neuro-fuzzy inference system (ANFIS was used for non-linear mapping between the processingparameters and the mechanical properties of LDPE-TPCS nanocomposites. ANFIS model due to possessing inference ability of fuzzy systems and also the learning feature of neural networks, could be used as a multiple inputs-multiple outputs to predict mechanical properties (such as ultimate tensile strength, elongation-at-break, Young’s modulus and relative impact strength of the nanocomposites. The proposed ANFIS model utilizes temperature, torque and Cloisite®15A contents as input parameters to predict the desired mechanical properties. The results obtained in this work indicatedthat ANFIS is an effective and intelligent method for prediction of the mechanical properties of the LDPE-TPCS nanocomposites with a good accuracy. The statistical quality of the ANFIS model was significant due to its acceptable mean square error criterion and good correlation coefficient (values > 0.8 between the experimental and simulated outputs.
Using saturation water percentage data to predict mechanical composition of soils

International Nuclear Information System (INIS)

Mbagwu, J.S.C.; Okafor, D.O.

1995-04-01

One hundred and sixty-six soil samples representing eleven textural classes and having wide variations in organic matter (OM) contents and other physico-chemical properties were collected from different locations in southeastern Nigeria to study the relationship between mechanical composition and saturation water percentage (SP). The objective was to develop a prediction model for silt + clay (SC) and clay (C) contents of these soils using the SP values. The magnitude of the correlation coefficients (r) between SC or C and SP was dependent on the amount of organic matter (OM) present in the soils. For soils with ≤ 1.00% OM, the correlation (r) between SC and SP was 0.9659 (p ≤ 0.001) and that between C and SP was 0.9539 (p ≤ 0.001). For soils with ≥ 2.00% OM, the 'r' values were generally low, varying between 0.5320 and 0.2665 for SC and 0.6008 and 0.3000 for C. The best-fit regression models for predicting SC and C were developed with soils having ≤ 1.00% OM. An independent data set from 25 soil samples collected from other parts of the study area was used to test the predictive ability of the best-fit models. These models predicted SC and C accurately in soils having between 0.28 and 1.10% OM, but poorly in soils having between 1.31 and 3.91% OM. These results show that the use of saturation water percentage to predict the mechanical composition of soils is most reliable for soils with low (≤ 1.00%) OM contents. (author). 18 refs, 2 figs, 5 tabs
Sorption mechanisms and sorption models

International Nuclear Information System (INIS)

Fedoroff, M.; Lefevre, G.; Duc, M.; Neskovic, C.; Milonjic, S.

2004-01-01

Sorption at the solid-liquid interfaces play a major role in many phenomena and technologies: chemical separations, catalysis, biological processes, transport of toxic and radioactive species in surface and underground waters. The long term safety of radioactive waste repositories is based on artificial and natural barriers, intended to sorb radionuclides after the moment when the storage matrixes and containers will be corroded. Predictions on the efficiency of sorption for more than 10 6 years have to be done in order to demonstrate the safety of such depositories, what is a goal never encountered in the history of sciences and technology. For all these purposes, and, especially for the long term prediction, acquiring of sorption data constitutes only a first step of studies. Modeling based on a very good knowledge of sorption mechanisms is needed. In this review, we shall examine the main approaches and models used to quantify sorption processes, including results taken from the literature and from our own studies. We shall compare sorption models and examine their adequacy with sorption mechanisms. The cited references are only a few examples of the numerous articles published in that field. (orig.)
Mechanics model for actin-based motility.

Science.gov (United States)

Lin, Yuan

2009-02-01

We present here a mechanics model for the force generation by actin polymerization. The possible adhesions between the actin filaments and the load surface, as well as the nucleation and capping of filament tips, are included in this model on top of the well-known elastic Brownian ratchet formulation. A closed form solution is provided from which the force-velocity relationship, summarizing the mechanics of polymerization, can be drawn. Model predictions on the velocity of moving beads driven by actin polymerization are consistent with experiment observations. This model also seems capable of explaining the enhanced actin-based motility of Listeria monocytogenes and beads by the presence of Vasodilator-stimulated phosphoprotein, as observed in recent experiments.
Preclinical models used for immunogenicity prediction of therapeutic proteins.

Science.gov (United States)

Brinks, Vera; Weinbuch, Daniel; Baker, Matthew; Dean, Yann; Stas, Philippe; Kostense, Stefan; Rup, Bonita; Jiskoot, Wim

2013-07-01

All therapeutic proteins are potentially immunogenic. Antibodies formed against these drugs can decrease efficacy, leading to drastically increased therapeutic costs and in rare cases to serious and sometimes life threatening side-effects. Many efforts are therefore undertaken to develop therapeutic proteins with minimal immunogenicity. For this, immunogenicity prediction of candidate drugs during early drug development is essential. Several in silico, in vitro and in vivo models are used to predict immunogenicity of drug leads, to modify potentially immunogenic properties and to continue development of drug candidates with expected low immunogenicity. Despite the extensive use of these predictive models, their actual predictive value varies. Important reasons for this uncertainty are the limited/insufficient knowledge on the immune mechanisms underlying immunogenicity of therapeutic proteins, the fact that different predictive models explore different components of the immune system and the lack of an integrated clinical validation. In this review, we discuss the predictive models in use, summarize aspects of immunogenicity that these models predict and explore the merits and the limitations of each of the models.
Does predictability matter? Effects of cue predictability on neurocognitive mechanisms underlying Prospective Memory

Directory of Open Access Journals (Sweden)

Giorgia eCona

2015-04-01

Full Text Available Prospective memory (PM represents the ability to successfully realize intentions when the appropriate moment or cue occurs. In this study, we used event-related potentials (ERPs to explore the impact of cue predictability on the cognitive and neural mechanisms supporting PM. Participants performed an ongoing task and, simultaneously, had to remember to execute a pre-specified action when they encountered the PM cues. The occurrence of the PM cues was predictable (being signalled by a warning cue for some participants and was completely unpredictable for others. In the predictable cue condition, the behavioural and ERP correlates of strategic monitoring were observed mainly in the ongoing trials wherein the PM cue was expected. In the unpredictable cue condition they were instead shown throughout the whole PM block. This pattern of results suggests that, in the predictable cue condition, participants engaged monitoring only when subjected to a context wherein the PM cue was expected, and disengaged monitoring when the PM cue was not expected. Conversely, participants in the unpredictable cue condition distributed their resources for strategic monitoring in more continuous manner. The findings of this study support the most recent views – the ‘Dynamic Multiprocess Framework’ and the ‘Attention to Delayed Intention’ (AtoDI model – confirming that strategic monitoring is a flexible mechanism that is recruited mainly when a PM cue is expected and that may interact with bottom-up spontaneous processes.
Anisotropic Elastoplastic Damage Mechanics Method to Predict Fatigue Life of the Structure

Directory of Open Access Journals (Sweden)

Hualiang Wan

2016-01-01

Full Text Available New damage mechanics method is proposed to predict the low-cycle fatigue life of metallic structures under multiaxial loading. The microstructure mechanical model is proposed to simulate anisotropic elastoplastic damage evolution. As the micromodel depends on few material parameters, the present method is very concise and suitable for engineering application. The material parameters in damage evolution equation are determined by fatigue experimental data of standard specimens. By employing further development on the ANSYS platform, the anisotropic elastoplastic damage mechanics-finite element method is developed. The fatigue crack propagation life of satellite structure is predicted using the present method and the computational results comply with the experimental data very well.
Computational modeling to predict mechanical function of joints: application to the lower leg with simulation of two cadaver studies.

Science.gov (United States)

Liacouras, Peter C; Wayne, Jennifer S

2007-12-01

Computational models of musculoskeletal joints and limbs can provide useful information about joint mechanics. Validated models can be used as predictive devices for understanding joint function and serve as clinical tools for predicting the outcome of surgical procedures. A new computational modeling approach was developed for simulating joint kinematics that are dictated by bone/joint anatomy, ligamentous constraints, and applied loading. Three-dimensional computational models of the lower leg were created to illustrate the application of this new approach. Model development began with generating three-dimensional surfaces of each bone from CT images and then importing into the three-dimensional solid modeling software SOLIDWORKS and motion simulation package COSMOSMOTION. Through SOLIDWORKS and COSMOSMOTION, each bone surface file was filled to create a solid object and positioned necessary components added, and simulations executed. Three-dimensional contacts were added to inhibit intersection of the bones during motion. Ligaments were represented as linear springs. Model predictions were then validated by comparison to two different cadaver studies, syndesmotic injury and repair and ankle inversion following ligament transection. The syndesmotic injury model was able to predict tibial rotation, fibular rotation, and anterior/posterior displacement. In the inversion simulation, calcaneofibular ligament extension and angles of inversion compared well. Some experimental data proved harder to simulate accurately, due to certain software limitations and lack of complete experimental data. Other parameters that could not be easily obtained experimentally can be predicted and analyzed by the computational simulations. In the syndesmotic injury study, the force generated in the tibionavicular and calcaneofibular ligaments reduced with the insertion of the staple, indicating how this repair technique changes joint function. After transection of the calcaneofibular
Cancellation mechanism in the predictions of electric dipole moments

Science.gov (United States)

Bian, Ligong; Chen, Ning

2017-06-01

The interpretation of the baryon asymmetry of the Universe necessitates the C P violation beyond the Standard Model (SM). We present a general cancellation mechanism in the theoretical predictions of the electron electric dipole moments (EDM), quark chromo-EDMs, and Weinberg operators. A relative large C P violation in the Higgs sector is allowed by the current electron EDM constraint released by the ACME collaboration in 2013, and the recent 199Hg EDM experiment. The cancellation mechanism can be induced by the mass splitting of heavy Higgs bosons around ˜O (0.1 - 1 ) GeV , and the extent of the mass degeneracy determines the magnitude of the C P -violating phase. We explicate this point by investigating the C P -violating two-Higgs-doublet model and the minimal supersymmetric Standard Model. The cancellation mechanism is general when there are C P violation and mixing in the Higgs sector of new physics models. The C P -violating phases in this scenario can be excluded or detected by the projected 225Ra EDM experiments with precision reaching ˜10-28 e .cm , as well as the future colliders.
Model Predictive Control of a Wave Energy Converter

DEFF Research Database (Denmark)

Andersen, Palle; Pedersen, Tom Søndergård; Nielsen, Kirsten Mølgaard

2015-01-01

In this paper reactive control and Model Predictive Control (MPC) for a Wave Energy Converter (WEC) are compared. The analysis is based on a WEC from Wave Star A/S designed as a point absorber. The model predictive controller uses wave models based on the dominating sea states combined with a model...... connecting undisturbed wave sequences to sequences of torque. Losses in the conversion from mechanical to electrical power are taken into account in two ways. Conventional reactive controllers are tuned for each sea state with the assumption that the converter has the same efficiency back and forth. MPC...
Mechanical characterization of structurally porous biomaterials built via additive manufacturing: experiments, predictive models, and design maps for load-bearing bone replacement implants.

Science.gov (United States)

Melancon, D; Bagheri, Z S; Johnston, R B; Liu, L; Tanzer, M; Pasini, D

2017-11-01

Porous biomaterials can be additively manufactured with micro-architecture tailored to satisfy the stringent mechano-biological requirements imposed by bone replacement implants. In a previous investigation, we introduced structurally porous biomaterials, featuring strength five times stronger than commercially available porous materials, and confirmed their bone ingrowth capability in an in vivo canine model. While encouraging, the manufactured biomaterials showed geometric mismatches between their internal porous architecture and that of its as-designed counterpart, as well as discrepancies between predicted and tested mechanical properties, issues not fully elucidated. In this work, we propose a systematic approach integrating computed tomography, mechanical testing, and statistical analysis of geometric imperfections to generate statistical based numerical models of high-strength additively manufactured porous biomaterials. The method is used to develop morphology and mechanical maps that illustrate the role played by pore size, porosity, strut thickness, and topology on the relations governing their elastic modulus and compressive yield strength. Overall, there are mismatches between the mechanical properties of ideal-geometry models and as-manufactured porous biomaterials with average errors of 49% and 41% respectively for compressive elastic modulus and yield strength. The proposed methodology gives more accurate predictions for the compressive stiffness and the compressive strength properties with a reduction of the average error to 11% and 7.6%. The implications of the results and the methodology here introduced are discussed in the relevant biomechanical and clinical context, with insight that highlights promises and limitations of additively manufactured porous biomaterials for load-bearing bone replacement implants. In this work, we perform mechanical characterization of load-bearing porous biomaterials for bone replacement over their entire design
Numerical Modelling and Damage Assessment of Rotary Wing Aircraft Cabin Door Using Continuum Damage Mechanics Model

Science.gov (United States)

Boyina, Gangadhara Rao T.; Rayavarapu, Vijaya Kumar; V. V., Subba Rao

2017-02-01

The prediction of ultimate strength remains the main challenge in the simulation of the mechanical response of composite structures. This paper examines continuum damage model to predict the strength and size effects for deformation and failure response of polymer composite laminates when subjected to complex state of stress. The paper also considers how the overall results of the exercise can be applied in design applications. The continuum damage model is described and the resulting prediction of size effects are compared against the standard benchmark solutions. The stress analysis for strength prediction of rotary wing aircraft cabin door is carried out. The goal of this study is to extend the proposed continuum damage model such that it can be accurately predict the failure around stress concentration regions. The finite element-based continuum damage mechanics model can be applied to the structures and components of arbitrary configurations where analytical solutions could not be developed.
Fracture mechanics model of fragmentation

International Nuclear Information System (INIS)

Glenn, L.A.; Gommerstadt, B.Y.; Chudnovsky, A.

1986-01-01

A model of the fragmentation process is developed, based on the theory of linear elastic fracture mechanics, which predicts the average fragment size as a function of strain rate and material properties. This approach permits a unification of previous results, yielding Griffith's solution in the low-strain-rate limit and Grady's solution at high strain rates

Brain mechanisms in religion and spirituality : An integrative predictive processing framework

NARCIS (Netherlands)

van Elk, Michiel; Aleman, Andre

We present the theory of predictive processing as a unifying framework to account for the neurocognitive basis of religion and spirituality. Our model is substantiated by discussing four different brain mechanisms that play a key role in religion and spirituality: temporal brain areas are associated
Rationally designed synthetic protein hydrogels with predictable mechanical properties.

Science.gov (United States)

Wu, Junhua; Li, Pengfei; Dong, Chenling; Jiang, Heting; Bin Xue; Gao, Xiang; Qin, Meng; Wang, Wei; Bin Chen; Cao, Yi

2018-02-12

Designing synthetic protein hydrogels with tailored mechanical properties similar to naturally occurring tissues is an eternal pursuit in tissue engineering and stem cell and cancer research. However, it remains challenging to correlate the mechanical properties of protein hydrogels with the nanomechanics of individual building blocks. Here we use single-molecule force spectroscopy, protein engineering and theoretical modeling to prove that the mechanical properties of protein hydrogels are predictable based on the mechanical hierarchy of the cross-linkers and the load-bearing modules at the molecular level. These findings provide a framework for rationally designing protein hydrogels with independently tunable elasticity, extensibility, toughness and self-healing. Using this principle, we demonstrate the engineering of self-healable muscle-mimicking hydrogels that can significantly dissipate energy through protein unfolding. We expect that this principle can be generalized for the construction of protein hydrogels with customized mechanical properties for biomedical applications.
Model of lifetime prediction - Study of the behaviour of polymers and organic matrix composites

International Nuclear Information System (INIS)

Colin, X.

2009-01-01

The team 'Aging of Organic Materials' of the Process and Engineering Laboratory in Mechanics and Materials (Arts et Metiers, ParisTech) has developed the model of lifetime prediction for the prediction of the behaviour of polymers and organic composites. This model has already given evidence of a real predictive mean for various industrial applications, as for instance the prediction of a rupture under the coupled effect of a mechanical load and a chemical degradation. (O.M.)
Bootstrap prediction and Bayesian prediction under misspecified models

OpenAIRE

Fushiki, Tadayoshi

2005-01-01

We consider a statistical prediction problem under misspecified models. In a sense, Bayesian prediction is an optimal prediction method when an assumed model is true. Bootstrap prediction is obtained by applying Breiman's `bagging' method to a plug-in prediction. Bootstrap prediction can be considered to be an approximation to the Bayesian prediction under the assumption that the model is true. However, in applications, there are frequently deviations from the assumed model. In this paper, bo...
Modeling of Complex Life Cycle Prediction Based on Cell Division

Directory of Open Access Journals (Sweden)

Fucheng Zhang

2017-01-01

Full Text Available Effective fault diagnosis and reasonable life expectancy are of great significance and practical engineering value for the safety, reliability, and maintenance cost of equipment and working environment. At present, the life prediction methods of the equipment are equipment life prediction based on condition monitoring, combined forecasting model, and driven data. Most of them need to be based on a large amount of data to achieve the problem. For this issue, we propose learning from the mechanism of cell division in the organism. We have established a moderate complexity of life prediction model across studying the complex multifactor correlation life model. In this paper, we model the life prediction of cell division. Experiments show that our model can effectively simulate the state of cell division. Through the model of reference, we will use it for the equipment of the complex life prediction.
Mechanical performance of injection molded polypropylene : characterization and modeling

NARCIS (Netherlands)

Erp, van T.B.; Govaert, L.E.; Peters, G.W.M.

2013-01-01

It is shown that predictions of local mechanical properties in a product can be made from the orientation only using an anisotropic viscoplastic model. Due to processing-induced crystalline orientations, the mechanical properties of injection-molded polymer products are anisotropic and exhibit
Impact of implementation choices on quantitative predictions of cell-based computational models

Science.gov (United States)

Kursawe, Jochen; Baker, Ruth E.; Fletcher, Alexander G.

2017-09-01

'Cell-based' models provide a powerful computational tool for studying the mechanisms underlying the growth and dynamics of biological tissues in health and disease. An increasing amount of quantitative data with cellular resolution has paved the way for the quantitative parameterisation and validation of such models. However, the numerical implementation of cell-based models remains challenging, and little work has been done to understand to what extent implementation choices may influence model predictions. Here, we consider the numerical implementation of a popular class of cell-based models called vertex models, which are often used to study epithelial tissues. In two-dimensional vertex models, a tissue is approximated as a tessellation of polygons and the vertices of these polygons move due to mechanical forces originating from the cells. Such models have been used extensively to study the mechanical regulation of tissue topology in the literature. Here, we analyse how the model predictions may be affected by numerical parameters, such as the size of the time step, and non-physical model parameters, such as length thresholds for cell rearrangement. We find that vertex positions and summary statistics are sensitive to several of these implementation parameters. For example, the predicted tissue size decreases with decreasing cell cycle durations, and cell rearrangement may be suppressed by large time steps. These findings are counter-intuitive and illustrate that model predictions need to be thoroughly analysed and implementation details carefully considered when applying cell-based computational models in a quantitative setting.
Modelling time-dependent mechanical behaviour of softwood using deformation kinetics

DEFF Research Database (Denmark)

Engelund, Emil Tang; Svensson, Staffan

2010-01-01

The time-dependent mechanical behaviour (TDMB) of softwood is relevant, e.g., when wood is used as building material where the mechanical properties must be predicted for decades ahead. The established mathematical models should be able to predict the time-dependent behaviour. However, these models...... are not always based on the actual physical processes causing time-dependent behaviour and the physical interpretation of their input parameters is difficult. The present study describes the TDMB of a softwood tissue and its individual tracheids. A model is constructed with a local coordinate system that follows...... macroscopic viscoelasticity, i.e., the time-dependent processes are to a significant degree reversible....
Prediction of Chemical Function: Model Development and Application

Science.gov (United States)

The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (...
Predictive Modelling and Time: An Experiment in Temporal Archaeological Predictive Models

OpenAIRE

David Ebert

2006-01-01

One of the most common criticisms of archaeological predictive modelling is that it fails to account for temporal or functional differences in sites. However, a practical solution to temporal or functional predictive modelling has proven to be elusive. This article discusses temporal predictive modelling, focusing on the difficulties of employing temporal variables, then introduces and tests a simple methodology for the implementation of temporal modelling. The temporal models thus created ar...
A theoretical model for predicting the Peak Cutting Force of conical picks

Directory of Open Access Journals (Sweden)

Gao Kuidong

2014-01-01

Full Text Available In order to predict the PCF (Peak Cutting Force of conical pick in rock cutting process, a theoretical model is established based on elastic fracture mechanics theory. The vertical fracture model of rock cutting fragment is also established based on the maximum tensile criterion. The relation between vertical fracture angle and associated parameters (cutting parameter  and ratio B of rock compressive strength to tensile strength is obtained by numerical analysis method and polynomial regression method, and the correctness of rock vertical fracture model is verified through experiments. Linear regression coefficient between the PCF of prediction and experiments is 0.81, and significance level less than 0.05 shows that the model for predicting the PCF is correct and reliable. A comparative analysis between the PCF obtained from this model and Evans model reveals that the result of this prediction model is more reliable and accurate. The results of this work could provide some guidance for studying the rock cutting theory of conical pick and designing the cutting mechanism.
The Prediction of Drought-Related Tree Mortality in Vegetation Models

Science.gov (United States)

Schwinning, S.; Jensen, J.; Lomas, M. R.; Schwartz, B.; Woodward, F. I.

2013-12-01

Drought-related tree die-off events at regional scales have been reported from all wooded continents and it has been suggested that their frequency may be increasing. The prediction of these drought-related die-off events from regional to global scales has been recognized as a critical need for the conservation of forest resources and improving the prediction of climate-vegetation interactions. However, there is no conceptual consensus on how to best approach the quantitative prediction of tree mortality. Current models use a variety of mechanisms to represent demographic events. Mortality is modeled to represent a number of different processes, including death by fire, wind throw, extreme temperatures, and self-thinning, and each vegetation model differs in the emphasis they place on specific mechanisms. Dynamic global vegetation models generally operate on the assumption of incremental vegetation shift due to changes in the carbon economy of plant functional types and proportional effects on recruitment, growth, competition and mortality, but this may not capture sudden and sweeping tree death caused by extreme weather conditions. We tested several different approaches to predicting tree mortality within the framework of the Sheffield Dynamic Global Vegetation Model. We applied the model to the state of Texas, USA, which in 2011 experienced extreme drought conditions, causing the death of an estimated 300 million trees statewide. We then compared predicted to actual mortality to determine which algorithms most accurately predicted geographical variation in tree mortality. We discuss implications regarding the ongoing debate on the causes of tree death.
Thermodynamical aspects of modeling the mechanical response of granular materials

International Nuclear Information System (INIS)

Elata, D.

1995-01-01

In many applications in rock physics, the material is treated as a continuum. By supplementing the related conservation laws with constitutive equations such as stress-strain relations, a well-posed problem can be formulated and solved. The stress-strain relations may be based on a combination of experimental data and a phenomenological or micromechanical model. If the model is physically sound and its parameters have a physical meaning, it can serve to predict the stress response of the material to unmeasured deformations, predict the stress response of other materials, and perhaps predict other categories of the mechanical response such as failure, permeability, and conductivity. However, it is essential that the model be consistent with all conservation laws and consistent with the second law of thermodynamics. Specifically, some models of the mechanical response of granular materials proposed in literature, are based on intergranular contact force-displacement laws that violate the second law of thermodynamics by permitting energy generation at no cost. This diminishes the usefulness of these models as it invalidates their predictive capabilities. [This work was performed under the auspices of the U.S. DOE by Lawrence Livermore National Laboratory under Contract No. W-7405-ENG-48.
An optimized chemical kinetic mechanism for HCCI combustion of PRFs using multi-zone model and genetic algorithm

International Nuclear Information System (INIS)

Neshat, Elaheh; Saray, Rahim Khoshbakhti

2015-01-01

Highlights: • A new chemical kinetic mechanism for PRFs HCCI combustion is developed. • New mechanism optimization is performed using genetic algorithm and multi-zone model. • Engine-related combustion and performance parameters are predicted accurately. • Engine unburned HC and CO emissions are predicted by the model properly. - Abstract: Development of comprehensive chemical kinetic mechanisms is required for HCCI combustion and emissions prediction to be used in engine development. The main purpose of this study is development of a new chemical kinetic mechanism for primary reference fuels (PRFs) HCCI combustion, which can be applied to combustion models to predict in-cylinder pressure and exhaust CO and UHC emissions, accurately. Hence, a multi-zone model is developed for HCCI engine simulation. Two semi-detailed chemical kinetic mechanisms those are suitable for premixed combustion are used for n-heptane and iso-octane HCCI combustion simulation. The iso-octane mechanism contains 84 species and 484 reactions and the n-heptane mechanism contains 57 species and 296 reactions. A simple interaction between iso-octane and n-heptane is considered in new mechanism. The multi-zone model is validated using experimental data for pure n-heptane and iso-octane. A new mechanism is prepared by combination of these two mechanisms for n-heptane and iso-octane blended fuel, which includes 101 species and 594 reactions. New mechanism optimization is performed using genetic algorithm and multi-zone model. Mechanism contains low temperature heat release region, which decreases with increasing octane number. The results showed that the optimized chemical kinetic mechanism is capable of predicting engine-related combustion and performance parameters. Also after implementing the optimized mechanism, engine unburned HC and CO emissions predicted by the model are in good agreement with the corresponding experimental data
Repeat-swap homology modeling of secondary active transporters: updated protocol and prediction of elevator-type mechanisms.

Science.gov (United States)

Vergara-Jaque, Ariela; Fenollar-Ferrer, Cristina; Kaufmann, Desirée; Forrest, Lucy R

2015-01-01

Secondary active transporters are critical for neurotransmitter clearance and recycling during synaptic transmission and uptake of nutrients. These proteins mediate the movement of solutes against their concentration gradients, by using the energy released in the movement of ions down pre-existing concentration gradients. To achieve this, transporters conform to the so-called alternating-access hypothesis, whereby the protein adopts at least two conformations in which the substrate binding sites are exposed to one or other side of the membrane, but not both simultaneously. Structures of a bacterial homolog of neuronal glutamate transporters, GltPh, in several different conformational states have revealed that the protein structure is asymmetric in the outward- and inward-open states, and that the conformational change connecting them involves a elevator-like movement of a substrate binding domain across the membrane. The structural asymmetry is created by inverted-topology repeats, i.e., structural repeats with similar overall folds whose transmembrane topologies are related to each other by two-fold pseudo-symmetry around an axis parallel to the membrane plane. Inverted repeats have been found in around three-quarters of secondary transporter folds. Moreover, the (a)symmetry of these systems has been successfully used as a bioinformatic tool, called "repeat-swap modeling" to predict structural models of a transporter in one conformation using the known structure of the transporter in the complementary conformation as a template. Here, we describe an updated repeat-swap homology modeling protocol, and calibrate the accuracy of the method using GltPh, for which both inward- and outward-facing conformations are known. We then apply this repeat-swap homology modeling procedure to a concentrative nucleoside transporter, VcCNT, which has a three-dimensional arrangement related to that of GltPh. The repeat-swapped model of VcCNT predicts that nucleoside transport also
Repeat-swap homology modeling of secondary active transporters: updated protocol and prediction of elevator-type mechanisms

Directory of Open Access Journals (Sweden)

Cristina eFenollar Ferrer

2015-09-01

Full Text Available Secondary active transporters are critical for neurotransmitter clearance and recycling during synaptic transmission and uptake of nutrients. These proteins mediate the movement of solutes against their concentration gradients, by using the energy released in the movement of ions down pre-existing concentration gradients. To achieve this, transporters conform to the so-called alternating-access hypothesis, whereby the protein adopts at least two conformations in which the substrate binding sites are exposed to either the outside or inside of the membrane, but not both simultaneously. Structures of a bacterial homolog of neuronal glutamate transporters, GltPh, in several different conformational states have revealed that the protein structure is asymmetric in the outward- and inward-open states, and that the conformational change connecting them involves a elevator-like movement of a substrate binding domain across the membrane. The structural asymmetry is created by inverted-topology repeats, i.e., structural repeats with similar overall folds whose transmembrane topologies are related to each other by two-fold pseudo-symmetry around an axis parallel to the membrane plane. Inverted repeats have been found in around three-quarters of secondary transporter folds. Moreover, the (asymmetry of these systems has been successfully used as a bioinformatic tool, called repeat-swap modeling to predict structural models of a transporter in one conformation using the known structure of the transporter in the complementary conformation as a template. Here, we describe an updated repeat-swap homology modeling protocol, and calibrate the accuracy of the method using GltPh, for which both inward- and outward-facing conformations are known. We then apply this repeat-swap homology modeling procedure to a concentrative nucleoside transporter, VcCNT, which has a three-dimensional arrangement related to that of GltPh. The repeat-swapped model of VcCNT predicts that
Probabilistic predictive modelling of carbon nanocomposites for medical implants design.

Science.gov (United States)

Chua, Matthew; Chui, Chee-Kong

2015-04-01

Modelling of the mechanical properties of carbon nanocomposites based on input variables like percentage weight of Carbon Nanotubes (CNT) inclusions is important for the design of medical implants and other structural scaffolds. Current constitutive models for the mechanical properties of nanocomposites may not predict well due to differences in conditions, fabrication techniques and inconsistencies in reagents properties used across industries and laboratories. Furthermore, the mechanical properties of the designed products are not deterministic, but exist as a probabilistic range. A predictive model based on a modified probabilistic surface response algorithm is proposed in this paper to address this issue. Tensile testing of three groups of different CNT weight fractions of carbon nanocomposite samples displays scattered stress-strain curves, with the instantaneous stresses assumed to vary according to a normal distribution at a specific strain. From the probabilistic density function of the experimental data, a two factors Central Composite Design (CCD) experimental matrix based on strain and CNT weight fraction input with their corresponding stress distribution was established. Monte Carlo simulation was carried out on this design matrix to generate a predictive probabilistic polynomial equation. The equation and method was subsequently validated with more tensile experiments and Finite Element (FE) studies. The method was subsequently demonstrated in the design of an artificial tracheal implant. Our algorithm provides an effective way to accurately model the mechanical properties in implants of various compositions based on experimental data of samples. Copyright © 2015 Elsevier Ltd. All rights reserved.
Predicting Silk Fiber Mechanical Properties through Multiscale Simulation and Protein Design.

Science.gov (United States)

Rim, Nae-Gyune; Roberts, Erin G; Ebrahimi, Davoud; Dinjaski, Nina; Jacobsen, Matthew M; Martín-Moldes, Zaira; Buehler, Markus J; Kaplan, David L; Wong, Joyce Y

2017-08-14

Silk is a promising material for biomedical applications, and much research is focused on how application-specific, mechanical properties of silk can be designed synthetically through proper amino acid sequences and processing parameters. This protocol describes an iterative process between research disciplines that combines simulation, genetic synthesis, and fiber analysis to better design silk fibers with specific mechanical properties. Computational methods are used to assess the protein polymer structure as it forms an interconnected fiber network through shearing and how this process affects fiber mechanical properties. Model outcomes are validated experimentally with the genetic design of protein polymers that match the simulation structures, fiber fabrication from these polymers, and mechanical testing of these fibers. Through iterative feedback between computation, genetic synthesis, and fiber mechanical testing, this protocol will enable a priori prediction capability of recombinant material mechanical properties via insights from the resulting molecular architecture of the fiber network based entirely on the initial protein monomer composition. This style of protocol may be applied to other fields where a research team seeks to design a biomaterial with biomedical application-specific properties. This protocol highlights when and how the three research groups (simulation, synthesis, and engineering) should be interacting to arrive at the most effective method for predictive design of their material.
Sea surface salinity and temperature-based predictive modeling of southwestern US winter precipitation: improvements, errors, and potential mechanisms

Science.gov (United States)

Liu, T.; Schmitt, R. W.; Li, L.

2017-12-01

Using 69 years of historical data from 1948-2017, we developed a method to globally search for sea surface salinity (SSS) and temperature (SST) predictors of regional terrestrial precipitation. We then applied this method to build an autumn (SON) SSS and SST-based 3-month lead predictive model of winter (DJF) precipitation in southwestern United States. We also find that SSS-only models perform better than SST-only models. We previously used an arbitrary correlation coefficient (r) threshold, |r| > 0.25, to define SSS and SST predictor polygons for best subset regression of southwestern US winter precipitation; from preliminary sensitivity tests, we find that |r| > 0.18 yields the best models. The observed below-average precipitation (0.69 mm/day) in winter 2015-2016 falls within the 95% confidence interval of the prediction model. However, the model underestimates the anomalous high precipitation (1.78 mm/day) in winter 2016-2017 by more than three-fold. Moisture transport mainly attributed to "pineapple express" atmospheric rivers (ARs) in winter 2016-2017 suggests that the model falls short on a sub-seasonal scale, in which case storms from ARs contribute a significant portion of seasonal terrestrial precipitation. Further, we identify a potential mechanism for long-range SSS and precipitation teleconnections: standing Rossby waves. The heat applied to the atmosphere from anomalous tropical rainfall can generate standing Rossby waves that propagate to higher latitudes. SSS anomalies may be indicative of anomalous tropical rainfall, and by extension, standing Rossby waves that provide the long-range teleconnections.
Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation.

Science.gov (United States)

Pearce, Marcus T

2018-05-11

Music perception depends on internal psychological models derived through exposure to a musical culture. It is hypothesized that this musical enculturation depends on two cognitive processes: (1) statistical learning, in which listeners acquire internal cognitive models of statistical regularities present in the music to which they are exposed; and (2) probabilistic prediction based on these learned models that enables listeners to organize and process their mental representations of music. To corroborate these hypotheses, I review research that uses a computational model of probabilistic prediction based on statistical learning (the information dynamics of music (IDyOM) model) to simulate data from empirical studies of human listeners. The results show that a broad range of psychological processes involved in music perception-expectation, emotion, memory, similarity, segmentation, and meter-can be understood in terms of a single, underlying process of probabilistic prediction using learned statistical models. Furthermore, IDyOM simulations of listeners from different musical cultures demonstrate that statistical learning can plausibly predict causal effects of differential cultural exposure to musical styles, providing a quantitative model of cultural distance. Understanding the neural basis of musical enculturation will benefit from close coordination between empirical neuroimaging and computational modeling of underlying mechanisms, as outlined here. © 2018 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals, Inc. on behalf of New York Academy of Sciences.

Learning Predictive Statistics: Strategies and Brain Mechanisms.

Science.gov (United States)

Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E; Kourtzi, Zoe

2017-08-30

When immersed in a new environment, we are challenged to decipher initially incomprehensible streams of sensory information. However, quite rapidly, the brain finds structure and meaning in these incoming signals, helping us to predict and prepare ourselves for future actions. This skill relies on extracting the statistics of event streams in the environment that contain regularities of variable complexity from simple repetitive patterns to complex probabilistic combinations. Here, we test the brain mechanisms that mediate our ability to adapt to the environment's statistics and predict upcoming events. By combining behavioral training and multisession fMRI in human participants (male and female), we track the corticostriatal mechanisms that mediate learning of temporal sequences as they change in structure complexity. We show that learning of predictive structures relates to individual decision strategy; that is, selecting the most probable outcome in a given context (maximizing) versus matching the exact sequence statistics. These strategies engage distinct human brain regions: maximizing engages dorsolateral prefrontal, cingulate, sensory-motor regions, and basal ganglia (dorsal caudate, putamen), whereas matching engages occipitotemporal regions (including the hippocampus) and basal ganglia (ventral caudate). Our findings provide evidence for distinct corticostriatal mechanisms that facilitate our ability to extract behaviorally relevant statistics to make predictions. SIGNIFICANCE STATEMENT Making predictions about future events relies on interpreting streams of information that may initially appear incomprehensible. Past work has studied how humans identify repetitive patterns and associative pairings. However, the natural environment contains regularities that vary in complexity from simple repetition to complex probabilistic combinations. Here, we combine behavior and multisession fMRI to track the brain mechanisms that mediate our ability to adapt to
Prediction of lithium-ion battery capacity with metabolic grey model

International Nuclear Information System (INIS)

Chen, Lin; Lin, Weilong; Li, Junzi; Tian, Binbin; Pan, Haihong

2016-01-01

Given the popularity of Lithium-ion batteries in EVs (electric vehicles), predicting the capacity quickly and accurately throughout a battery's full life-time is still a challenging issue for ensuring the reliability of EVs. This paper proposes an approach in predicting the varied capacity with discharge cycles based on metabolic grey theory and consider issues from two perspectives: 1) three metabolic grey models will be presented, including MGM (metabolic grey model), MREGM (metabolic Residual-error grey model), and MMREGM (metabolic Markov-residual-error grey model); 2) the universality of these models will be explored under different conditions (such as various discharge rates and temperatures). Furthermore, the research findings in this paper demonstrate the excellent performance of the prediction depending on the three models; however, the precision of the MREGM model is inferior compared to the others. Therefore, we have obtained the conclusion in which the MGM model and the MMREGM model have excellent performances in predicting the capacity under a variety of load conditions, even using few data points for modeling. Also, the universality of the metabolic grey prediction theory is verified by predicting the capacity of batteries under different discharge rates and different temperatures. - Highlights: • The metabolic mechanism is introduced in a grey system for capacity prediction. • Three metabolic grey models are presented and studied. • The universality of these models under different conditions is assessed. • A few data points are required for predicting the capacity with these models.
Modeling of mechanical properties in alpha/beta-titanium alloys

Science.gov (United States)

Kar, Sujoy Kumar

2005-11-01

The accelerated insertion of titanium alloys in component application requires the development of predictive capabilities for various aspects of their behavior, for example, phase stability, microstructural evolution and property-microstructure relationships over a wide range of length and time scales. In this presentation some navel aspects of property-microstructure relationships and microstructural evolution in alpha/beta Ti alloys will be discussed. Neural Network (NN) Models based on a Bayesian framework have been developed to predict the mechanical properties of alpha/beta Ti alloys. The development of such rules-based model requires the population of extensive databases, which in the present case are microstructurally-based. The steps involved in database development include producing controlled variations of the microstructure using novel approaches to heat-treatments, the use of standardized stereology protocols to characterize and quantify microstructural features rapidly, and mechanical testing of the heat-treated specimens. These databases have been used to train and test NN Models for prediction of mechanical properties. In addition, these models have been used to identify the influence of individual microstructural features on the mechanical properties, consequently guiding the efforts towards development of more robust mechanistically based models. In order to understand the property-microstructure relationships, a detailed understanding of microstructure evolution is imperative. The crystallography of the microstructure developing as a result of the solid-state beta → beta+alpha transformation has been studied in detail by employing Scanning Electron Microscopy (SEM), Orientation Imaging Microscopy (in a high resolution SEM), site-specific TEM sample preparation using focused ion beam, and TEM based techniques. The influence of variant selection on the evolution of microstructure will be specifically addressed.
An Intelligent Model for Stock Market Prediction

Directory of Open Access Journals (Sweden)

IbrahimM. Hamed

2012-08-01

Full Text Available This paper presents an intelligent model for stock market signal prediction using Multi-Layer Perceptron (MLP Artificial Neural Networks (ANN. Blind source separation technique, from signal processing, is integrated with the learning phase of the constructed baseline MLP ANN to overcome the problems of prediction accuracy and lack of generalization. Kullback Leibler Divergence (KLD is used, as a learning algorithm, because it converges fast and provides generalization in the learning mechanism. Both accuracy and efficiency of the proposed model were confirmed through the Microsoft stock, from wall-street market, and various data sets, from different sectors of the Egyptian stock market. In addition, sensitivity analysis was conducted on the various parameters of the model to ensure the coverage of the generalization issue. Finally, statistical significance was examined using ANOVA test.
Improved predictive modeling of white LEDs with accurate luminescence simulation and practical inputs with TracePro opto-mechanical design software

Science.gov (United States)

Tsao, Chao-hsi; Freniere, Edward R.; Smith, Linda

2009-02-01

The use of white LEDs for solid-state lighting to address applications in the automotive, architectural and general illumination markets is just emerging. LEDs promise greater energy efficiency and lower maintenance costs. However, there is a significant amount of design and cost optimization to be done while companies continue to improve semiconductor manufacturing processes and begin to apply more efficient and better color rendering luminescent materials such as phosphor and quantum dot nanomaterials. In the last decade, accurate and predictive opto-mechanical software modeling has enabled adherence to performance, consistency, cost, and aesthetic criteria without the cost and time associated with iterative hardware prototyping. More sophisticated models that include simulation of optical phenomenon, such as luminescence, promise to yield designs that are more predictive - giving design engineers and materials scientists more control over the design process to quickly reach optimum performance, manufacturability, and cost criteria. A design case study is presented where first, a phosphor formulation and excitation source are optimized for a white light. The phosphor formulation, the excitation source and other LED components are optically and mechanically modeled and ray traced. Finally, its performance is analyzed. A blue LED source is characterized by its relative spectral power distribution and angular intensity distribution. YAG:Ce phosphor is characterized by relative absorption, excitation and emission spectra, quantum efficiency and bulk absorption coefficient. Bulk scatter properties are characterized by wavelength dependent scatter coefficients, anisotropy and bulk absorption coefficient.
Prediction Model of Machining Failure Trend Based on Large Data Analysis

Science.gov (United States)

Li, Jirong

2017-12-01

The mechanical processing has high complexity, strong coupling, a lot of control factors in the machining process, it is prone to failure, in order to improve the accuracy of fault detection of large mechanical equipment, research on fault trend prediction requires machining, machining fault trend prediction model based on fault data. The characteristics of data processing using genetic algorithm K mean clustering for machining, machining feature extraction which reflects the correlation dimension of fault, spectrum characteristics analysis of abnormal vibration of complex mechanical parts processing process, the extraction method of the abnormal vibration of complex mechanical parts processing process of multi-component spectral decomposition and empirical mode decomposition Hilbert based on feature extraction and the decomposition results, in order to establish the intelligent expert system for the data base, combined with large data analysis method to realize the machining of the Fault trend prediction. The simulation results show that this method of fault trend prediction of mechanical machining accuracy is better, the fault in the mechanical process accurate judgment ability, it has good application value analysis and fault diagnosis in the machining process.
Effect of continuum damage mechanics on spring back prediction in metal forming processes

International Nuclear Information System (INIS)

Nayebi, Ali; Shahabi, Mehdi

2017-01-01

The influence of considering the variations in material properties was investigated through continuum damage mechanics according to the Lemaitre isotropic unified damage law to predict the bending force and spring back in V-bending sheet metal forming processes, with emphasis on Finite element (FE) simulation considerations. The material constants of the damage model were calibrated through a uniaxial tensile test with an appropriate and convenient repeating strategy. Holloman’s isotropic and Ziegler’s linear kinematic hardening laws were employed to describe the behavior of a hardening material. To specify the ideal FE conditions for simulating spring back, the effect of the various numerical considerations during FE simulation was investigated and compared with the experimental outcome. Results indicate that considering continuum damage mechanics decreased the predicted bending force and improved the accuracy of spring back prediction.
Durability and life prediction modeling in polyimide composites

Science.gov (United States)

Binienda, Wieslaw K.

1995-01-01

Sudden appearance of cracks on a macroscopically smooth surface of brittle materials due to cooling or drying shrinkage is a phenomenon related to many engineering problems. Although conventional strength theories can be used to predict the necessary condition for crack appearance, they are unable to predict crack spacing and depth. On the other hand, fracture mechanics theory can only study the behavior of existing cracks. The theory of crack initiation can be summarized into three conditions, which is a combination of a strength criterion and laws of energy conservation, the average crack spacing and depth can thus be determined. The problem of crack initiation from the surface of an elastic half plane is solved and compares quite well with available experimental evidence. The theory of crack initiation is also applied to concrete pavements. The influence of cracking is modeled by the additional compliance according to Okamura's method. The theoretical prediction by this structural mechanics type of model correlates very well with the field observation. The model may serve as a theoretical foundation for future pavement joint design. The initiation of interactive cracks of quasi-brittle material is studied based on a theory of cohesive crack model. These cracks may grow simultaneously, or some of them may close during certain stages. The concept of crack unloading of cohesive crack model is proposed. The critical behavior (crack bifurcation, maximum loads) of the cohesive crack model are characterized by rate equations. The post-critical behavior of crack initiation is also studied.
Modeling The Effect Of Extruder Screw Speed On The Mechanical ...

African Journals Online (AJOL)

Modeling The Effect Of Extruder Screw Speed On The Mechanical Properties Of High Density Polyethylene Blown Film. ... Journal of Modeling, Design and Management of Engineering Systems ... Two sets of multiple linear regression models were developed to predict impact failure weight and tenacity respectively.
Reduced chemical kinetic mechanisms for NOx emission prediction in biomass combustion

DEFF Research Database (Denmark)

Houshfar, Ehsan; Skreiberg, Øyvind; Glarborg, Peter

2012-01-01

Because of the complex composition of biomass, the chemical mechanism contains many different species and therefore a large number of reactions. Although biomass gas‐phase combustion is fairly well researched and understood, the proposed mechanisms are still complex and need very long computational...... time and powerful hardware resources. A reduction of the mechanism for biomass volatile oxidation has therefore been performed to avoid these difficulties. The selected detailed mechanism in this study contains 81 species and 703 elementary reactions. Necessity analysis is used to determine which...... reactions and chemical species, that is, 35 species and 198 reactions, corresponding to 72% reduction in the number of reactions and, therefore, improving the computational time considerably. Yet, the model based on the reduced mechanism predicts correctly concentrations of NOx and CO that are essentially...
On the use of effective stress in three-dimensional hydro-mechanical coupled model

International Nuclear Information System (INIS)

Arairo, W.; Prunier, F.; Djeran-Maigre, I.; Millard, A.

2014-01-01

In the last decades, a number of hydro-mechanical elastoplastic constitutive models for unsaturated soils have been proposed. Those models couple the hydraulic and mechanical behaviour of unsaturated soils, and take into account the effects of the degree of saturation on the stress-strain behaviour and the effects of deformation on the soil-water characteristic response with a simple reversible part for the hysteresis. In addition, the influence of the suction on the stress-strain behaviour is considered. However, until now, few models predict the stress-strain and soil-water characteristic responses of unsaturated soils in a fully three-dimensional Finite Element code. This paper presents the predictions of an unsaturated soil model in a Three-dimensional Framework, and develops a study on the effect of partial saturation on the stability of shallow foundation resting on unsaturated silty soil. Qualitative predictions of the constitutive model show that incorporating a special formulation for the effective stress into an elastoplastic coupled hydro-mechanical model opens a full range of possibilities in modelling unsaturated soil behaviour. (authors)
Model predictive control based on reduced order models applied to belt conveyor system.

Science.gov (United States)

Chen, Wei; Li, Xin

2016-11-01

In the paper, a model predictive controller based on reduced order model is proposed to control belt conveyor system, which is an electro-mechanics complex system with long visco-elastic body. Firstly, in order to design low-degree controller, the balanced truncation method is used for belt conveyor model reduction. Secondly, MPC algorithm based on reduced order model for belt conveyor system is presented. Because of the error bound between the full-order model and reduced order model, two Kalman state estimators are applied in the control scheme to achieve better system performance. Finally, the simulation experiments are shown that balanced truncation method can significantly reduce the model order with high-accuracy and model predictive control based on reduced-model performs well in controlling the belt conveyor system. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
A mechanical deformation model of metallic fuel pin under steady state conditions

International Nuclear Information System (INIS)

Lee, D. W.; Lee, B. W.; Kim, Y. I.; Han, D. H.

2004-01-01

As a mechanical deformation model of the MACSIS code predicts the cladding deformation due to the simple thin shell theory, it is impossible to predict the FCMI(Fuel-Cladding Mechanical Interaction). Therefore, a mechanical deformation model used the generalized plane strain is developed. The DEFORM is a mechanical deformation routine which is used to analyze the stresses and strains in the fuel and cladding of a metallic fuel pin of LMRs. The accuracy of the program is demonstrated by comparison of the DEFORM predictions with the result of another code calculations or experimental results in literature. The stress/strain distributions of elastic part under free thermal expansion condition are completely matched with the results of ANSYS code. The swelling and creep solutions are reasonably well agreed with the simulations of ALFUS and LIFE-M codes, respectively. The predicted cladding strains are under estimated than experimental data at the range of high burnup. Therefore, it is recommended that the fine tuning of the DEFORM based on various range of experimental data
CT-Based Micro-Mechanical Approach to Predict Response of Closed-Cell Porous Biomaterials to Low-Velocity Impact

Directory of Open Access Journals (Sweden)

Mehrdad Koloushani

2018-03-01

Full Text Available In this study, a new numerical approach based on CT-scan images and finite element (FE method has been used to predict the mechanical behavior of closed-cell foams under impact loading. Micro-structural FE models based on CT-scan images of foam specimens (elastic-plastic material model with material constants of bulk aluminum and macro-mechanical FE models (with crushable foam material model with material constants of foams were constructed. Several experimental tests were also conducted to see which of the two noted (micro- or macro- mechanical FE models can better predict the deformation and force-displacement curves of foams. Compared to the macro-structural models, the results of the micro-structural models were much closer to the corresponding experimental results. This can be explained by the fact that the micro-structural models are able to take into account the interaction of stress waves with cell walls and the complex pathways the stress waves have to go through, while the macro-structural models do not have such capabilities. Despite their high demand for computational resources, using micro-scale FE models is very beneficial when one needs to understand the failure mechanisms acting in the micro-structure of a foam in order to modify or diminish them.
Mechanical modeling of the growth of salt structures

Energy Technology Data Exchange (ETDEWEB)

Alfaro, Ruben Alberto Mazariegos [Texas A & M Univ., College Station, TX (United States)

1993-05-01

A 2D numerical model for studying the morphology and history of salt structures by way of computer simulations is presented. The model is based on conservation laws for physical systems, a fluid marker equation to keep track of the salt/sediments interface, and two constitutive laws for rocksalt. When buoyancy alone is considered, the fluid-assisted diffusion model predicts evolution of salt structures 2.5 times faster than the power-law creep model. Both rheological laws predict strain rates of the order of 4.0 x 10^-15 s^-1 for similar structural maturity level of salt structures. Equivalent stresses and viscosities predicted by the fluid-assisted diffusion law are 10² times smaller than those predicted by the power-law creep rheology. Use of East Texas Basin sedimentation rates and power-law creep rheology indicate that differential loading is an effective mechanism to induce perturbations that amplify and evolve to mature salt structures, similar to those observed under natural geological conditions.
Prediction of contact mechanics in metal-on-metal Total Hip Replacement for parametrically comprehensive designs and loads.

Science.gov (United States)

Donaldson, Finn E; Nyman, Edward; Coburn, James C

2015-07-16

Manufacturers and investigators of Total Hip Replacement (THR) bearings require tools to predict the contact mechanics resulting from diverse design and loading parameters. This study provides contact mechanics solutions for metal-on-metal (MoM) bearings that encompass the current design space and could aid pre-clinical design optimization and evaluation. Stochastic finite element (FE) simulation was used to calculate the head-on-cup contact mechanics for five thousand combinations of design and loading parameters. FE results were used to train a Random Forest (RF) surrogate model to rapidly predict the contact patch dimensions, contact area, pressures and plastic deformations for arbitrary designs and loading. In addition to widely observed polar and edge contact, FE results included ring-polar, asymmetric-polar, and transitional categories which have previously received limited attention. Combinations of design and load parameters associated with each contact category were identified. Polar contact pressures were predicted in the range of 0-200 MPa with no permanent deformation. Edge loading (with subluxation) was associated with pressures greater than 500 MPa and induced permanent deformation in 83% of cases. Transitional-edge contact (with little subluxation) was associated with intermediate pressures and permanent deformation in most cases, indicating that, even with ideal anatomical alignment, bearings may face extreme wear challenges. Surrogate models were able to accurately predict contact mechanics 18,000 times faster than FE analyses. The developed surrogate models enable rapid prediction of MoM bearing contact mechanics across the most comprehensive range of loading and designs to date, and may be useful to those performing bearing design optimization or evaluation. Published by Elsevier Ltd.
Functional neuroimaging of psychotherapeutic processes in anxiety and depression: from mechanisms to predictions.

Science.gov (United States)

Lueken, Ulrike; Hahn, Tim

2016-01-01

The review provides an update of functional neuroimaging studies that identify neural processes underlying psychotherapy and predict outcomes following psychotherapeutic treatment in anxiety and depressive disorders. Following current developments in this field, studies were classified as 'mechanistic' or 'predictor' studies (i.e., informing neurobiological models about putative mechanisms versus aiming to provide predictive information). Mechanistic evidence points toward a dual-process model of psychotherapy in anxiety disorders with abnormally increased limbic activation being decreased, while prefrontal activity is increased. Partly overlapping findings are reported for depression, albeit with a stronger focus on prefrontal activation following treatment. No studies directly comparing neural pathways of psychotherapy between anxiety and depression were detected. Consensus is accumulating for an overarching role of the anterior cingulate cortex in modulating treatment response across disorders. When aiming to quantify clinical utility, the need for single-subject predictions is increasingly recognized and predictions based on machine learning approaches show high translational potential. Present findings encourage the search for predictors providing clinically meaningful information for single patients. However, independent validation as a crucial prerequisite for clinical use is still needed. Identifying nonresponders a priori creates the need for alternative treatment options that can be developed based on an improved understanding of those neural mechanisms underlying effective interventions.
ONKALO rock mechanics model (RMM) - Version 2.0

International Nuclear Information System (INIS)

Moenkkoenen, H.; Hakala, M.; Paananen, M.; Laine, E.

2012-02-01

The Rock Mechanics Model of the ONKALO rock volume is a description of the significant features and parameters related to rock mechanics. The main objective is to develop a tool to predict the rock properties, quality and hence the potential for stress failure which can then be used for continuing design of the ONKALO and the repository. This is the second implementation of the Rock Mechanics Model and it includes sub-models of the intact rock strength, in situ stress, thermal properties, rock mass quality and properties of the brittle deformation zones. Because of the varying quantities of available data for the different parameters, the types of presentations also vary: some data sets can be presented in the style of a 3D block model but, in other cases, a single distribution represents the whole rock volume hosting the ONKALO. (orig.)
Identifying mechanisms that structure ecological communities by snapping model parameters to empirically observed tradeoffs.

Science.gov (United States)

Thomas Clark, Adam; Lehman, Clarence; Tilman, David

2018-04-01

Theory predicts that interspecific tradeoffs are primary determinants of coexistence and community composition. Using information from empirically observed tradeoffs to augment the parametrisation of mechanism-based models should therefore improve model predictions, provided that tradeoffs and mechanisms are chosen correctly. We developed and tested such a model for 35 grassland plant species using monoculture measurements of three species characteristics related to nitrogen uptake and retention, which previous experiments indicate as important at our site. Matching classical theoretical expectations, these characteristics defined a distinct tradeoff surface, and models parameterised with these characteristics closely matched observations from experimental multi-species mixtures. Importantly, predictions improved significantly when we incorporated information from tradeoffs by 'snapping' characteristics to the nearest location on the tradeoff surface, suggesting that the tradeoffs and mechanisms we identify are important determinants of local community structure. This 'snapping' method could therefore constitute a broadly applicable test for identifying influential tradeoffs and mechanisms. © 2018 The Authors. Ecology Letters published by CNRS and John Wiley & Sons Ltd.
Characterization and modeling of mechanical behavior of quenching and partitioning steels

International Nuclear Information System (INIS)

Arlazarov, A.; Bouaziz, O.; Masse, J.P.; Kegel, F.

2015-01-01

Q and P annealing was applied to cold rolled carbon–manganese steel with Si. Q and P cycles with different partitioning temperature and time were simulated and the evolution of microstructure and mechanical properties was investigated. All the microstructures were composed of three constituents: partitioned martensite, laths of retained austenite and MA islands. Fine microstructure characterization confirmed that C diffusion plays an important role for the stabilization of retained austenite at room temperature and further TRIP effect during mechanical loading. Good compromise between yield strength (∼1200 MPa) and uniform elongation (∼11%) was found in the case of 400 °C partitioning for 300 s due to the enhanced mechanical stability of retained austenite. Evolution of microstructure and mechanical properties was discussed and some mechanisms were proposed to explain the observations. Mechanical model for the prediction of stress–strain curves of Q and P steels was proposed, based on the obtained experimental data. Accurate prediction of stress–strain curves using model was achieved

Characterization and modeling of mechanical behavior of quenching and partitioning steels

Energy Technology Data Exchange (ETDEWEB)

Arlazarov, A., E-mail: artem.arlazarov@arcelormittal.com [ArcelorMittal Global Research and Development, Voie Romaine-BP30320, 57283 Maizières-lès-Metz Cedex (France); Laboratoire d’Etude des Microstructures et de Mécanique des Matériaux (LEM3), CNRS UMR 7239, Université de Lorraine, 57012 Metz Cedex (France); Bouaziz, O. [ArcelorMittal Global Research and Development, Voie Romaine-BP30320, 57283 Maizières-lès-Metz Cedex (France); Laboratoire d’Etude des Microstructures et de Mécanique des Matériaux (LEM3), CNRS UMR 7239, Université de Lorraine, 57012 Metz Cedex (France); Masse, J.P.; Kegel, F. [ArcelorMittal Global Research and Development, Voie Romaine-BP30320, 57283 Maizières-lès-Metz Cedex (France)

2015-01-03

Q and P annealing was applied to cold rolled carbon–manganese steel with Si. Q and P cycles with different partitioning temperature and time were simulated and the evolution of microstructure and mechanical properties was investigated. All the microstructures were composed of three constituents: partitioned martensite, laths of retained austenite and MA islands. Fine microstructure characterization confirmed that C diffusion plays an important role for the stabilization of retained austenite at room temperature and further TRIP effect during mechanical loading. Good compromise between yield strength (∼1200 MPa) and uniform elongation (∼11%) was found in the case of 400 °C partitioning for 300 s due to the enhanced mechanical stability of retained austenite. Evolution of microstructure and mechanical properties was discussed and some mechanisms were proposed to explain the observations. Mechanical model for the prediction of stress–strain curves of Q and P steels was proposed, based on the obtained experimental data. Accurate prediction of stress–strain curves using model was achieved.
Fretting wear damage of steam generator tubes and its prediction modeling

International Nuclear Information System (INIS)

Che Honglong; Lei Mingkai

2013-01-01

The steam generator is the key equipment used for the energy transition in nuclear power plant. Since the high-temperature and high-pressure fluid flows with high speed, the steam generator tubes will be excited and vibrate, leading to the tremendous fretting wear problem on the tubes, sometimes even leading to tube cracking. This paper introduces typical fretting wear cases, the result of corresponding simulation wear experiment and damage mechanism which combining mechanical wear and erosion-corrosion. Work rate model could give a reasonable life prediction about the steam generator tube, and this predictive model has been used in nuclear power plant safety assessment. (authors)
Modeling for prediction of restrained shrinkage effect in concrete repair

International Nuclear Information System (INIS)

Yuan Yingshu; Li Guo; Cai Yue

2003-01-01

A general model of autogenous shrinkage caused by chemical reaction (chemical shrinkage) is developed by means of Arrhenius' law and a degree of chemical reaction. Models of tensile creep and relaxation modulus are built based on a viscoelastic, three-element model. Tests of free shrinkage and tensile creep were carried out to determine some coefficients in the models. Two-dimensional FEM analysis based on the models and other constitutions can predict the development of tensile strength and cracking. Three groups of patch-repaired beams were designed for analysis and testing. The prediction from the analysis shows agreement with the test results. The cracking mechanism after repair is discussed
AMOC decadal variability in Earth system models: Mechanisms and climate impacts

Energy Technology Data Exchange (ETDEWEB)

Fedorov, Alexey [Yale Univ., New Haven, CT (United States)

2017-09-06

This is the final report for the project titled "AMOC decadal variability in Earth system models: Mechanisms and climate impacts". The central goal of this one-year research project was to understand the mechanisms of decadal and multi-decadal variability of the Atlantic Meridional Overturning Circulation (AMOC) within a hierarchy of climate models ranging from realistic ocean GCMs to Earth system models. The AMOC is a key element of ocean circulation responsible for oceanic transport of heat from low to high latitudes and controlling, to a large extent, climate variations in the North Atlantic. The questions of the AMOC stability, variability and predictability, directly relevant to the questions of climate predictability, were at the center of the research work.
Improving students' meaningful learning on the predictive nature of quantum mechanics

Directory of Open Access Journals (Sweden)

Rodolfo Alves de Carvalho Neto

2009-03-01

Full Text Available This paper deals with research about teaching quantum mechanics to 3rd year high school students and their meaningful learning of its predictive aspect; it is based on the Master’s dissertation of one of the authors (CARVALHO NETO, 2006. While teaching quantum mechanics, we emphasized its predictive and essentially probabilistic nature, based on Niels Bohr’s complementarity interpretation (BOHR, 1958. In this context, we have discussed the possibility of predicting measurement results in well-defined experimental contexts, even for individual events. Interviews with students reveal that they have used quantum mechanical ideas, suggesting their meaningful learning of the essentially probabilistic predictions of quantum mechanics.
Ground Motion Prediction Model Using Artificial Neural Network

Science.gov (United States)

Dhanya, J.; Raghukanth, S. T. G.

2018-03-01

This article focuses on developing a ground motion prediction equation based on artificial neural network (ANN) technique for shallow crustal earthquakes. A hybrid technique combining genetic algorithm and Levenberg-Marquardt technique is used for training the model. The present model is developed to predict peak ground velocity, and 5% damped spectral acceleration. The input parameters for the prediction are moment magnitude ( M w), closest distance to rupture plane ( R rup), shear wave velocity in the region ( V s30) and focal mechanism ( F). A total of 13,552 ground motion records from 288 earthquakes provided by the updated NGA-West2 database released by Pacific Engineering Research Center are utilized to develop the model. The ANN architecture considered for the model consists of 192 unknowns including weights and biases of all the interconnected nodes. The performance of the model is observed to be within the prescribed error limits. In addition, the results from the study are found to be comparable with the existing relations in the global database. The developed model is further demonstrated by estimating site-specific response spectra for Shimla city located in Himalayan region.
An excitable cortex and memory model successfully predicts new pseudopod dynamics.

Directory of Open Access Journals (Sweden)

Robert M Cooper

Full Text Available Motile eukaryotic cells migrate with directional persistence by alternating left and right turns, even in the absence of external cues. For example, Dictyostelium discoideum cells crawl by extending distinct pseudopods in an alternating right-left pattern. The mechanisms underlying this zig-zag behavior, however, remain unknown. Here we propose a new Excitable Cortex and Memory (EC&M model for understanding the alternating, zig-zag extension of pseudopods. Incorporating elements of previous models, we consider the cell cortex as an excitable system and include global inhibition of new pseudopods while a pseudopod is active. With the novel hypothesis that pseudopod activity makes the local cortex temporarily more excitable--thus creating a memory of previous pseudopod locations--the model reproduces experimentally observed zig-zag behavior. Furthermore, the EC&M model makes four new predictions concerning pseudopod dynamics. To test these predictions we develop an algorithm that detects pseudopods via hierarchical clustering of individual membrane extensions. Data from cell-tracking experiments agrees with all four predictions of the model, revealing that pseudopod placement is a non-Markovian process affected by the dynamics of previous pseudopods. The model is also compatible with known limits of chemotactic sensitivity. In addition to providing a predictive approach to studying eukaryotic cell motion, the EC&M model provides a general framework for future models, and suggests directions for new research regarding the molecular mechanisms underlying directional persistence.
Modelling bankruptcy prediction models in Slovak companies

Directory of Open Access Journals (Sweden)

Kovacova Maria

2017-01-01

Full Text Available An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression and early artificial intelligence models (e.g. artificial neural networks, there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models.
Improvement of NO and CO predictions for a homogeneous combustion SI engine using a novel emissions model

International Nuclear Information System (INIS)

Karvountzis-Kontakiotis, Apostolos; Ntziachristos, Leonidas

2016-01-01

Highlights: • Presentation of a novel emissions model to predict pollutants formation in engines. • Model based on detailed chemistry, requires no application-specific calibration. • Combined with 0D and 1D combustion models with low additional computational cost. • Demonstrates accurate prediction of cyclic variability of pollutants emissions. - Abstract: This study proposes a novel emissions model for the prediction of spark ignition (SI) engine emissions at homogeneous combustion conditions, using post combustion analysis and a detailed chemistry mechanism. The novel emissions model considers an unburned and a burned zone, where the latter is considered as a homogeneous reactor and is modeled using a detailed chemical kinetics mechanism. This allows detailed emission predictions at high speed practically based only on combustion pressure and temperature profiles, without the need for calibration of the model parameters. The predictability of the emissions model is compared against the extended Zeldovich mechanism for NO and a simplified two-step reaction kinetic model for CO, which both constitute the most widespread existing approaches in the literature. Under various engine load and speed conditions examined, the mean error in NO prediction was 28% for the existing models and less than 1.3% for the new model proposed. The novel emissions model was also used to predict emissions variation due to cyclic combustion variability and demonstrated mean prediction error of 6% and 3.6% for NO and CO respectively, compared to 36% (NO) and 67% (CO) for the simplified model. The results show that the emissions model proposed offers substantial improvements in the prediction of the results without significant increase in calculation time.
Rotary ultrasonic machining of CFRP: a mechanistic predictive model for cutting force.

Science.gov (United States)

Cong, W L; Pei, Z J; Sun, X; Zhang, C L

2014-02-01

Cutting force is one of the most important output variables in rotary ultrasonic machining (RUM) of carbon fiber reinforced plastic (CFRP) composites. Many experimental investigations on cutting force in RUM of CFRP have been reported. However, in the literature, there are no cutting force models for RUM of CFRP. This paper develops a mechanistic predictive model for cutting force in RUM of CFRP. The material removal mechanism of CFRP in RUM has been analyzed first. The model is based on the assumption that brittle fracture is the dominant mode of material removal. CFRP micromechanical analysis has been conducted to represent CFRP as an equivalent homogeneous material to obtain the mechanical properties of CFRP from its components. Based on this model, relationships between input variables (including ultrasonic vibration amplitude, tool rotation speed, feedrate, abrasive size, and abrasive concentration) and cutting force can be predicted. The relationships between input variables and important intermediate variables (indentation depth, effective contact time, and maximum impact force of single abrasive grain) have been investigated to explain predicted trends of cutting force. Experiments are conducted to verify the model, and experimental results agree well with predicted trends from this model. Copyright © 2013 Elsevier B.V. All rights reserved.
Mechanics, Models and Methods in Civil Engineering

CERN Document Server

Maceri, Franco

2012-01-01

„Mechanics, Models and Methods in Civil Engineering” collects leading papers dealing with actual Civil Engineering problems. The approach is in the line of the Italian-French school and therefore deeply couples mechanics and mathematics creating new predictive theories, enhancing clarity in understanding, and improving effectiveness in applications. The authors of the contributions collected here belong to the Lagrange Laboratory, an European Research Network active since many years. This book will be of a major interest for the reader aware of modern Civil Engineering.
Continuum Damage Mechanics Models for the Analysis of Progressive Failure in Open-Hole Tension Laminates

Science.gov (United States)

Song, Kyonchan; Li, Yingyong; Rose, Cheryl A.

2011-01-01

The performance of a state-of-the-art continuum damage mechanics model for interlaminar damage, coupled with a cohesive zone model for delamination is examined for failure prediction of quasi-isotropic open-hole tension laminates. Limitations of continuum representations of intra-ply damage and the effect of mesh orientation on the analysis predictions are discussed. It is shown that accurate prediction of matrix crack paths and stress redistribution after cracking requires a mesh aligned with the fiber orientation. Based on these results, an aligned mesh is proposed for analysis of the open-hole tension specimens consisting of different meshes within the individual plies, such that the element edges are aligned with the ply fiber direction. The modeling approach is assessed by comparison of analysis predictions to experimental data for specimen configurations in which failure is dominated by complex interactions between matrix cracks and delaminations. It is shown that the different failure mechanisms observed in the tests are well predicted. In addition, the modeling approach is demonstrated to predict proper trends in the effect of scaling on strength and failure mechanisms of quasi-isotropic open-hole tension laminates.
Statistical models for expert judgement and wear prediction

International Nuclear Information System (INIS)

Pulkkinen, U.

1994-01-01

This thesis studies the statistical analysis of expert judgements and prediction of wear. The point of view adopted is the one of information theory and Bayesian statistics. A general Bayesian framework for analyzing both the expert judgements and wear prediction is presented. Information theoretic interpretations are given for some averaging techniques used in the determination of consensus distributions. Further, information theoretic models are compared with a Bayesian model. The general Bayesian framework is then applied in analyzing expert judgements based on ordinal comparisons. In this context, the value of information lost in the ordinal comparison process is analyzed by applying decision theoretic concepts. As a generalization of the Bayesian framework, stochastic filtering models for wear prediction are formulated. These models utilize the information from condition monitoring measurements in updating the residual life distribution of mechanical components. Finally, the application of stochastic control models in optimizing operational strategies for inspected components are studied. Monte-Carlo simulation methods, such as the Gibbs sampler and the stochastic quasi-gradient method, are applied in the determination of posterior distributions and in the solution of stochastic optimization problems. (orig.) (57 refs., 7 figs., 1 tab.)
Fast analytical model of MZI micro-opto-mechanical pressure sensor

Science.gov (United States)

Rochus, V.; Jansen, R.; Goyvaerts, J.; Neutens, P.; O’Callaghan, J.; Rottenberg, X.

2018-06-01

This paper presents a fast analytical procedure in order to design a micro-opto-mechanical pressure sensor (MOMPS) taking into account the mechanical nonlinearity and the optical losses. A realistic model of the photonic MZI is proposed, strongly coupled to a nonlinear mechanical model of the membrane. Based on the membrane dimensions, the residual stress, the position of the waveguide, the optical wavelength and the phase variation due to the opto-mechanical coupling, we derive an analytical model which allows us to predict the response of the total system. The effect of the nonlinearity and the losses on the total performance are carefully studied and measurements on fabricated devices are used to validate the model. Finally, a design procedure is proposed in order to realize fast design of this new type of pressure sensor.
Modeling the Mechanical Performance of Die Casting Dies

Energy Technology Data Exchange (ETDEWEB)

R. Allen Miller

2004-02-27

The following report covers work performed at Ohio State on modeling the mechanical performance of dies. The focus of the project was development and particularly verification of finite element techniques used to model and predict displacements and stresses in die casting dies. The work entails a major case study performed with and industrial partner on a production die and laboratory experiments performed at Ohio State.
Development and validation of deterioration models for concrete bridge decks - phase 2 : mechanics-based degradation models.

Science.gov (United States)

2013-06-01

This report summarizes a research project aimed at developing degradation models for bridge decks in the state of Michigan based on durability mechanics. A probabilistic framework to implement local-level mechanistic-based models for predicting the c...
Prediction of equibiaxial loading stress in collagen-based extracellular matrix using a three-dimensional unit cell model.

Science.gov (United States)

Susilo, Monica E; Bell, Brett J; Roeder, Blayne A; Voytik-Harbin, Sherry L; Kokini, Klod; Nauman, Eric A

2013-03-01

Mechanical signals are important factors in determining cell fate. Therefore, insights as to how mechanical signals are transferred between the cell and its surrounding three-dimensional collagen fibril network will provide a basis for designing the optimum extracellular matrix (ECM) microenvironment for tissue regeneration. Previously we described a cellular solid model to predict fibril microstructure-mechanical relationships of reconstituted collagen matrices due to unidirectional loads (Acta Biomater 2010;6:1471-86). The model consisted of representative volume elements made up of an interconnected network of flexible struts. The present study extends this work by adapting the model to account for microstructural anisotropy of the collagen fibrils and a biaxial loading environment. The model was calibrated based on uniaxial tensile data and used to predict the equibiaxial tensile stress-stretch relationship. Modifications to the model significantly improved its predictive capacity for equibiaxial loading data. With a comparable fibril length (model 5.9-8μm, measured 7.5μm) and appropriate fibril anisotropy the anisotropic model provides a better representation of the collagen fibril microstructure. Such models are important tools for tissue engineering because they facilitate prediction of microstructure-mechanical relationships for collagen matrices over a wide range of microstructures and provide a framework for predicting cell-ECM interactions. Copyright © 2012 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
Investigation of Mechanical Properties of Unidirectional Steel Fiber/Polyester Composites: Experiments and Micromechanical Predictions

DEFF Research Database (Denmark)

Raghavalu Thirumalai, Durai Prabhakaran; Løgstrup Andersen, Tom; Bech, Jakob Ilsted

2016-01-01

the role of material and process parameters on material properties. Two types of SFRP were studied: polyester resin reinforced by both steel fabric containing unidirectional fibers and steel fibers wound on a metal frame with 0° orientations. The effects of the fiber volume fraction and the role of polymer......The article introduces steel fiber reinforced polymer composites, which is considered new for composite product developments. These composites consist of steel fibers or filaments of 0.21 mm diameter embedded in a polyester resin. The goal of this investigation is to characterize the mechanical...... performance of steel fiber reinforced polyester composites at room temperature. The mechanical properties of unidirectional steel fiber reinforced polyester composites (SFRP) are evaluated experimentally and compared with the predicted values by micro-mechanical models. These predictions help to understand...
Choking flow modeling with mechanical and thermal non-equilibrium

Energy Technology Data Exchange (ETDEWEB)

Yoon, H.J.; Ishii, M.; Revankar, S.T. [School of Nuclear Engineering, Purdue University, West Lafayette, IN 47907 (United States)

2006-01-15

The mechanistic model, which considers the mechanical and thermal non-equilibrium, is described for two-phase choking flow. The choking mass flux is obtained from the momentum equation with the definition of choking. The key parameter for the mechanical non-equilibrium is a slip ratio. The dependent parameters for the slip ratio are identified. In this research, the slip ratio which is defined in the drift flux model is used to identify the impact parameters on the slip ratio. Because the slip ratio in the drift flux model is related to the distribution parameter and drift velocity, the adequate correlations depending on the flow regime are introduced in this study. For the thermal non-equilibrium, the model is developed with bubble conduction time and Bernoulli choking model. In case of highly subcooled water compared to the inlet pressure, the Bernoulli choking model using the pressure undershoot is used because there is no bubble generation in the test section. When the phase change happens inside the test section, two-phase choking model with relaxation time calculates the choking mass flux. According to the comparison of model prediction with experimental data shows good agreement. The developed model shows good prediction in both low and high pressure ranges. (author)
A structural model for the flexural mechanics of nonwoven tissue engineering scaffolds.

Science.gov (United States)

Engelmayr, George C; Sacks, Michael S

2006-08-01

The development of methods to predict the strength and stiffness of biomaterials used in tissue engineering is critical for load-bearing applications in which the essential functional requirements are primarily mechanical. We previously quantified changes in the effective stiffness (E) of needled nonwoven polyglycolic acid (PGA) and poly-L-lactic acid (PLLA) scaffolds due to tissue formation and scaffold degradation under three-point bending. Toward predicting these changes, we present a structural model for E of a needled nonwoven scaffold in flexure. The model accounted for the number and orientation of fibers within a representative volume element of the scaffold demarcated by the needling process. The spring-like effective stiffness of the curved fibers was calculated using the sinusoidal fiber shapes. Structural and mechanical properties of PGA and PLLA fibers and PGA, PLLA, and 50:50 PGA/PLLA scaffolds were measured and compared with model predictions. To verify the general predictive capability, the predicted dependence of E on fiber diameter was compared with experimental measurements. Needled nonwoven scaffolds were found to exhibit distinct preferred (PD) and cross-preferred (XD) fiber directions, with an E ratio (PD/XD) of approximately 3:1. The good agreement between the predicted and experimental dependence of E on fiber diameter (R2 = 0.987) suggests that the structural model can be used to design scaffolds with E values more similar to native soft tissues. A comparison with previous results for cell-seeded scaffolds (Engelmayr, G. C., Jr., et al., 2005, Biomaterials, 26(2), pp. 175-187) suggests, for the first time, that the primary mechanical effect of collagen deposition is an increase in the number of fiber-fiber bond points yielding effectively stiffer scaffold fibers. This finding indicated that the effects of tissue deposition on needled nonwoven scaffold mechanics do not follow a rule-of-mixtures behavior. These important results underscore

Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction.

Science.gov (United States)

Soleimani, Hossein; Hensman, James; Saria, Suchi

2017-08-21

Missing data and noisy observations pose significant challenges for reliably predicting events from irregularly sampled multivariate time series (longitudinal) data. Imputation methods, which are typically used for completing the data prior to event prediction, lack a principled mechanism to account for the uncertainty due to missingness. Alternatively, state-of-the-art joint modeling techniques can be used for jointly modeling the longitudinal and event data and compute event probabilities conditioned on the longitudinal observations. These approaches, however, make strong parametric assumptions and do not easily scale to multivariate signals with many observations. Our proposed approach consists of several key innovations. First, we develop a flexible and scalable joint model based upon sparse multiple-output Gaussian processes. Unlike state-of-the-art joint models, the proposed model can explain highly challenging structure including non-Gaussian noise while scaling to large data. Second, we derive an optimal policy for predicting events using the distribution of the event occurrence estimated by the joint model. The derived policy trades-off the cost of a delayed detection versus incorrect assessments and abstains from making decisions when the estimated event probability does not satisfy the derived confidence criteria. Experiments on a large dataset show that the proposed framework significantly outperforms state-of-the-art techniques in event prediction.
Facial motion engages predictive visual mechanisms.

Directory of Open Access Journals (Sweden)

Jordy Kaufman

Full Text Available We employed a novel cuing paradigm to assess whether dynamically versus statically presented facial expressions differentially engaged predictive visual mechanisms. Participants were presented with a cueing stimulus that was either the static depiction of a low intensity expressed emotion; or a dynamic sequence evolving from a neutral expression to the low intensity expressed emotion. Following this cue and a backwards mask, participants were presented with a probe face that displayed either the same emotion (congruent or a different emotion (incongruent with respect to that displayed by the cue although expressed at a high intensity. The probe face had either the same or different identity from the cued face. The participants' task was to indicate whether or not the probe face showed the same emotion as the cue. Dynamic cues and same identity cues both led to a greater tendency towards congruent responding, although these factors did not interact. Facial motion also led to faster responding when the probe face was emotionally congruent to the cue. We interpret these results as indicating that dynamic facial displays preferentially invoke predictive visual mechanisms, and suggest that motoric simulation may provide an important basis for the generation of predictions in the visual system.
Development of mathematical model to predict the mechanical properties of friction stir

Directory of Open Access Journals (Sweden)

R. Palanivel

2011-01-01

Full Text Available This paper presents a systematic approach to develop the mathematical model for predicting the ultimate tensile strength,yield strength, and percentage of elongation of AA6351 aluminum alloy which is widely used in automotive, aircraft anddefense Industries by incorporating (FSW friction stir welding process parameter such as tool rotational speed, weldingspeed, and axial force. FSW has been carried out based on three factors five level central composite rotatable design withfull replications technique. Response surface methodology (RSM is employed to develop the mathematical model. Analysisof variance (ANOVA Technique is used to check the adequacy of the developed mathematical model. The developedmathematical model can be used effectively at 95% confidence level. The effect of FSW process parameter on mechanicalproperties of AA6351 aluminum alloy has been analyzed in detail.
The Modeling of the Effects of Soiling, Its Mechanisms, and the Corresponding Abrasion

Energy Technology Data Exchange (ETDEWEB)

Simpson, Lin; Muller, Matthew; Deceglie, Michael; Miller, David; Moutinho, Helio

2016-02-24

Decreasing LCOE with predictive soiling loss models (using site data to predict annualized energy loss), quantification of different soiling mechanisms (using AFM-based characterization), and developing standards for PV module coatings.
Boron carbide reinforced aluminium matrix composite: Physical, mechanical characterization and mathematical modelling

International Nuclear Information System (INIS)

Shirvanimoghaddam, K.; Khayyam, H.; Abdizadeh, H.; Karbalaei Akbari, M.; Pakseresht, A.H.; Ghasali, E.; Naebe, M.

2016-01-01

This paper investigates the manufacturing of aluminium–boron carbide composites using the stir casting method. Mechanical and physical properties tests to obtain hardness, ultimate tensile strength (UTS) and density are performed after solidification of specimens. The results show that hardness and tensile strength of aluminium based composite are higher than monolithic metal. Increasing the volume fraction of B_4C, enhances the tensile strength and hardness of the composite; however over-loading of B_4C caused particle agglomeration, rejection from molten metal and migration to slag. This phenomenon decreases the tensile strength and hardness of the aluminium based composite samples cast at 800 °C. For Al-15 vol% B_4C samples, the ultimate tensile strength and Vickers hardness of the samples that were cast at 1000 °C, are the highest among all composites. To predict the mechanical properties of aluminium matrix composites, two key prediction modelling methods including Neural Network learned by Levenberg–Marquardt Algorithm (NN-LMA) and Thin Plate Spline (TPS) models are constructed based on experimental data. Although the results revealed that both mathematical models of mechanical properties of Al–B_4C are reliable with a high level of accuracy, the TPS models predict the hardness and tensile strength values with less error compared to NN-LMA models.
Computational modeling of elastic properties of carbon nanotube/polymer composites with interphase regions. Part II: Mechanical modeling

KAUST Repository

Han, Fei; Azdoud, Yan; Lubineau, Gilles

2014-01-01

We present two modeling approaches for predicting the macroscopic elastic properties of carbon nanotubes/polymer composites with thick interphase regions at the nanotube/matrix frontier. The first model is based on local continuum mechanics
Histologic and biochemical alterations predict pulmonary mechanical dysfunction in aging mice with chronic lung inflammation.

Directory of Open Access Journals (Sweden)

Christopher B Massa

2017-08-01

Full Text Available Both aging and chronic inflammation produce complex structural and biochemical alterations to the lung known to impact work of breathing. Mice deficient in surfactant protein D (Sftpd develop progressive age-related lung pathology characterized by tissue destruction/remodeling, accumulation of foamy macrophages and alteration in surfactant composition. This study proposes to relate changes in tissue structure seen in normal aging and in chronic inflammation to altered lung mechanics using a computational model. Alterations in lung function in aging and Sftpd -/- mice have been inferred from fitting simple mechanical models to respiratory impedance data (Zrs, however interpretation has been confounded by the simultaneous presence of multiple coexisting pathophysiologic processes. In contrast to the inverse modeling approach, this study uses simulation from experimental measurements to recapitulate how aging and inflammation alter Zrs. Histologic and mechanical measurements were made in C57BL6/J mice and congenic Sftpd-/- mice at 8, 27 and 80 weeks of age (n = 8/group. An anatomic computational model based on published airway morphometry was developed and Zrs was simulated between 0.5 and 20 Hz. End expiratory pressure dependent changes in airway caliber and recruitment were estimated from mechanical measurements. Tissue elements were simulated using the constant phase model of viscoelasticity. Baseline elastance distribution was estimated in 8-week-old wild type mice, and stochastically varied for each condition based on experimentally measured alteration in elastic fiber composition, alveolar geometry and surfactant composition. Weighing reduction in model error against increasing model complexity allowed for identification of essential features underlying mechanical pathology and their contribution to Zrs. Using a maximum likelihood approach, alteration in lung recruitment and diminished elastic fiber density were shown predictive of mechanical
Histologic and biochemical alterations predict pulmonary mechanical dysfunction in aging mice with chronic lung inflammation.

Science.gov (United States)

Massa, Christopher B; Groves, Angela M; Jaggernauth, Smita U; Laskin, Debra L; Gow, Andrew J

2017-08-01

Both aging and chronic inflammation produce complex structural and biochemical alterations to the lung known to impact work of breathing. Mice deficient in surfactant protein D (Sftpd) develop progressive age-related lung pathology characterized by tissue destruction/remodeling, accumulation of foamy macrophages and alteration in surfactant composition. This study proposes to relate changes in tissue structure seen in normal aging and in chronic inflammation to altered lung mechanics using a computational model. Alterations in lung function in aging and Sftpd -/- mice have been inferred from fitting simple mechanical models to respiratory impedance data (Zrs), however interpretation has been confounded by the simultaneous presence of multiple coexisting pathophysiologic processes. In contrast to the inverse modeling approach, this study uses simulation from experimental measurements to recapitulate how aging and inflammation alter Zrs. Histologic and mechanical measurements were made in C57BL6/J mice and congenic Sftpd-/- mice at 8, 27 and 80 weeks of age (n = 8/group). An anatomic computational model based on published airway morphometry was developed and Zrs was simulated between 0.5 and 20 Hz. End expiratory pressure dependent changes in airway caliber and recruitment were estimated from mechanical measurements. Tissue elements were simulated using the constant phase model of viscoelasticity. Baseline elastance distribution was estimated in 8-week-old wild type mice, and stochastically varied for each condition based on experimentally measured alteration in elastic fiber composition, alveolar geometry and surfactant composition. Weighing reduction in model error against increasing model complexity allowed for identification of essential features underlying mechanical pathology and their contribution to Zrs. Using a maximum likelihood approach, alteration in lung recruitment and diminished elastic fiber density were shown predictive of mechanical alteration at
Thermomechanical Modeling of Sintered Silver - A Fracture Mechanics-based Approach: Extended Abstract: Preprint

Energy Technology Data Exchange (ETDEWEB)

Paret, Paul P [National Renewable Energy Laboratory (NREL), Golden, CO (United States); DeVoto, Douglas J [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Narumanchi, Sreekant V [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2017-09-01

Sintered silver has proven to be a promising candidate for use as a die-attach and substrate-attach material in automotive power electronics components. It holds promise of greater reliability than lead-based and lead-free solders, especially at higher temperatures (less than 200 degrees Celcius). Accurate predictive lifetime models of sintered silver need to be developed and its failure mechanisms thoroughly characterized before it can be deployed as a die-attach or substrate-attach material in wide-bandgap device-based packages. We present a finite element method (FEM) modeling methodology that can offer greater accuracy in predicting the failure of sintered silver under accelerated thermal cycling. A fracture mechanics-based approach is adopted in the FEM model, and J-integral/thermal cycle values are computed. In this paper, we outline the procedures for obtaining the J-integral/thermal cycle values in a computational model and report on the possible advantage of using these values as modeling parameters in a predictive lifetime model.
Thermal-mechanical deformation modelling of soft tissues for thermal ablation.

Science.gov (United States)

Li, Xin; Zhong, Yongmin; Jazar, Reza; Subic, Aleksandar

2014-01-01

Modeling of thermal-induced mechanical behaviors of soft tissues is of great importance for thermal ablation. This paper presents a method by integrating the heating process with thermal-induced mechanical deformations of soft tissues for simulation and analysis of the thermal ablation process. This method combines bio-heat transfer theories, constitutive elastic material law under thermal loads as well as non-rigid motion dynamics to predict and analyze thermal-mechanical deformations of soft tissues. The 3D governing equations of thermal-mechanical soft tissue deformation are discretized by using the finite difference scheme and are subsequently solved by numerical algorithms. Experimental results show that the proposed method can effectively predict the thermal-induced mechanical behaviors of soft tissues, and can be used for the thermal ablation therapy to effectively control the delivered heat energy for cancer treatment.
Aespoe Pillar Stability Experiment. Summary of preparatory work and predictive modelling

International Nuclear Information System (INIS)

Andersson, J. Christer

2004-11-01

The Aespoe Pillar Stability Experiment, APSE, is a large scale rock mechanics experiment for research of the spalling process and the possibility for numerical modelling of it. The experiment can be summarized in three objectives: Demonstrate the current capability to predict spalling in a fractured rock mass; Demonstrate the effect of backfill (confining pressure) on the rock mass response; and Comparison of 2D and 3D mechanical and thermal predicting capabilities. This report is a summary of the works that has been performed in the experiment prior to the heating of the rock mass. The major activities that have been performed and are discussed herein are: 1) The geology of the experiment drift in general and the experiment volume in particular. 2) The design process of the experiment and thoughts behind some of the important decisions. 3) The monitoring programme and the supporting constructions for the instruments. 4) The numerical modelling, approaches taken and a summary of the predictions. In the end of the report there is a comparison of the results from the different models. Included is also a comparison of the time needed for building, realizing and make changes in the different models
Computationally efficient thermal-mechanical modelling of selective laser melting

Science.gov (United States)

Yang, Yabin; Ayas, Can

2017-10-01

The Selective laser melting (SLM) is a powder based additive manufacturing (AM) method to produce high density metal parts with complex topology. However, part distortions and accompanying residual stresses deteriorates the mechanical reliability of SLM products. Modelling of the SLM process is anticipated to be instrumental for understanding and predicting the development of residual stress field during the build process. However, SLM process modelling requires determination of the heat transients within the part being built which is coupled to a mechanical boundary value problem to calculate displacement and residual stress fields. Thermal models associated with SLM are typically complex and computationally demanding. In this paper, we present a simple semi-analytical thermal-mechanical model, developed for SLM that represents the effect of laser scanning vectors with line heat sources. The temperature field within the part being build is attained by superposition of temperature field associated with line heat sources in a semi-infinite medium and a complimentary temperature field which accounts for the actual boundary conditions. An analytical solution of a line heat source in a semi-infinite medium is first described followed by the numerical procedure used for finding the complimentary temperature field. This analytical description of the line heat sources is able to capture the steep temperature gradients in the vicinity of the laser spot which is typically tens of micrometers. In turn, semi-analytical thermal model allows for having a relatively coarse discretisation of the complimentary temperature field. The temperature history determined is used to calculate the thermal strain induced on the SLM part. Finally, a mechanical model governed by elastic-plastic constitutive rule having isotropic hardening is used to predict the residual stresses.
A highly predictive A 4 flavor 3-3-1 model with radiative inverse seesaw mechanism

Science.gov (United States)

Cárcamo Hernández, A. E.; Long, H. N.

2018-04-01

We build a highly predictive 3-3-1 model, where the field content is extended by including several SU(3) L scalar singlets and six right handed Majorana neutrinos. In our model the {SU}{(3)}C× {SU}{(3)}L× U{(1)}X gauge symmetry is supplemented by the {A}4× {Z}4× {Z}6× {Z}16× {Z}16{\\prime } discrete group, which allows to get a very good description of the low energy fermion flavor data. In the model under consideration, the {A}4× {Z}4× {Z}6× {Z}16× {Z}16{\\prime } discrete group is broken at very high energy scale down to the preserved Z 2 discrete symmetry, thus generating the observed pattern of SM fermion masses and mixing angles and allowing the implementation of the loop level inverse seesaw mechanism for the generation of the light active neutrino masses, respectively. The obtained values for the physical observables in the quark sector agree with the experimental data, whereas those ones for the lepton sector also do, only for the case of inverted neutrino mass spectrum. The normal neutrino mass hierarchy scenario of the model is ruled out by the neutrino oscillation experimental data. We find an effective Majorana neutrino mass parameter of neutrinoless double beta decay of m ee = 46.9 meV, a leptonic Dirac CP violating phase of -81.37° and a Jarlskog invariant of about 10-2 for the inverted neutrino mass hierarchy. The preserved Z 2 symmetry allows for a stable scalar dark matter candidate.
An utilization of liquid sublayer dryout mechanism in predicting critical heat flux under low pressure and low velocity conditions in round tubes

International Nuclear Information System (INIS)

Lee, Kwang-Won; Baik, Se-Jin; Ro, Tae-Sun

2000-01-01

From a theoretical assessment of extensive critical heat flux (CHF) data under low pressure and low velocity (LPLV) conditions, it was found out that lots of CHF data would not be well predicted by a normal annular film dryout (AFD) mechanism, although their flow patterns were identified as annular-mist flow. To predict these CHF data, a liquid sublayer dryout (LSD) mechanism has been newly utilized in developing the mechanistic CHF model based on each identified CHF mechanism. This mechanism postulates that the CHF occurrence is caused by dryout of the thin liquid sublayer resulting from the annular film separation or breaking down due to nucleate boiling in annular film or hydrodynamic fluctuation. In principle, this mechanism well supports the experimental evidence of residual film flow rate at the CHF location, which can not be explained by the AFD mechanism. For a comparative assessment of each mechanism, the CHF model based on the LSD mechanism is developed together with that based on the AFD mechanism. The validation of these models is performed on the 1406 CHF data points ranging over P=0.1-2 MPa, G=4-499 kg m -2 s -1 , L/D=4-402. This model validation shows that 1055 and 231 CHF data are predicted within ±30 error bound by the LSD mechanism and the AFD mechanism, respectively. However, some CHF data whose critical qualities are <0.4 or whose tube length-to-diameter ratios are <70 are considerably overestimated by the CHF model based on the LSD mechanism. These overestimations seem to be caused by an inadequate CHF mechanism classification and an insufficient consideration of the flow instability effect on CHF. Further studies for a new classification criterion screening the CHF data affected by flow instabilities as well as a new bubble detachment model for LPLV conditions, are needed to improve the model accuracy.
Predictive modeling of complications.

Science.gov (United States)

Osorio, Joseph A; Scheer, Justin K; Ames, Christopher P

2016-09-01

Predictive analytic algorithms are designed to identify patterns in the data that allow for accurate predictions without the need for a hypothesis. Therefore, predictive modeling can provide detailed and patient-specific information that can be readily applied when discussing the risks of surgery with a patient. There are few studies using predictive modeling techniques in the adult spine surgery literature. These types of studies represent the beginning of the use of predictive analytics in spine surgery outcomes. We will discuss the advancements in the field of spine surgery with respect to predictive analytics, the controversies surrounding the technique, and the future directions.
An analytical model of the mechanical properties of bulk coal under confined stress

Science.gov (United States)

Wang, G.X.; Wang, Z.T.; Rudolph, V.; Massarotto, P.; Finley, R.J.

2007-01-01

This paper presents the development of an analytical model which can be used to relate the structural parameters of coal to its mechanical properties such as elastic modulus and Poisson's ratio under a confined stress condition. This model is developed primarily to support process modeling of coalbed methane (CBM) or CO2-enhanced CBM (ECBM) recovery from coal seam. It applied an innovative approach by which stresses acting on and strains occurring in coal are successively combined in rectangular coordinates, leading to the aggregated mechanical constants. These mechanical properties represent important information for improving CBM/ECBM simulations and incorporating within these considerations of directional permeability. The model, consisting of constitutive equations which implement a mechanically consistent stress-strains correlation, can be used as a generalized tool to study the mechanical and fluid behaviors of coal composites. An example using the model to predict the stress-strain correlation of coal under triaxial confined stress by accounting for the elastic and brittle (non-elastic) deformations is discussed. The result shows a good agreement between the prediction and the experimental measurement. ?? 2007 Elsevier Ltd. All rights reserved.
An Experimental Study on Mechanical Modeling of Ceramics Based on Microstructure

Directory of Open Access Journals (Sweden)

Ya-Nan Zhang

2015-11-01

Full Text Available The actual grinding result of ceramics has not been well predicted by the present mechanical models. No allowance is made for direct effects of materials microstructure and almost all the mechanical models were obtained based on crystalline ceramics. In order to improve the mechanical models of ceramics, surface grinding experiments on crystalline ceramics and non-crystalline ceramics were conducted in this research. The normal and tangential grinding forces were measured to calculate single grit force and specific grinding energy. Grinding surfaces were observed. For crystalline alumina ceramics, the predictive modeling of normal force per grit fits well with the experimental result, when the maximum undeformed chip thickness is less than a critical depth, which turns out to be close to the grain size of alumina. Meanwhile, there is a negative correlation between the specific grinding energy and the maximum undeformed chip thickness. With the decreasing maximum undeformed chip thickness, the proportions of ductile removal and transgranular fracture increase. However, the grinding force models are not applicable for non-crystalline ceramic fused silica and the specific grinding energy fluctuates irregularly as a function of maximum undeformed chip thickness seen from the experiment.
Properties, Mechanisms and Predictability of Eddies in the Red Sea

KAUST Repository

Zhan, Peng

2018-04-01

Eddies are one of the key features of the Red Sea circulation. They are not only crucial for energy conversion among dynamics at diﬀerent scales, but also for materials transport across the basin. This thesis focuses on studying the characteristics of Red Sea eddies, including their temporal and spatial properties, their energy budget, the mechanisms of their evolution, and their predictability. Remote sensing data, in-situ observations, the oceanic general circulation model, and data assimilation techniques were employed in this thesis. The eddies in the Red Sea were ﬁrst identiﬁed using altimeter data by applying an improved winding-angle method, based on which the statistical properties of those eddies were derived. The results suggested that eddies occur more frequently in the central basin of the Red Sea and exhibit a signiﬁcant seasonal variation. The mechanisms of the eddies’ evolution, particularly the eddy kinetic energy budget, were then investigated based on the outputs of a long-term eddy resolving numerical model conﬁgured for the Red Sea with realistic forcing. Examination of the energy budget revealed that the eddies acquire the vast majority of kinetic energy through conversion of eddy available potential energy via baroclinic instability, which is intensiﬁed during winter. The possible factors modulating the behavior of the several observed eddies in the Red Sea were then revealed by conducting a sensitivity analysis using the adjoint model. These eddies were found to exhibit diﬀerent sensitivities to external forcings, suggesting diﬀerent mechanisms for their evolution. This is the ﬁrst known adjoint sensitivity study on speciﬁc eddy events in the Red Sea and was hitherto not previously appreciated. The last chapter examines the predictability of Red Sea eddies using an ensemble-based forecasting and assimilation system. The forecast sea surface height was used to evaluate the overall performance of the short-term eddy
Experimental approach and micro-mechanical modeling of the mechanical behavior of irradiated zirconium alloys

International Nuclear Information System (INIS)

Onimus, F.

2003-12-01

Zirconium alloys cladding tubes containing nuclear fuel of the Pressurized Water Reactors constitute the first safety barrier against the dissemination of radioactive elements. Thus, it is essential to predict the mechanical behavior of the material in-reactor conditions. This study aims, on the one hand, to identify and characterize the mechanisms of the plastic deformation of irradiated zirconium alloys and, on the other hand, to propose a micro-mechanical modeling based on these mechanisms. The experimental analysis shows that, for the irradiated material, the plastic deformation occurs by dislocation channeling. For transverse tensile test and internal pressure test this channeling occurs in the basal planes. However, for axial tensile test, the study revealed that the plastic deformation also occurs by channeling but in the prismatic and pyramidal planes. In addition, the study of the macroscopic mechanical behavior, compared to the deformation mechanisms observed by TEM, suggested that the internal stress is higher in the case of irradiated material than in the case of non-irradiated material, because of the very heterogeneous character of the plastic deformation. This analysis led to a coherent interpretation of the mechanical behavior of irradiated materials, in terms of deformation mechanisms. The mechanical behavior of irradiated materials was finally modeled by applying homogenization methods for heterogeneous materials. This model is able to reproduce adequately the mechanical behavior of the irradiated material, in agreement with the TEM observations. (author)
Predictive model identifies key network regulators of cardiomyocyte mechano-signaling.

Directory of Open Access Journals (Sweden)

Philip M Tan

2017-11-01

Full Text Available Mechanical strain is a potent stimulus for growth and remodeling in cells. Although many pathways have been implicated in stretch-induced remodeling, the control structures by which signals from distinct mechano-sensors are integrated to modulate hypertrophy and gene expression in cardiomyocytes remain unclear. Here, we constructed and validated a predictive computational model of the cardiac mechano-signaling network in order to elucidate the mechanisms underlying signal integration. The model identifies calcium, actin, Ras, Raf1, PI3K, and JAK as key regulators of cardiac mechano-signaling and characterizes crosstalk logic imparting differential control of transcription by AT1R, integrins, and calcium channels. We find that while these regulators maintain mostly independent control over distinct groups of transcription factors, synergy between multiple pathways is necessary to activate all the transcription factors necessary for gene transcription and hypertrophy. We also identify a PKG-dependent mechanism by which valsartan/sacubitril, a combination drug recently approved for treating heart failure, inhibits stretch-induced hypertrophy, and predict further efficacious pairs of drug targets in the network through a network-wide combinatorial search.

Mathematical modelling methodologies in predictive food microbiology: a SWOT analysis.

Science.gov (United States)

Ferrer, Jordi; Prats, Clara; López, Daniel; Vives-Rego, Josep

2009-08-31

Predictive microbiology is the area of food microbiology that attempts to forecast the quantitative evolution of microbial populations over time. This is achieved to a great extent through models that include the mechanisms governing population dynamics. Traditionally, the models used in predictive microbiology are whole-system continuous models that describe population dynamics by means of equations applied to extensive or averaged variables of the whole system. Many existing models can be classified by specific criteria. We can distinguish between survival and growth models by seeing whether they tackle mortality or cell duplication. We can distinguish between empirical (phenomenological) models, which mathematically describe specific behaviour, and theoretical (mechanistic) models with a biological basis, which search for the underlying mechanisms driving already observed phenomena. We can also distinguish between primary, secondary and tertiary models, by examining their treatment of the effects of external factors and constraints on the microbial community. Recently, the use of spatially explicit Individual-based Models (IbMs) has spread through predictive microbiology, due to the current technological capacity of performing measurements on single individual cells and thanks to the consolidation of computational modelling. Spatially explicit IbMs are bottom-up approaches to microbial communities that build bridges between the description of micro-organisms at the cell level and macroscopic observations at the population level. They provide greater insight into the mesoscale phenomena that link unicellular and population levels. Every model is built in response to a particular question and with different aims. Even so, in this research we conducted a SWOT (Strength, Weaknesses, Opportunities and Threats) analysis of the different approaches (population continuous modelling and Individual-based Modelling), which we hope will be helpful for current and future
Using molecular mechanics to predict bulk material properties of fibronectin fibers.

Directory of Open Access Journals (Sweden)

Mark J Bradshaw

Full Text Available The structural proteins of the extracellular matrix (ECM form fibers with finely tuned mechanical properties matched to the time scales of cell traction forces. Several proteins such as fibronectin (Fn and fibrin undergo molecular conformational changes that extend the proteins and are believed to be a major contributor to the extensibility of bulk fibers. The dynamics of these conformational changes have been thoroughly explored since the advent of single molecule force spectroscopy and molecular dynamics simulations but remarkably, these data have not been rigorously applied to the understanding of the time dependent mechanics of bulk ECM fibers. Using measurements of protein density within fibers, we have examined the influence of dynamic molecular conformational changes and the intermolecular arrangement of Fn within fibers on the bulk mechanical properties of Fn fibers. Fibers were simulated as molecular strands with architectures that promote either equal or disparate molecular loading under conditions of constant extension rate. Measurements of protein concentration within micron scale fibers using deep ultraviolet transmission microscopy allowed the simulations to be scaled appropriately for comparison to in vitro measurements of fiber mechanics as well as providing estimates of fiber porosity and water content, suggesting Fn fibers are approximately 75% solute. Comparing the properties predicted by single molecule measurements to in vitro measurements of Fn fibers showed that domain unfolding is sufficient to predict the high extensibility and nonlinear stiffness of Fn fibers with surprising accuracy, with disparately loaded fibers providing the best fit to experiment. This work shows the promise of this microstructural modeling approach for understanding Fn fiber properties, which is generally applicable to other ECM fibers, and could be further expanded to tissue scale by incorporating these simulated fibers into three dimensional
Numerical modelling of ductile damage mechanics coupled with an unconventional plasticity model

Directory of Open Access Journals (Sweden)

R. Fincato

2016-10-01

Full Text Available Ductility in metals includes the material’s capability to tolerate plastic deformations before partial or total degradation of its mechanical properties. Modelling this parameter is important in structure and component design because it can be used to estimate material failure under a generic multi-axial stress state. Previous work has attempted to provide accurate descriptions of the mechanical property degradation resulting from the formation, growth, and coalescence of microvoids in the medium. Experimentally, ductile damage is inherently linked with the accumulation of plastic strain; therefore, coupling damage and elastoplasticity is necessary for describing this phenomenon accurately. In this paper, we combine the approach proposed by Lemaitre with the features of an unconventional plasticity model, the extended subloading surface model, to predict material fatigue even for loading conditions below the yield stress
Computational implementation of the multi-mechanism deformation coupled fracture model for salt

International Nuclear Information System (INIS)

Koteras, J.R.; Munson, D.E.

1996-01-01

The Multi-Mechanism Deformation (M-D) model for creep in rock salt has been used in three-dimensional computations for the Waste Isolation Pilot Plant (WIPP), a potential waste, repository. These computational studies are relied upon to make key predictions about long-term behavior of the repository. Recently, the M-D model was extended to include creep-induced damage. The extended model, the Multi-Mechanism Deformation Coupled Fracture (MDCF) model, is considerably more complicated than the M-D model and required a different technology from that of the M-D model for a computational implementation
Relating Cohesive Zone Model to Linear Elastic Fracture Mechanics

Science.gov (United States)

Wang, John T.

2010-01-01

The conditions required for a cohesive zone model (CZM) to predict a failure load of a cracked structure similar to that obtained by a linear elastic fracture mechanics (LEFM) analysis are investigated in this paper. This study clarifies why many different phenomenological cohesive laws can produce similar fracture predictions. Analytical results for five cohesive zone models are obtained, using five different cohesive laws that have the same cohesive work rate (CWR-area under the traction-separation curve) but different maximum tractions. The effect of the maximum traction on the predicted cohesive zone length and the remote applied load at fracture is presented. Similar to the small scale yielding condition for an LEFM analysis to be valid. the cohesive zone length also needs to be much smaller than the crack length. This is a necessary condition for a CZM to obtain a fracture prediction equivalent to an LEFM result.
Multi-fidelity machine learning models for accurate bandgap predictions of solids

International Nuclear Information System (INIS)

Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

2016-01-01

Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelity quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.
Modelling the Size Effects on the Mechanical Properties of Micro/Nano Structures

Directory of Open Access Journals (Sweden)

Amir Musa Abazari

2015-11-01

Full Text Available Experiments on micro- and nano-mechanical systems (M/NEMS have shown that their behavior under bending loads departs in many cases from the classical predictions using Euler-Bernoulli theory and Hooke’s law. This anomalous response has usually been seen as a dependence of the material properties on the size of the structure, in particular thickness. A theoretical model that allows for quantitative understanding and prediction of this size effect is important for the design of M/NEMS. In this paper, we summarize and analyze the five theories that can be found in the literature: Grain Boundary Theory (GBT, Surface Stress Theory (SST, Residual Stress Theory (RST, Couple Stress Theory (CST and Surface Elasticity Theory (SET. By comparing these theories with experimental data we propose a simplified model combination of CST and SET that properly fits all considered cases, therefore delivering a simple (two parameters model that can be used to predict the mechanical properties at the nanoscale.
Predictive thermodynamic models for liquid--liquid extraction of single, binary and ternary lanthanides and actinides

International Nuclear Information System (INIS)

Hoh, Y.C.

1977-03-01

Chemically based thermodynamic models to predict the distribution coefficients and the separation factors for the liquid--liquid extraction of lanthanides-organophosphorus compounds were developed by assuming that the quotient of the activity coefficients of each species varies slightly with its concentrations, by using aqueous lanthanide or actinide complexes stoichiometric stability constants expressed as its degrees of formation, by making use of the extraction mechanism and the equilibrium constant for the extraction reaction. For a single component system, the thermodynamic model equations which predict the distribution coefficients, are dependent on the free organic concentration, the equilibrated ligand and hydrogen ion concentrations, the degree of formation, and on the extraction mechanism. For a binary component system, the thermodynamic model equation which predicts the separation factors is the same for all cases. This model equation is dependent on the degrees of formation of each species in their binary system and can be used in a ternary component system to predict the separation factors for the solutes relative to each other
Candidate Prediction Models and Methods

DEFF Research Database (Denmark)

Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik

2005-01-01

This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...... the possibilities w.r.t. different numerical weather predictions actually available to the project....
Resource-estimation models and predicted discovery

International Nuclear Information System (INIS)

Hill, G.W.

1982-01-01

Resources have been estimated by predictive extrapolation from past discovery experience, by analogy with better explored regions, or by inference from evidence of depletion of targets for exploration. Changes in technology and new insights into geological mechanisms have occurred sufficiently often in the long run to form part of the pattern of mature discovery experience. The criterion, that a meaningful resource estimate needs an objective measure of its precision or degree of uncertainty, excludes 'estimates' based solely on expert opinion. This is illustrated by development of error measures for several persuasive models of discovery and production of oil and gas in USA, both annually and in terms of increasing exploration effort. Appropriate generalizations of the models resolve many points of controversy. This is illustrated using two USA data sets describing discovery of oil and of U 3 O 8 ; the latter set highlights an inadequacy of available official data. Review of the oil-discovery data set provides a warrant for adjusting the time-series prediction to a higher resource figure for USA petroleum. (author)
A Long-Term Prediction Model of Beijing Haze Episodes Using Time Series Analysis

Directory of Open Access Journals (Sweden)

Xiaoping Yang

2016-01-01

Full Text Available The rapid industrial development has led to the intermittent outbreak of pm2.5 or haze in developing countries, which has brought about great environmental issues, especially in big cities such as Beijing and New Delhi. We investigated the factors and mechanisms of haze change and present a long-term prediction model of Beijing haze episodes using time series analysis. We construct a dynamic structural measurement model of daily haze increment and reduce the model to a vector autoregressive model. Typical case studies on 886 continuous days indicate that our model performs very well on next day’s Air Quality Index (AQI prediction, and in severely polluted cases (AQI ≥ 300 the accuracy rate of AQI prediction even reaches up to 87.8%. The experiment of one-week prediction shows that our model has excellent sensitivity when a sudden haze burst or dissipation happens, which results in good long-term stability on the accuracy of the next 3–7 days’ AQI prediction.
Wind power prediction models

Science.gov (United States)

Levy, R.; Mcginness, H.

1976-01-01

Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.
Inverse and Predictive Modeling

Energy Technology Data Exchange (ETDEWEB)

Syracuse, Ellen Marie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-09-27

The LANL Seismo-Acoustic team has a strong capability in developing data-driven models that accurately predict a variety of observations. These models range from the simple – one-dimensional models that are constrained by a single dataset and can be used for quick and efficient predictions – to the complex – multidimensional models that are constrained by several types of data and result in more accurate predictions. Team members typically build models of geophysical characteristics of Earth and source distributions at scales of 1 to 1000s of km, the techniques used are applicable for other types of physical characteristics at an even greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.
Archaeological predictive model set.

Science.gov (United States)

2015-03-01

This report is the documentation for Task 7 of the Statewide Archaeological Predictive Model Set. The goal of this project is to : develop a set of statewide predictive models to assist the planning of transportation projects. PennDOT is developing t...
Climate Modeling and Causal Identification for Sea Ice Predictability

Energy Technology Data Exchange (ETDEWEB)

Hunke, Elizabeth Clare [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Urrego Blanco, Jorge Rolando [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Urban, Nathan Mark [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2018-02-12

This project aims to better understand causes of ongoing changes in the Arctic climate system, particularly as decreasing sea ice trends have been observed in recent decades and are expected to continue in the future. As part of the Sea Ice Prediction Network, a multi-agency effort to improve sea ice prediction products on seasonal-to-interannual time scales, our team is studying sensitivity of sea ice to a collection of physical process and feedback mechanism in the coupled climate system. During 2017 we completed a set of climate model simulations using the fully coupled ACME-HiLAT model. The simulations consisted of experiments in which cloud, sea ice, and air-ocean turbulent exchange parameters previously identified as important for driving output uncertainty in climate models were perturbed to account for parameter uncertainty in simulated climate variables. We conducted a sensitivity study to these parameters, which built upon a previous study we made for standalone simulations (Urrego-Blanco et al., 2016, 2017). Using the results from the ensemble of coupled simulations, we are examining robust relationships between climate variables that emerge across the experiments. We are also using causal discovery techniques to identify interaction pathways among climate variables which can help identify physical mechanisms and provide guidance in predictability studies. This work further builds on and leverages the large ensemble of standalone sea ice simulations produced in our previous w14_seaice project.
Relative sensitivity analysis of the predictive properties of sloppy models.

Science.gov (United States)

Myasnikova, Ekaterina; Spirov, Alexander

2018-01-25

Commonly among the model parameters characterizing complex biological systems are those that do not significantly influence the quality of the fit to experimental data, so-called "sloppy" parameters. The sloppiness can be mathematically expressed through saturating response functions (Hill's, sigmoid) thereby embodying biological mechanisms responsible for the system robustness to external perturbations. However, if a sloppy model is used for the prediction of the system behavior at the altered input (e.g. knock out mutations, natural expression variability), it may demonstrate the poor predictive power due to the ambiguity in the parameter estimates. We introduce a method of the predictive power evaluation under the parameter estimation uncertainty, Relative Sensitivity Analysis. The prediction problem is addressed in the context of gene circuit models describing the dynamics of segmentation gene expression in Drosophila embryo. Gene regulation in these models is introduced by a saturating sigmoid function of the concentrations of the regulatory gene products. We show how our approach can be applied to characterize the essential difference between the sensitivity properties of robust and non-robust solutions and select among the existing solutions those providing the correct system behavior at any reasonable input. In general, the method allows to uncover the sources of incorrect predictions and proposes the way to overcome the estimation uncertainties.
Formability prediction for AHSS materials using damage models

Science.gov (United States)

Amaral, R.; Santos, Abel D.; José, César de Sá; Miranda, Sara

2017-05-01

Advanced high strength steels (AHSS) are seeing an increased use, mostly due to lightweight design in automobile industry and strict regulations on safety and greenhouse gases emissions. However, the use of these materials, characterized by a high strength to weight ratio, stiffness and high work hardening at early stages of plastic deformation, have imposed many challenges in sheet metal industry, mainly their low formability and different behaviour, when compared to traditional steels, which may represent a defying task, both to obtain a successful component and also when using numerical simulation to predict material behaviour and its fracture limits. Although numerical prediction of critical strains in sheet metal forming processes is still very often based on the classic forming limit diagrams, alternative approaches can use damage models, which are based on stress states to predict failure during the forming process and they can be classified as empirical, physics based and phenomenological models. In the present paper a comparative analysis of different ductile damage models is carried out, in order numerically evaluate two isotropic coupled damage models proposed by Johnson-Cook and Gurson-Tvergaard-Needleman (GTN), each of them corresponding to the first two previous group classification. Finite element analysis is used considering these damage mechanics approaches and the obtained results are compared with experimental Nakajima tests, thus being possible to evaluate and validate the ability to predict damage and formability limits for previous defined approaches.
Formability prediction for AHSS materials using damage models

International Nuclear Information System (INIS)

Amaral, R.; Miranda, Sara; Santos, Abel D.; José, César de Sá

2017-01-01

Advanced high strength steels (AHSS) are seeing an increased use, mostly due to lightweight design in automobile industry and strict regulations on safety and greenhouse gases emissions. However, the use of these materials, characterized by a high strength to weight ratio, stiffness and high work hardening at early stages of plastic deformation, have imposed many challenges in sheet metal industry, mainly their low formability and different behaviour, when compared to traditional steels, which may represent a defying task, both to obtain a successful component and also when using numerical simulation to predict material behaviour and its fracture limits. Although numerical prediction of critical strains in sheet metal forming processes is still very often based on the classic forming limit diagrams, alternative approaches can use damage models, which are based on stress states to predict failure during the forming process and they can be classified as empirical, physics based and phenomenological models. In the present paper a comparative analysis of different ductile damage models is carried out, in order numerically evaluate two isotropic coupled damage models proposed by Johnson-Cook and Gurson-Tvergaard-Needleman (GTN), each of them corresponding to the first two previous group classification. Finite element analysis is used considering these damage mechanics approaches and the obtained results are compared with experimental Nakajima tests, thus being possible to evaluate and validate the ability to predict damage and formability limits for previous defined approaches. (paper)
Predictive Modeling in Actinide Chemistry and Catalysis

Energy Technology Data Exchange (ETDEWEB)

Yang, Ping [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2016-05-16

These are slides from a presentation on predictive modeling in actinide chemistry and catalysis. The following topics are covered in these slides: Structures, bonding, and reactivity (bonding can be quantified by optical probes and theory, and electronic structures and reaction mechanisms of actinide complexes); Magnetic resonance properties (transition metal catalysts with multi-nuclear centers, and NMR/EPR parameters); Moving to more complex systems (surface chemistry of nanomaterials, and interactions of ligands with nanoparticles); Path forward and conclusions.
Predictive Multiscale Modeling of Nanocellulose Based Materials and Systems

International Nuclear Information System (INIS)

Kovalenko, Andriy

2014-01-01

Cellulose Nanocrysals (CNC) is a renewable biodegradable biopolymer with outstanding mechanical properties made from highly abundant natural source, and therefore is very attractive as reinforcing additive to replace petroleum-based plastics in biocomposite materials, foams, and gels. Large-scale applications of CNC are currently limited due to its low solubility in non-polar organic solvents used in existing polymerization technologies. The solvation properties of CNC can be improved by chemical modification of its surface. Development of effective surface modifications has been rather slow because extensive chemical modifications destabilize the hydrogen bonding network of cellulose and deteriorate the mechanical properties of CNC. We employ predictive multiscale theory, modeling, and simulation to gain a fundamental insight into the effect of CNC surface modifications on hydrogen bonding, CNC crystallinity, solvation thermodynamics, and CNC compatibilization with the existing polymerization technologies, so as to rationally design green nanomaterials with improved solubility in non-polar solvents, controlled liquid crystal ordering and optimized extrusion properties. An essential part of this multiscale modeling approach is the statistical- mechanical 3D-RISM-KH molecular theory of solvation, coupled with quantum mechanics, molecular mechanics, and multistep molecular dynamics simulation. The 3D-RISM-KH theory provides predictive modeling of both polar and non-polar solvents, solvent mixtures, and electrolyte solutions in a wide range of concentrations and thermodynamic states. It properly accounts for effective interactions in solution such as steric effects, hydrophobicity and hydrophilicity, hydrogen bonding, salt bridges, buffer, co-solvent, and successfully predicts solvation effects and processes in bulk liquids, solvation layers at solid surface, and in pockets and other inner spaces of macromolecules and supramolecular assemblies. This methodology

Predictive Multiscale Modeling of Nanocellulose Based Materials and Systems

Science.gov (United States)

Kovalenko, Andriy

2014-08-01

Cellulose Nanocrysals (CNC) is a renewable biodegradable biopolymer with outstanding mechanical properties made from highly abundant natural source, and therefore is very attractive as reinforcing additive to replace petroleum-based plastics in biocomposite materials, foams, and gels. Large-scale applications of CNC are currently limited due to its low solubility in non-polar organic solvents used in existing polymerization technologies. The solvation properties of CNC can be improved by chemical modification of its surface. Development of effective surface modifications has been rather slow because extensive chemical modifications destabilize the hydrogen bonding network of cellulose and deteriorate the mechanical properties of CNC. We employ predictive multiscale theory, modeling, and simulation to gain a fundamental insight into the effect of CNC surface modifications on hydrogen bonding, CNC crystallinity, solvation thermodynamics, and CNC compatibilization with the existing polymerization technologies, so as to rationally design green nanomaterials with improved solubility in non-polar solvents, controlled liquid crystal ordering and optimized extrusion properties. An essential part of this multiscale modeling approach is the statistical- mechanical 3D-RISM-KH molecular theory of solvation, coupled with quantum mechanics, molecular mechanics, and multistep molecular dynamics simulation. The 3D-RISM-KH theory provides predictive modeling of both polar and non-polar solvents, solvent mixtures, and electrolyte solutions in a wide range of concentrations and thermodynamic states. It properly accounts for effective interactions in solution such as steric effects, hydrophobicity and hydrophilicity, hydrogen bonding, salt bridges, buffer, co-solvent, and successfully predicts solvation effects and processes in bulk liquids, solvation layers at solid surface, and in pockets and other inner spaces of macromolecules and supramolecular assemblies. This methodology
Mechanical Models of the Dynamics of Vitreous Substitutes

Directory of Open Access Journals (Sweden)

Krystyna Isakova

2014-01-01

Full Text Available We discuss some aspects of the fluid dynamics of vitreous substitutes in the vitreous chamber, focussing on the flow induced by rotations of the eye bulb. We use simple, yet not trivial, theoretical models to highlight mechanical concepts that are relevant to understand the dynamics of vitreous substitutes and also to identify ideal properties for vitreous replacement fluids. We first recall results by previous authors, showing that the maximum shear stress on the retina grows with increasing viscosity of the fluid up to a saturation value. We then investigate how the wall shear stress changes if a thin layer of aqueous humour is present in the vitreous chamber, separating the retina from the vitreous replacement fluid. The theoretical predictions show that the existence of a thin layer of aqueous is sufficient to substantially decrease the shear stress on the retina. We finally discuss a theoretical model that predicts the stability conditions of the interface between the aqueous and a vitreous substitute. We discuss the implications of this model to understand the mechanisms leading to the formation of emulsion in the vitreous chamber, showing that instability of the interface is possible in a range of parameters relevant for the human eye.
Modeling of two-phase flow with thermal and mechanical non-equilibrium

International Nuclear Information System (INIS)

Houdayer, G.; Pinet, B.; Le Coq, G.; Reocreux, M.; Rousseau, J.C.

1977-01-01

To improve two-phase flow modeling by taking into account thermal and mechanical non-equilibrium a joint effort on analytical experiment and physical modeling has been undertaken. A model describing thermal non-equilibrium effects is first presented. A correlation of mass transfer has been developed using steam water critical flow tests. This model has been used to predict in a satisfactory manner blowdown tests. It has been incorporated in CLYSTERE system code. To take into account mechanical non-equilibrium, a six equations model is written. To get information on the momentum transfers special nitrogen-water tests have been undertaken. The first results of these studies are presented
Micro-mechanics based damage mechanics for 3D Orthogonal Woven Composites: Experiment and Numerical Modelling

KAUST Repository

Saleh, Mohamed Nasr; Lubineau, Gilles; Potluri, Prasad; Withers, Philip; Soutis, Constantinos

2016-01-01

Damage initiation and evolution of three-dimensional (3D) orthogonal woven carbon fibre composite (3DOWC) is investigated experimentally and numerically. Meso-scale homogenisation of the representative volume element (RVE) is utilised to predict the elastic properties, simulate damage initiation and evolution when loaded in tension. The effect of intra-yarns transverse cracking and shear diffused damage on the in-plane transverse modulus and shear modulus is investigated while one failure criterion is introduced to simulate the matrix damage. The proposed model is based on two major assumptions. First, the effect of the binder yarns, on the in-plane properties, is neglected, so the 3DOWC unit cell can be approximated as a (0o/90o) cross-ply laminate. Second, a micro-mechanics based damage approach is used at the meso-scale, so damage indicators can be correlated, explicitly, to the density of cracks within the material. Results from the simulated RVE are validated against experimental results along the warp (0o direction) and weft (90o direction). This approach paves the road for more predictive models as damage evolution laws are obtained from micro mechanical considerations and rely on few well-defined material parameters. This largely differs from classical damage mechanics approaches in which the evolution law is obtained by retrofitting experimental observations.
Micro-mechanics based damage mechanics for 3D Orthogonal Woven Composites: Experiment and Numerical Modelling

KAUST Repository

Saleh, Mohamed Nasr

2016-01-08

Damage initiation and evolution of three-dimensional (3D) orthogonal woven carbon fibre composite (3DOWC) is investigated experimentally and numerically. Meso-scale homogenisation of the representative volume element (RVE) is utilised to predict the elastic properties, simulate damage initiation and evolution when loaded in tension. The effect of intra-yarns transverse cracking and shear diffused damage on the in-plane transverse modulus and shear modulus is investigated while one failure criterion is introduced to simulate the matrix damage. The proposed model is based on two major assumptions. First, the effect of the binder yarns, on the in-plane properties, is neglected, so the 3DOWC unit cell can be approximated as a (0o/90o) cross-ply laminate. Second, a micro-mechanics based damage approach is used at the meso-scale, so damage indicators can be correlated, explicitly, to the density of cracks within the material. Results from the simulated RVE are validated against experimental results along the warp (0o direction) and weft (90o direction). This approach paves the road for more predictive models as damage evolution laws are obtained from micro mechanical considerations and rely on few well-defined material parameters. This largely differs from classical damage mechanics approaches in which the evolution law is obtained by retrofitting experimental observations.
Endocrine disrupting chemicals in fish: developing exposure indicators and predictive models of effects based on mechanism of action.

Science.gov (United States)

Ankley, Gerald T; Bencic, David C; Breen, Michael S; Collette, Timothy W; Conolly, Rory B; Denslow, Nancy D; Edwards, Stephen W; Ekman, Drew R; Garcia-Reyero, Natalia; Jensen, Kathleen M; Lazorchak, James M; Martinović, Dalma; Miller, David H; Perkins, Edward J; Orlando, Edward F; Villeneuve, Daniel L; Wang, Rong-Lin; Watanabe, Karen H

2009-05-05

Knowledge of possible toxic mechanisms (or modes) of action (MOA) of chemicals can provide valuable insights as to appropriate methods for assessing exposure and effects, thereby reducing uncertainties related to extrapolation across species, endpoints and chemical structure. However, MOA-based testing seldom has been used for assessing the ecological risk of chemicals. This is in part because past regulatory mandates have focused more on adverse effects of chemicals (reductions in survival, growth or reproduction) than the pathways through which these effects are elicited. A recent departure from this involves endocrine-disrupting chemicals (EDCs), where there is a need to understand both MOA and adverse outcomes. To achieve this understanding, advances in predictive approaches are required whereby mechanistic changes caused by chemicals at the molecular level can be translated into apical responses meaningful to ecological risk assessment. In this paper we provide an overview and illustrative results from a large, integrated project that assesses the effects of EDCs on two small fish models, the fathead minnow (Pimephales promelas) and zebrafish (Danio rerio). For this work a systems-based approach is being used to delineate toxicity pathways for 12 model EDCs with different known or hypothesized toxic MOA. The studies employ a combination of state-of-the-art genomic (transcriptomic, proteomic, metabolomic), bioinformatic and modeling approaches, in conjunction with whole animal testing, to develop response linkages across biological levels of organization. This understanding forms the basis for predictive approaches for species, endpoint and chemical extrapolation. Although our project is focused specifically on EDCs in fish, we believe that the basic conceptual approach has utility for systematically assessing exposure and effects of chemicals with other MOA across a variety of biological systems.
Grey prediction with rolling mechanism for electricity demand forecasting of Turkey

International Nuclear Information System (INIS)

Akay, Diyar; Atak, Mehmet

2007-01-01

The need for energy supply, especially for electricity, has been increasing in the last two decades in Turkey. In addition, owing to the uncertain economic structure of the country, electricity consumption has a chaotic and nonlinear trend. Hence, electricity configuration planning and estimation has been the most critical issue of active concern for Turkey. The Turkish Ministry of Energy and Natural Resources (MENR) has officially carried out energy planning studies using the Model of Analysis of the Energy Demand (MAED). In this paper, Grey prediction with rolling mechanism (GPRM) approach is proposed to predict the Turkey's total and industrial electricity consumption. GPRM approach is used because of high prediction accuracy, applicability in the case of limited data situations and requirement of little computational effort. Results show that proposed approach estimates more accurate results than the results of MAED, and have explicit advantages over extant studies. Future projections have also been done for total and industrial sector, respectively
Hybrid Multi-Physics Modeling of an Ultra-Fast Electro-Mechanical Actuator

Directory of Open Access Journals (Sweden)

Ara Bissal

2015-12-01

Full Text Available The challenges of an HVDC breaker are to generate impulsive forces in the order of hundreds of kilonewtons within fractions of a millisecond, to withstand the arising internal mechanical stresses and to transmit these forces via an electrically-insulating device to the contact system with minimum time delay. In this work, several models were developed with different levels of complexity, computation time and accuracy. Experiments were done with two mushroom-shaped armatures to validate the developed simulation models. It was concluded that although the electromagnetic force generation mechanism is highly sensitive to the mechanical response of the system, the developed first order hybrid model is able to predict the performance of the breaker with good accuracy.
Towards an automatic model transformation mechanism from UML state machines to DEVS models

Directory of Open Access Journals (Sweden)

Ariel González

2015-08-01

Full Text Available The development of complex event-driven systems requires studies and analysis prior to deployment with the goal of detecting unwanted behavior. UML is a language widely used by the software engineering community for modeling these systems through state machines, among other mechanisms. Currently, these models do not have appropriate execution and simulation tools to analyze the real behavior of systems. Existing tools do not provide appropriate libraries (sampling from a probability distribution, plotting, etc. both to build and to analyze models. Modeling and simulation for design and prototyping of systems are widely used techniques to predict, investigate and compare the performance of systems. In particular, the Discrete Event System Specification (DEVS formalism separates the modeling and simulation; there are several tools available on the market that run and collect information from DEVS models. This paper proposes a model transformation mechanism from UML state machines to DEVS models in the Model-Driven Development (MDD context, through the declarative QVT Relations language, in order to perform simulations using tools, such as PowerDEVS. A mechanism to validate the transformation is proposed. Moreover, examples of application to analyze the behavior of an automatic banking machine and a control system of an elevator are presented.
Computerized mathematical model for prediction of resin/fiber composite properties

International Nuclear Information System (INIS)

Lowe, K.A.

1985-01-01

A mathematical model has been developed for the design and optimization of resin formulations. The behavior of a fiber-reinforced cured resin matrix can be predicted from constituent properties of the formulation and fiber when component interaction is taken into account. A computer implementation of the mathematical model has been coded to simulate resin/fiber response and generate expected values for any definable properties of the composite. The algorithm is based on multistage regression techniques and the manipulation of n-order matrices. Excellent correlation between actual test values and predicted values has been observed for physical, mechanical, and qualitative properties of resin/fiber composites. Both experimental and commercial resin systems with various fiber reinforcements have been successfully characterized by the model. 6 references, 3 figures, 2 tables
Confidence scores for prediction models

DEFF Research Database (Denmark)

Gerds, Thomas Alexander; van de Wiel, MA

2011-01-01

In medical statistics, many alternative strategies are available for building a prediction model based on training data. Prediction models are routinely compared by means of their prediction performance in independent validation data. If only one data set is available for training and validation,...
Analytical prediction model for non-symmetric fatigue crack growth in Fibre Metal Laminates

NARCIS (Netherlands)

Wang, W.; Rans, C.D.; Benedictus, R.

2017-01-01

This paper proposes an analytical model for predicting the non-symmetric crack growth and accompanying delamination growth in FMLs. The general approach of this model applies Linear Elastic Fracture Mechanics, the principle of superposition, and displacement compatibility based on the
Incorporating drug delivery into an imaging-driven, mechanics-coupled reaction diffusion model for predicting the response of breast cancer to neoadjuvant chemotherapy: theory and preliminary clinical results

Science.gov (United States)

Jarrett, Angela M.; Hormuth, David A.; Barnes, Stephanie L.; Feng, Xinzeng; Huang, Wei; Yankeelov, Thomas E.

2018-05-01

Clinical methods for assessing tumor response to therapy are largely rudimentary, monitoring only temporal changes in tumor size. Our goal is to predict the response of breast tumors to therapy using a mathematical model that utilizes magnetic resonance imaging (MRI) data obtained non-invasively from individual patients. We extended a previously established, mechanically coupled, reaction-diffusion model for predicting tumor response initialized with patient-specific diffusion weighted MRI (DW-MRI) data by including the effects of chemotherapy drug delivery, which is estimated using dynamic contrast-enhanced (DCE-) MRI data. The extended, drug incorporated, model is initialized using patient-specific DW-MRI and DCE-MRI data. Data sets from five breast cancer patients were used—obtained before, after one cycle, and at mid-point of neoadjuvant chemotherapy. The DCE-MRI data was used to estimate spatiotemporal variations in tumor perfusion with the extended Kety–Tofts model. The physiological parameters derived from DCE-MRI were used to model changes in delivery of therapy drugs within the tumor for incorporation in the extended model. We simulated the original model and the extended model in both 2D and 3D and compare the results for this five-patient cohort. Preliminary results show reductions in the error of model predicted tumor cellularity and size compared to the experimentally-measured results for the third MRI scan when therapy was incorporated. Comparing the two models for agreement between the predicted total cellularity and the calculated total cellularity (from the DW-MRI data) reveals an increased concordance correlation coefficient from 0.81 to 0.98 for the 2D analysis and 0.85 to 0.99 for the 3D analysis (p < 0.01 for each) when the extended model was used in place of the original model. This study demonstrates the plausibility of using DCE-MRI data as a means to estimate drug delivery on a patient-specific basis in predictive models and
Confronting species distribution model predictions with species functional traits.

Science.gov (United States)

Wittmann, Marion E; Barnes, Matthew A; Jerde, Christopher L; Jones, Lisa A; Lodge, David M

2016-02-01

Species distribution models are valuable tools in studies of biogeography, ecology, and climate change and have been used to inform conservation and ecosystem management. However, species distribution models typically incorporate only climatic variables and species presence data. Model development or validation rarely considers functional components of species traits or other types of biological data. We implemented a species distribution model (Maxent) to predict global climate habitat suitability for Grass Carp (Ctenopharyngodon idella). We then tested the relationship between the degree of climate habitat suitability predicted by Maxent and the individual growth rates of both wild (N = 17) and stocked (N = 51) Grass Carp populations using correlation analysis. The Grass Carp Maxent model accurately reflected the global occurrence data (AUC = 0.904). Observations of Grass Carp growth rate covered six continents and ranged from 0.19 to 20.1 g day(-1). Species distribution model predictions were correlated (r = 0.5, 95% CI (0.03, 0.79)) with observed growth rates for wild Grass Carp populations but were not correlated (r = -0.26, 95% CI (-0.5, 0.012)) with stocked populations. Further, a review of the literature indicates that the few studies for other species that have previously assessed the relationship between the degree of predicted climate habitat suitability and species functional traits have also discovered significant relationships. Thus, species distribution models may provide inferences beyond just where a species may occur, providing a useful tool to understand the linkage between species distributions and underlying biological mechanisms.
Computer models versus reality: how well do in silico models currently predict the sensitization potential of a substance.

Science.gov (United States)

Teubner, Wera; Mehling, Anette; Schuster, Paul Xaver; Guth, Katharina; Worth, Andrew; Burton, Julien; van Ravenzwaay, Bennard; Landsiedel, Robert

2013-12-01

National legislations for the assessment of the skin sensitization potential of chemicals are increasingly based on the globally harmonized system (GHS). In this study, experimental data on 55 non-sensitizing and 45 sensitizing chemicals were evaluated according to GHS criteria and used to test the performance of computer (in silico) models for the prediction of skin sensitization. Statistic models (Vega, Case Ultra, TOPKAT), mechanistic models (Toxtree, OECD (Q)SAR toolbox, DEREK) or a hybrid model (TIMES-SS) were evaluated. Between three and nine of the substances evaluated were found in the individual training sets of various models. Mechanism based models performed better than statistical models and gave better predictivities depending on the stringency of the domain definition. Best performance was achieved by TIMES-SS, with a perfect prediction, whereby only 16% of the substances were within its reliability domain. Some models offer modules for potency; however predictions did not correlate well with the GHS sensitization subcategory derived from the experimental data. In conclusion, although mechanistic models can be used to a certain degree under well-defined conditions, at the present, the in silico models are not sufficiently accurate for broad application to predict skin sensitization potentials. Copyright © 2013 Elsevier Inc. All rights reserved.
Fracture Mechanics Prediction of Fatigue Life of Aluminum Highway Bridges

DEFF Research Database (Denmark)

Rom, Søren; Agerskov, Henning

2015-01-01

Fracture mechanics prediction of the fatigue life of aluminum highway bridges under random loading is studied. The fatigue life of welded joints has been determined from fracture mechanics analyses and the results obtained have been compared with results from experimental investigations. The fati......Fracture mechanics prediction of the fatigue life of aluminum highway bridges under random loading is studied. The fatigue life of welded joints has been determined from fracture mechanics analyses and the results obtained have been compared with results from experimental investigations...... against fatigue in aluminum bridges, may give results which are unconservative. Furthermore, it was in both investigations found that the validity of the results obtained from Miner's rule will depend on the distribution of the load history in tension and compression....
Predictive multiscale computational model of shoe-floor coefficient of friction.

Science.gov (United States)

Moghaddam, Seyed Reza M; Acharya, Arjun; Redfern, Mark S; Beschorner, Kurt E

2018-01-03

Understanding the frictional interactions between the shoe and floor during walking is critical to prevention of slips and falls, particularly when contaminants are present. A multiscale finite element model of shoe-floor-contaminant friction was developed that takes into account the surface and material characteristics of the shoe and flooring in microscopic and macroscopic scales. The model calculates shoe-floor coefficient of friction (COF) in boundary lubrication regime where effects of adhesion friction and hydrodynamic pressures are negligible. The validity of model outputs was assessed by comparing model predictions to the experimental results from mechanical COF testing. The multiscale model estimates were linearly related to the experimental results (p < 0.0001). The model predicted 73% of variability in experimentally-measured shoe-floor-contaminant COF. The results demonstrate the potential of multiscale finite element modeling in aiding slip-resistant shoe and flooring design and reducing slip and fall injuries. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.
Artificial neural networks in prediction of mechanical behavior of concrete at high temperature

International Nuclear Information System (INIS)

Mukherjee, A.; Nag Biswas, S.

1997-01-01

The behavior of concrete structures that are exposed to extreme thermo-mechanical loading is an issue of great importance in nuclear engineering. The mechanical behavior of concrete at high temperature is non-linear. The properties that regulate its response are highly temperature dependent and extremely complex. In addition, the constituent materials, e.g. aggregates, influence the response significantly. Attempts have been made to trace the stress-strain curve through mathematical models and rheological models. However, it has been difficult to include all the contributing factors in the mathematical model. This paper examines a new programming paradigm, artificial neural networks, for the problem. Implementing a feedforward network and backpropagation algorithm the stress-strain relationship of the material is captured. The neural networks for the prediction of uniaxial behavior of concrete at high temperature has been presented here. The results of the present investigation are very encouraging. (orig.)
Prediction of the mechanical properties of zeolite pellets for aerospace molecular decontamination applications

Directory of Open Access Journals (Sweden)

Guillaume Rioland

2016-11-01

Full Text Available Zeolite pellets containing 5 wt % of binder (methylcellulose or sodium metasilicate were formed with a hydraulic press. This paper describes a mathematical model to predict the mechanical properties (uniaxial and diametric compression of these pellets for arbitrary dimensions (height and diameter using a design of experiments (DOE methodology. A second-degree polynomial equation including interactions was used to approximate the experimental results. This leads to an empirical model for the estimation of the mechanical properties of zeolite pellets with 5 wt % of binder. The model was verified by additional experimental tests including pellets of different dimensions created with different applied pressures. The optimum dimensions were found to be a diameter of 10–23 mm, a height of 1–3.5 mm and an applied pressure higher than 200 MPa. These pellets are promising for technological uses in molecular decontamination for aerospace-based applications.
A predictive coding account of bistable perception - a model-based fMRI study.

Science.gov (United States)

Weilnhammer, Veith; Stuke, Heiner; Hesselmann, Guido; Sterzer, Philipp; Schmack, Katharina

2017-05-01

In bistable vision, subjective perception wavers between two interpretations of a constant ambiguous stimulus. This dissociation between conscious perception and sensory stimulation has motivated various empirical studies on the neural correlates of bistable perception, but the neurocomputational mechanism behind endogenous perceptual transitions has remained elusive. Here, we recurred to a generic Bayesian framework of predictive coding and devised a model that casts endogenous perceptual transitions as a consequence of prediction errors emerging from residual evidence for the suppressed percept. Data simulations revealed close similarities between the model's predictions and key temporal characteristics of perceptual bistability, indicating that the model was able to reproduce bistable perception. Fitting the predictive coding model to behavioural data from an fMRI-experiment on bistable perception, we found a correlation across participants between the model parameter encoding perceptual stabilization and the behaviourally measured frequency of perceptual transitions, corroborating that the model successfully accounted for participants' perception. Formal model comparison with established models of bistable perception based on mutual inhibition and adaptation, noise or a combination of adaptation and noise was used for the validation of the predictive coding model against the established models. Most importantly, model-based analyses of the fMRI data revealed that prediction error time-courses derived from the predictive coding model correlated with neural signal time-courses in bilateral inferior frontal gyri and anterior insulae. Voxel-wise model selection indicated a superiority of the predictive coding model over conventional analysis approaches in explaining neural activity in these frontal areas, suggesting that frontal cortex encodes prediction errors that mediate endogenous perceptual transitions in bistable perception. Taken together, our current work

A predictive coding account of bistable perception - a model-based fMRI study.

Directory of Open Access Journals (Sweden)

Veith Weilnhammer

2017-05-01

Full Text Available In bistable vision, subjective perception wavers between two interpretations of a constant ambiguous stimulus. This dissociation between conscious perception and sensory stimulation has motivated various empirical studies on the neural correlates of bistable perception, but the neurocomputational mechanism behind endogenous perceptual transitions has remained elusive. Here, we recurred to a generic Bayesian framework of predictive coding and devised a model that casts endogenous perceptual transitions as a consequence of prediction errors emerging from residual evidence for the suppressed percept. Data simulations revealed close similarities between the model's predictions and key temporal characteristics of perceptual bistability, indicating that the model was able to reproduce bistable perception. Fitting the predictive coding model to behavioural data from an fMRI-experiment on bistable perception, we found a correlation across participants between the model parameter encoding perceptual stabilization and the behaviourally measured frequency of perceptual transitions, corroborating that the model successfully accounted for participants' perception. Formal model comparison with established models of bistable perception based on mutual inhibition and adaptation, noise or a combination of adaptation and noise was used for the validation of the predictive coding model against the established models. Most importantly, model-based analyses of the fMRI data revealed that prediction error time-courses derived from the predictive coding model correlated with neural signal time-courses in bilateral inferior frontal gyri and anterior insulae. Voxel-wise model selection indicated a superiority of the predictive coding model over conventional analysis approaches in explaining neural activity in these frontal areas, suggesting that frontal cortex encodes prediction errors that mediate endogenous perceptual transitions in bistable perception. Taken together
A polynomial based model for cell fate prediction in human diseases.

Science.gov (United States)

Ma, Lichun; Zheng, Jie

2017-12-21

Cell fate regulation directly affects tissue homeostasis and human health. Research on cell fate decision sheds light on key regulators, facilitates understanding the mechanisms, and suggests novel strategies to treat human diseases that are related to abnormal cell development. In this study, we proposed a polynomial based model to predict cell fate. This model was derived from Taylor series. As a case study, gene expression data of pancreatic cells were adopted to test and verify the model. As numerous features (genes) are available, we employed two kinds of feature selection methods, i.e. correlation based and apoptosis pathway based. Then polynomials of different degrees were used to refine the cell fate prediction function. 10-fold cross-validation was carried out to evaluate the performance of our model. In addition, we analyzed the stability of the resultant cell fate prediction model by evaluating the ranges of the parameters, as well as assessing the variances of the predicted values at randomly selected points. Results show that, within both the two considered gene selection methods, the prediction accuracies of polynomials of different degrees show little differences. Interestingly, the linear polynomial (degree 1 polynomial) is more stable than others. When comparing the linear polynomials based on the two gene selection methods, it shows that although the accuracy of the linear polynomial that uses correlation analysis outcomes is a little higher (achieves 86.62%), the one within genes of the apoptosis pathway is much more stable. Considering both the prediction accuracy and the stability of polynomial models of different degrees, the linear model is a preferred choice for cell fate prediction with gene expression data of pancreatic cells. The presented cell fate prediction model can be extended to other cells, which may be important for basic research as well as clinical study of cell development related diseases.
Numerical modelling of the time-dependent mechanical behaviour of softwood

DEFF Research Database (Denmark)

Engelund, Emil Tang

2010-01-01

When using wood as a structural material it is important to consider its time-dependent mechanical behaviour and to predict this behaviour for decades ahead. For this purpose, several rheological mathematical models, spanning from fairly simple to very complex ones, have been developed over...
Development of centrifuge modeling for evaluating the mechanisms of collapse above underground openings

International Nuclear Information System (INIS)

Davis, B.C.; Kutter, B.L.; Chang, J.D.L.

1988-01-01

Improved prediction of surface collapse above an underground cavity is important in many LLNL programs, including Nuclear Test. To improve the predictive capability, LLNL must better understand the mechanisms involved in the process of collapse. The research aims to develop the centrifuge technique for modeling mechanisms of underground collapse in soil. The authors will also evaluate the adequacy of existing constitutive or flow models of soils for modeling underground collapse. During FY 86, using the centrifuge at University of California, Davis, the authors developed the basic centrifugal modeling technique, conducted experiments, and modeled the process on a computer. In FY 87, they continued to develop the experimental method and analyze results. Results to date have shown that the model dimensions are not necessarily the critical dimensions (i.e., those determining the adequacy of the model). Rather, the critical dimension is the diameter of the chimney above the opening that develops during collapse
Predicting failure response of spot welded joints using recent extensions to the Gurson model

DEFF Research Database (Denmark)

Nielsen, Kim Lau

2010-01-01

The plug failure modes of resistance spot welded shear-lab and cross-tension test specimens are studied, using recent extensions to the Gurson model. A comparison of the predicted mechanical response is presented when using either: (i) the Gurson-Tvergaard-Needleman model (GTN-model), (ii...... is presented. The models are applied to predict failure of specimens containing a fully intact weld nugget as well as a partly removed weld nugget to address the problems of shrinkage voids or larger weld defects. All analysis are carried out by full 3D finite element modelling....
A mechanical model for FRP-strengthened beams in bending

Directory of Open Access Journals (Sweden)

P. S. Valvo

2012-10-01

Full Text Available We analyse the problem of a simply supported beam, strengthened with a fibre-reinforced polymer (FRP strip bonded to its intrados and subjected to bending couples applied to its end sections. A mechanical model is proposed, whereby the beam and FRP strip are modelled according to classical beam theory, while the adhesive and its neighbouring layers are modelled as an interface having a piecewise linear constitutive law defined over three intervals (elastic response – softening response – debonding. The model is described by a set of differential equations with appropriate boundary conditions. An analytical solution to the problem is determined, including explicit expressions for the internal forces, displacements and interfacial stresses. The model predicts an overall non-linear mechanical response for the strengthened beam, ranging over several stages: from linearly elastic behaviour to damage, until the complete detachment of the FRP reinforcement.
Degradation Prediction Model Based on a Neural Network with Dynamic Windows

Science.gov (United States)

Zhang, Xinghui; Xiao, Lei; Kang, Jianshe

2015-01-01

Tracking degradation of mechanical components is very critical for effective maintenance decision making. Remaining useful life (RUL) estimation is a widely used form of degradation prediction. RUL prediction methods when enough run-to-failure condition monitoring data can be used have been fully researched, but for some high reliability components, it is very difficult to collect run-to-failure condition monitoring data, i.e., from normal to failure. Only a certain number of condition indicators in certain period can be used to estimate RUL. In addition, some existing prediction methods have problems which block RUL estimation due to poor extrapolability. The predicted value converges to a certain constant or fluctuates in certain range. Moreover, the fluctuant condition features also have bad effects on prediction. In order to solve these dilemmas, this paper proposes a RUL prediction model based on neural network with dynamic windows. This model mainly consists of three steps: window size determination by increasing rate, change point detection and rolling prediction. The proposed method has two dominant strengths. One is that the proposed approach does not need to assume the degradation trajectory is subject to a certain distribution. The other is it can adapt to variation of degradation indicators which greatly benefits RUL prediction. Finally, the performance of the proposed RUL prediction model is validated by real field data and simulation data. PMID:25806873
Does predictability matter? Effects of cue predictability on neurocognitive mechanisms underlying prospective memory

OpenAIRE

Cona, Giorgia; Arcara, Giorgio; Tarantino, Vincenza; Bisiacchi, Patrizia S.

2015-01-01

Prospective memory (PM) represents the ability to successfully realize intentions when the appropriate moment or cue occurs. In this study, we used event-related potentials (ERPs) to explore the impact of cue predictability on the cognitive and neural mechanisms supporting PM. Participants performed an ongoing task and, simultaneously, had to remember to execute a pre-specified action when they encountered the PM cues. The occurrence of the PM cues was predictable (being signaled by a warning...
Prediction of Chemical Function: Model Development and ...

Science.gov (United States)

The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (HT) screening-level exposures developed under ExpoCast can be combined with HT screening (HTS) bioactivity data for the risk-based prioritization of chemicals for further evaluation. The functional role (e.g. solvent, plasticizer, fragrance) that a chemical performs can drive both the types of products in which it is found and the concentration in which it is present and therefore impacting exposure potential. However, critical chemical use information (including functional role) is lacking for the majority of commercial chemicals for which exposure estimates are needed. A suite of machine-learning based models for classifying chemicals in terms of their likely functional roles in products based on structure were developed. This effort required collection, curation, and harmonization of publically-available data sources of chemical functional use information from government and industry bodies. Physicochemical and structure descriptor data were generated for chemicals with function data. Machine-learning classifier models for function were then built in a cross-validated manner from the descriptor/function data using the method of random forests. The models were applied to: 1) predict chemi
Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches

Energy Technology Data Exchange (ETDEWEB)

Singh, Kunwar P., E-mail: kpsingh_52@yahoo.com [Academy of Scientific and Innovative Research, Council of Scientific and Industrial Research, New Delhi (India); Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001 (India); Gupta, Shikha; Rai, Premanjali [Academy of Scientific and Innovative Research, Council of Scientific and Industrial Research, New Delhi (India); Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001 (India)

2013-10-15

Robust global models capable of discriminating positive and non-positive carcinogens; and predicting carcinogenic potency of chemicals in rodents were developed. The dataset of 834 structurally diverse chemicals extracted from Carcinogenic Potency Database (CPDB) was used which contained 466 positive and 368 non-positive carcinogens. Twelve non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals and nonlinearity in the data were evaluated using Tanimoto similarity index and Brock–Dechert–Scheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) models were constructed for classification and function optimization problems using the carcinogenicity end point in rat. Validation of the models was performed using the internal and external procedures employing a wide series of statistical checks. PNN constructed using five descriptors rendered classification accuracy of 92.09% in complete rat data. The PNN model rendered classification accuracies of 91.77%, 80.70% and 92.08% in mouse, hamster and pesticide data, respectively. The GRNN constructed with nine descriptors yielded correlation coefficient of 0.896 between the measured and predicted carcinogenic potency with mean squared error (MSE) of 0.44 in complete rat data. The rat carcinogenicity model (GRNN) applied to the mouse and hamster data yielded correlation coefficient and MSE of 0.758, 0.71 and 0.760, 0.46, respectively. The results suggest for wide applicability of the inter-species models in predicting carcinogenic potency of chemicals. Both the PNN and GRNN (inter-species) models constructed here can be useful tools in predicting the carcinogenicity of new chemicals for regulatory purposes. - Graphical abstract: Figure (a) shows classification accuracies (positive and non-positive carcinogens) in rat, mouse, hamster, and pesticide data yielded by optimal PNN model. Figure (b) shows generalization and predictive
Coupling of EIT with computational lung modeling for predicting patient-specific ventilatory responses.

Science.gov (United States)

Roth, Christian J; Becher, Tobias; Frerichs, Inéz; Weiler, Norbert; Wall, Wolfgang A

2017-04-01

Providing optimal personalized mechanical ventilation for patients with acute or chronic respiratory failure is still a challenge within a clinical setting for each case anew. In this article, we integrate electrical impedance tomography (EIT) monitoring into a powerful patient-specific computational lung model to create an approach for personalizing protective ventilatory treatment. The underlying computational lung model is based on a single computed tomography scan and able to predict global airflow quantities, as well as local tissue aeration and strains for any ventilation maneuver. For validation, a novel "virtual EIT" module is added to our computational lung model, allowing to simulate EIT images based on the patient's thorax geometry and the results of our numerically predicted tissue aeration. Clinically measured EIT images are not used to calibrate the computational model. Thus they provide an independent method to validate the computational predictions at high temporal resolution. The performance of this coupling approach has been tested in an example patient with acute respiratory distress syndrome. The method shows good agreement between computationally predicted and clinically measured airflow data and EIT images. These results imply that the proposed framework can be used for numerical prediction of patient-specific responses to certain therapeutic measures before applying them to an actual patient. In the long run, definition of patient-specific optimal ventilation protocols might be assisted by computational modeling. NEW & NOTEWORTHY In this work, we present a patient-specific computational lung model that is able to predict global and local ventilatory quantities for a given patient and any selected ventilation protocol. For the first time, such a predictive lung model is equipped with a virtual electrical impedance tomography module allowing real-time validation of the computed results with the patient measurements. First promising results
Accurate and dynamic predictive model for better prediction in medicine and healthcare.

Science.gov (United States)

Alanazi, H O; Abdullah, A H; Qureshi, K N; Ismail, A S

2018-05-01

Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance. In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life. The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.
Prediction of mechanical properties of trabecular bone using quantitative MRI

International Nuclear Information System (INIS)

Lammentausta, E; Hakulinen, M A; Jurvelin, J S; Nieminen, M T

2006-01-01

Techniques for quantitative magnetic resonance imaging (MRI) have been developed for non-invasive estimation of the mineral density and structure of trabecular bone. The R* 2 relaxation rate (i.e. 1/T* 2 ) is sensitive to bone mineral density (BMD) via susceptibility differences between trabeculae and bone marrow, and by binarizing MRI images, structural variables, such as apparent bone volume fraction, can be assessed. In the present study, trabecular bone samples of human patellae were investigated in vitro at 1.5 T to determine the ability of MRI-derived variables (R* 2 and bone volume fraction) to predict the mechanical properties (Young's modulus, yield stress and ultimate strength). Further, the MRI variables were correlated with reference measurements of volumetric BMD and bone area fraction as determined with a clinical pQCT system. The MRI variables correlated significantly (p 2 and MRI-derived bone volume fraction further improved the prediction of yield stress and ultimate strength. Although pQCT showed a trend towards better prediction of the mechanical properties, current results demonstrate the feasibility of combined MR imaging of marrow susceptibility and bone volume fraction in predicting the mechanical strength of trabecular bone and bone mineral density
Flexible non-linear predictive models for large-scale wind turbine diagnostics

DEFF Research Database (Denmark)

Bach-Andersen, Martin; Rømer-Odgaard, Bo; Winther, Ole

2017-01-01

We demonstrate how flexible non-linear models can provide accurate and robust predictions on turbine component temperature sensor data using data-driven principles and only a minimum of system modeling. The merits of different model architectures are evaluated using data from a large set...... of turbines operating under diverse conditions. We then go on to test the predictive models in a diagnostic setting, where the output of the models are used to detect mechanical faults in rotor bearings. Using retrospective data from 22 actual rotor bearing failures, the fault detection performance...... of the models are quantified using a structured framework that provides the metrics required for evaluating the performance in a fleet wide monitoring setup. It is demonstrated that faults are identified with high accuracy up to 45 days before a warning from the hard-threshold warning system....
Insight into model mechanisms through automatic parameter fitting: a new methodological framework for model development.

Science.gov (United States)

Tøndel, Kristin; Niederer, Steven A; Land, Sander; Smith, Nicolas P

2014-05-20

Striking a balance between the degree of model complexity and parameter identifiability, while still producing biologically feasible simulations using modelling is a major challenge in computational biology. While these two elements of model development are closely coupled, parameter fitting from measured data and analysis of model mechanisms have traditionally been performed separately and sequentially. This process produces potential mismatches between model and data complexities that can compromise the ability of computational frameworks to reveal mechanistic insights or predict new behaviour. In this study we address this issue by presenting a generic framework for combined model parameterisation, comparison of model alternatives and analysis of model mechanisms. The presented methodology is based on a combination of multivariate metamodelling (statistical approximation of the input-output relationships of deterministic models) and a systematic zooming into biologically feasible regions of the parameter space by iterative generation of new experimental designs and look-up of simulations in the proximity of the measured data. The parameter fitting pipeline includes an implicit sensitivity analysis and analysis of parameter identifiability, making it suitable for testing hypotheses for model reduction. Using this approach, under-constrained model parameters, as well as the coupling between parameters within the model are identified. The methodology is demonstrated by refitting the parameters of a published model of cardiac cellular mechanics using a combination of measured data and synthetic data from an alternative model of the same system. Using this approach, reduced models with simplified expressions for the tropomyosin/crossbridge kinetics were found by identification of model components that can be omitted without affecting the fit to the parameterising data. Our analysis revealed that model parameters could be constrained to a standard deviation of on
An approach to model validation and model-based prediction -- polyurethane foam case study.

Energy Technology Data Exchange (ETDEWEB)

Dowding, Kevin J.; Rutherford, Brian Milne

2003-07-01

analyses and hypothesis tests as a part of the validation step to provide feedback to analysts and modelers. Decisions on how to proceed in making model-based predictions are made based on these analyses together with the application requirements. Updating modifying and understanding the boundaries associated with the model are also assisted through this feedback. (4) We include a ''model supplement term'' when model problems are indicated. This term provides a (bias) correction to the model so that it will better match the experimental results and more accurately account for uncertainty. Presumably, as the models continue to develop and are used for future applications, the causes for these apparent biases will be identified and the need for this supplementary modeling will diminish. (5) We use a response-modeling approach for our predictions that allows for general types of prediction and for assessment of prediction uncertainty. This approach is demonstrated through a case study supporting the assessment of a weapons response when subjected to a hydrocarbon fuel fire. The foam decomposition model provides an important element of the response of a weapon system in this abnormal thermal environment. Rigid foam is used to encapsulate critical components in the weapon system providing the needed mechanical support as well as thermal isolation. Because the foam begins to decompose at temperatures above 250 C, modeling the decomposition is critical to assessing a weapons response. In the validation analysis it is indicated that the model tends to ''exaggerate'' the effect of temperature changes when compared to the experimental results. The data, however, are too few and to restricted in terms of experimental design to make confident statements regarding modeling problems. For illustration, we assume these indications are correct and compensate for this apparent bias by constructing a model supplement term for use in the model
Modeling of mechanical properties of as-cast Mg-Li-Al alloys based on PSO-BP algorithm

Directory of Open Access Journals (Sweden)

Li Ming

2012-05-01

Full Text Available Artificial neural networks have been widely used to predict the mechanical properties of alloys in material research. This study aims to investigate the implicit relationship between the compositions and mechanical properties of as-cast Mg-Li-Al alloys. Based on the experimental collection of the tensile strength and the elongation of representative Mg-Li-Al alloys, a momentum back-propagation (BP neural network with a single hidden layer was established. Particle swarm optimization (PSO was applied to optimize the BP model. In the neural network, the input variables were the contents of Mg, Li and Al, and the output variables were the tensile strength and the elongation. The results show that the proposed PSO-BP model can describe the quantitative relationship between the Mg-Li-Al alloy’s composition and its mechanical properties. It is possible that the mechanical properties to be predicted without experiment by inputting the alloy composition into the trained network model. The prediction of the influence of Al addition on the mechanical properties of as-cast Mg-Li-Al alloys is consistent with the related research results.
Health-aware Model Predictive Control of Wind Turbines using Fatigue Prognosis

DEFF Research Database (Denmark)

Sardi, Hector Eloy Sanchez; Escobet, Teressa; Puig, Vicenc

2015-01-01

management module with the control provides a mechanism for the wind turbine to operate safely and optimize the trade-off between components life and energy production. The research presented in this paper explores the integration of model predictive control (MPC) with fatigue-based prognosis approach...
Prediction of tectonic stresses and fracture networks with geomechanical reservoir models

International Nuclear Information System (INIS)

Henk, A.; Fischer, K.

2014-09-01

This project evaluates the potential of geomechanical Finite Element (FE) models for the prediction of in situ stresses and fracture networks in faulted reservoirs. Modeling focuses on spatial variations of the in situ stress distribution resulting from faults and contrasts in mechanical rock properties. In a first methodological part, a workflow is developed for building such geomechanical reservoir models and calibrating them to field data. In the second part, this workflow was applied successfully to an intensively faulted gas reservoir in the North German Basin. A truly field-scale geomechanical model covering more than 400km 2 was built and calibrated. It includes a mechanical stratigraphy as well as a network of 86 faults. The latter are implemented as distinct planes of weakness and allow the fault-specific evaluation of shear and normal stresses. A so-called static model describes the recent state of the reservoir and, thus, after calibration its results reveal the present-day in situ stress distribution. Further geodynamic modeling work considers the major stages in the tectonic history of the reservoir and provides insights in the paleo stress distribution. These results are compared to fracture data and hydraulic fault behavior observed today. The outcome of this project confirms the potential of geomechanical FE models for robust stress and fracture predictions. The workflow is generally applicable and can be used for modeling of any stress-sensitive reservoir.
Prediction of tectonic stresses and fracture networks with geomechanical reservoir models

Energy Technology Data Exchange (ETDEWEB)

Henk, A.; Fischer, K. [TU Darmstadt (Germany). Inst. fuer Angewandte Geowissenschaften

2014-09-15

This project evaluates the potential of geomechanical Finite Element (FE) models for the prediction of in situ stresses and fracture networks in faulted reservoirs. Modeling focuses on spatial variations of the in situ stress distribution resulting from faults and contrasts in mechanical rock properties. In a first methodological part, a workflow is developed for building such geomechanical reservoir models and calibrating them to field data. In the second part, this workflow was applied successfully to an intensively faulted gas reservoir in the North German Basin. A truly field-scale geomechanical model covering more than 400km{sup 2} was built and calibrated. It includes a mechanical stratigraphy as well as a network of 86 faults. The latter are implemented as distinct planes of weakness and allow the fault-specific evaluation of shear and normal stresses. A so-called static model describes the recent state of the reservoir and, thus, after calibration its results reveal the present-day in situ stress distribution. Further geodynamic modeling work considers the major stages in the tectonic history of the reservoir and provides insights in the paleo stress distribution. These results are compared to fracture data and hydraulic fault behavior observed today. The outcome of this project confirms the potential of geomechanical FE models for robust stress and fracture predictions. The workflow is generally applicable and can be used for modeling of any stress-sensitive reservoir.

Hyper-resolution hydrological modeling: Completeness of Formulation, Appropriateness of Descritization, and Physical LImits of Predictability

Science.gov (United States)

Ogden, F. L.

2017-12-01

HIgh performance computing and the widespread availabilities of geospatial physiographic and forcing datasets have enabled consideration of flood impact predictions with longer lead times and more detailed spatial descriptions. We are now considering multi-hour flash flood forecast lead times at the subdivision level in so-called hydroblind regions away from the National Hydrography network. However, the computational demands of such models are high, necessitating a nested simulation approach. Research on hyper-resolution hydrologic modeling over the past three decades have illustrated some fundamental limits on predictability that are simultaneously related to runoff generation mechanism(s), antecedent conditions, rates and total amounts of precipitation, discretization of the model domain, and complexity or completeness of the model formulation. This latter point is an acknowledgement that in some ways hydrologic understanding in key areas related to land use, land cover, tillage practices, seasonality, and biological effects has some glaring deficiencies. This presentation represents a review of what is known related to the interacting effects of precipitation amount, model spatial discretization, antecedent conditions, physiographic characteristics and model formulation completeness for runoff predictions. These interactions define a region in multidimensional forcing, parameter and process space where there are in some cases clear limits on predictability, and in other cases diminished uncertainty.
A numerical approach to model and predict the energy absorption and crush mechanics within a long-fiber composite crush tube

Science.gov (United States)

Pickett, Leon, Jr.

Past research has conclusively shown that long fiber structural composites possess superior specific energy absorption characteristics as compared to steel and aluminum structures. However, destructive physical testing of composites is very costly and time consuming. As a result, numerical solutions are desirable as an alternative to experimental testing. Up until this point, very little numerical work has been successful in predicting the energy absorption of composite crush structures. This research investigates the ability to use commercially available numerical modeling tools to approximate the energy absorption capability of long-fiber composite crush tubes. This study is significant because it provides a preliminary analysis of the suitability of LS-DYNA to numerically characterize the crushing behavior of a dynamic axial impact crushing event. Composite crushing theory suggests that there are several crushing mechanisms occurring during a composite crush event. This research evaluates the capability and suitability of employing, LS-DYNA, to simulate the dynamic crush event of an E-glass/epoxy cylindrical tube. The model employed is the composite "progressive failure model", a much more limited failure model when compared to the experimental failure events which naturally occur. This numerical model employs (1) matrix cracking, (2) compression, and (3) fiber breakage failure modes only. The motivation for the work comes from the need to reduce the significant cost associated with experimental trials. This research chronicles some preliminary efforts to better understand the mechanics essential in pursuit of this goal. The immediate goal is to begin to provide deeper understanding of a composite crush event and ultimately create a viable alternative to destructive testing of composite crush tubes.
Multirule Based Diagnostic Approach for the Fog Predictions Using WRF Modelling Tool

Directory of Open Access Journals (Sweden)

Swagata Payra

2014-01-01

Full Text Available The prediction of fog onset remains difficult despite the progress in numerical weather prediction. It is a complex process and requires adequate representation of the local perturbations in weather prediction models. It mainly depends upon microphysical and mesoscale processes that act within the boundary layer. This study utilizes a multirule based diagnostic (MRD approach using postprocessing of the model simulations for fog predictions. The empiricism involved in this approach is mainly to bridge the gap between mesoscale and microscale variables, which are related to mechanism of the fog formation. Fog occurrence is a common phenomenon during winter season over Delhi, India, with the passage of the western disturbances across northwestern part of the country accompanied with significant amount of moisture. This study implements the above cited approach for the prediction of occurrences of fog and its onset time over Delhi. For this purpose, a high resolution weather research and forecasting (WRF model is used for fog simulations. The study involves depiction of model validation and postprocessing of the model simulations for MRD approach and its subsequent application to fog predictions. Through this approach model identified foggy and nonfoggy days successfully 94% of the time. Further, the onset of fog events is well captured within an accuracy of 30–90 minutes. This study demonstrates that the multirule based postprocessing approach is a useful and highly promising tool in improving the fog predictions.
Multi-model analysis in hydrological prediction

Science.gov (United States)

Lanthier, M.; Arsenault, R.; Brissette, F.

2017-12-01

Hydrologic modelling, by nature, is a simplification of the real-world hydrologic system. Therefore ensemble hydrological predictions thus obtained do not present the full range of possible streamflow outcomes, thereby producing ensembles which demonstrate errors in variance such as under-dispersion. Past studies show that lumped models used in prediction mode can return satisfactory results, especially when there is not enough information available on the watershed to run a distributed model. But all lumped models greatly simplify the complex processes of the hydrologic cycle. To generate more spread in the hydrologic ensemble predictions, multi-model ensembles have been considered. In this study, the aim is to propose and analyse a method that gives an ensemble streamflow prediction that properly represents the forecast probabilities and reduced ensemble bias. To achieve this, three simple lumped models are used to generate an ensemble. These will also be combined using multi-model averaging techniques, which generally generate a more accurate hydrogram than the best of the individual models in simulation mode. This new predictive combined hydrogram is added to the ensemble, thus creating a large ensemble which may improve the variability while also improving the ensemble mean bias. The quality of the predictions is then assessed on different periods: 2 weeks, 1 month, 3 months and 6 months using a PIT Histogram of the percentiles of the real observation volumes with respect to the volumes of the ensemble members. Initially, the models were run using historical weather data to generate synthetic flows. This worked for individual models, but not for the multi-model and for the large ensemble. Consequently, by performing data assimilation at each prediction period and thus adjusting the initial states of the models, the PIT Histogram could be constructed using the observed flows while allowing the use of the multi-model predictions. The under-dispersion has been
Modeling the mechanics of cancer: effect of changes in cellular and extra-cellular mechanical properties.

Science.gov (United States)

Katira, Parag; Bonnecaze, Roger T; Zaman, Muhammad H

2013-01-01

Malignant transformation, though primarily driven by genetic mutations in cells, is also accompanied by specific changes in cellular and extra-cellular mechanical properties such as stiffness and adhesivity. As the transformed cells grow into tumors, they interact with their surroundings via physical contacts and the application of forces. These forces can lead to changes in the mechanical regulation of cell fate based on the mechanical properties of the cells and their surrounding environment. A comprehensive understanding of cancer progression requires the study of how specific changes in mechanical properties influences collective cell behavior during tumor growth and metastasis. Here we review some key results from computational models describing the effect of changes in cellular and extra-cellular mechanical properties and identify mechanistic pathways for cancer progression that can be targeted for the prediction, treatment, and prevention of cancer.
Fast integration-based prediction bands for ordinary differential equation models.

Science.gov (United States)

Hass, Helge; Kreutz, Clemens; Timmer, Jens; Kaschek, Daniel

2016-04-15

To gain a deeper understanding of biological processes and their relevance in disease, mathematical models are built upon experimental data. Uncertainty in the data leads to uncertainties of the model's parameters and in turn to uncertainties of predictions. Mechanistic dynamic models of biochemical networks are frequently based on nonlinear differential equation systems and feature a large number of parameters, sparse observations of the model components and lack of information in the available data. Due to the curse of dimensionality, classical and sampling approaches propagating parameter uncertainties to predictions are hardly feasible and insufficient. However, for experimental design and to discriminate between competing models, prediction and confidence bands are essential. To circumvent the hurdles of the former methods, an approach to calculate a profile likelihood on arbitrary observations for a specific time point has been introduced, which provides accurate confidence and prediction intervals for nonlinear models and is computationally feasible for high-dimensional models. In this article, reliable and smooth point-wise prediction and confidence bands to assess the model's uncertainty on the whole time-course are achieved via explicit integration with elaborate correction mechanisms. The corresponding system of ordinary differential equations is derived and tested on three established models for cellular signalling. An efficiency analysis is performed to illustrate the computational benefit compared with repeated profile likelihood calculations at multiple time points. The integration framework and the examples used in this article are provided with the software package Data2Dynamics, which is based on MATLAB and freely available at http://www.data2dynamics.org helge.hass@fdm.uni-freiburg.de Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e
A hybrid model to predict the entrainment and subsurface transport of oil

International Nuclear Information System (INIS)

Spaulding, M.L.; Odulo, A.; Kolluru, V.S.

1992-01-01

The entrainment of surface oil into the water column and its subsequent subsurface transport and dispersion are predicted by a hybrid analytic-numerical solution to the advective diffusion equation. Total oil or selected hydrocarbon component concentrations in the water column are predicted. Assuming that the principal mechanism for entrainment is due to breaking waves, the oil entrainment rate is specified using the empirically based algorithm of Delvigne and Sweeney (1988). The subsurface transport model explicitly accounts for buoyant forces on dispersed oil by droplet size. Application of the model to an analytic test case and several hypothetical scenarios illustrates the model's utility. 35 refs., 8 figs., 2 tabs
Numerical Modeling and Mechanical Analysis of Flexible Risers

Directory of Open Access Journals (Sweden)

J. Y. Li

2015-01-01

Full Text Available ABAQUS is used to create a detailed finite element model for a 10-layer unbonded flexible riser to simulate the riser’s mechanical behavior under three load conditions: tension force and internal and external pressure. It presents a technique to create detailed finite element model and to analyze flexible risers. In FEM model, all layers are modeled separately with contact interfaces; interaction between steel trips in certain layers has been considered as well. FEM model considering contact interaction, geometric nonlinearity, and friction has been employed to accurately simulate the structural behavior of riser. The model includes the main features of the riser geometry with very little simplifying assumptions. The model was solved using a fully explicit time-integration scheme implemented in a parallel environment on an eight-processor cluster and 24 G memory computer. There is a very good agreement obtained from numerical and analytical comparisons, which validates the use of numerical model here. The results from the numerical simulation show that the numerical model takes into account various details of the riser. It has been shown that the detailed finite element model can be used to predict riser’s mechanics behavior under various load cases and bound conditions.
Predictive Finite Rate Model for Oxygen-Carbon Interactions at High Temperature

Science.gov (United States)

Poovathingal, Savio

An oxidation model for carbon surfaces is developed to predict ablation rates for carbon heat shields used in hypersonic vehicles. Unlike existing empirical models, the approach used here was to probe gas-surface interactions individually and then based on an understanding of the relevant fundamental processes, build a predictive model that would be accurate over a wide range of pressures and temperatures, and even microstructures. Initially, molecular dynamics was used to understand the oxidation processes on the surface. The molecular dynamics simulations were compared to molecular beam experiments and good qualitative agreement was observed. The simulations reproduced cylindrical pitting observed in the experiments where oxidation was rapid and primarily occurred around a defect. However, the studies were limited to small systems at low temperatures and could simulate time scales only of the order of nanoseconds. Molecular beam experiments at high surface temperature indicated that a majority of surface reaction products were produced through thermal mechanisms. Since the reactions were thermal, they occurred over long time scales which were computationally prohibitive for molecular dynamics to simulate. The experiments provided detailed dynamical data on the scattering of O, O2, CO, and CO2 and it was found that the data from molecular beam experiments could be used directly to build a model. The data was initially used to deduce surface reaction probabilities at 800 K. The reaction probabilities were then incorporated into the direct simulation Monte Carlo (DSMC) method. Simulations were performed where the microstructure was resolved and dissociated oxygen convected and diffused towards it. For a gas-surface temperature of 800 K, it was found that despite CO being the dominant surface reaction product, a gas-phase reaction forms significant CO2 within the microstructure region. It was also found that surface area did not play any role in concentration of
Delayed hydride cracking: theoretical model testing to predict cracking velocity

International Nuclear Information System (INIS)

Mieza, Juan I.; Vigna, Gustavo L.; Domizzi, Gladys

2009-01-01

Pressure tubes from Candu nuclear reactors as any other component manufactured with Zr alloys are prone to delayed hydride cracking. That is why it is important to be able to predict the cracking velocity during the component lifetime from parameters easy to be measured, such as: hydrogen concentration, mechanical and microstructural properties. Two of the theoretical models reported in literature to calculate the DHC velocity were chosen and combined, and using the appropriate variables allowed a comparison with experimental results of samples from Zr-2.5 Nb tubes with different mechanical and structural properties. In addition, velocities measured by other authors in irradiated materials could be reproduced using the model described above. (author)
Micro-Vibration Performance Prediction of SEPTA24 Using SMeSim (RUAG Space Mechanism Simulator Tool)

Science.gov (United States)

Omiciuolo, Manolo; Lang, Andreas; Wismer, Stefan; Barth, Stephan; Szekely, Gerhard

2013-09-01

Scientific space missions are currently challenging the performances of their payloads. The performances can be dramatically restricted by micro-vibration loads generated by any moving parts of the satellites, thus by Solar Array Drive Assemblies too. Micro-vibration prediction of SADAs is therefore very important to support their design and optimization in the early stages of a programme. The Space Mechanism Simulator (SMeSim) tool, developed by RUAG, enhances the capability of analysing the micro-vibration emissivity of a Solar Array Drive Assembly (SADA) under a specified set of boundary conditions. The tool is developed in the Matlab/Simulink® environment throughout a library of blocks simulating the different components a SADA is made of. The modular architecture of the blocks, assembled by the user, and the set up of the boundary conditions allow time-domain and frequency-domain analyses of a rigid multi-body model with concentrated flexibilities and coupled- electronic control of the mechanism. SMeSim is used to model the SEPTA24 Solar Array Drive Mechanism and predict its micro-vibration emissivity. SMeSim and the return of experience earned throughout its development and use can now support activities like verification by analysis of micro-vibration emissivity requirements and/or design optimization to minimize the micro- vibration emissivity of a SADA.
Advanced Computational Modeling Approaches for Shock Response Prediction

Science.gov (United States)

Derkevorkian, Armen; Kolaini, Ali R.; Peterson, Lee

2015-01-01

Motivation: (1) The activation of pyroshock devices such as explosives, separation nuts, pin-pullers, etc. produces high frequency transient structural response, typically from few tens of Hz to several hundreds of kHz. (2) Lack of reliable analytical tools makes the prediction of appropriate design and qualification test levels a challenge. (3) In the past few decades, several attempts have been made to develop methodologies that predict the structural responses to shock environments. (4) Currently, there is no validated approach that is viable to predict shock environments overt the full frequency range (i.e., 100 Hz to 10 kHz). Scope: (1) Model, analyze, and interpret space structural systems with complex interfaces and discontinuities, subjected to shock loads. (2) Assess the viability of a suite of numerical tools to simulate transient, non-linear solid mechanics and structural dynamics problems, such as shock wave propagation.
Lexical prediction via forward models: N400 evidence from German Sign Language.

Science.gov (United States)

Hosemann, Jana; Herrmann, Annika; Steinbach, Markus; Bornkessel-Schlesewsky, Ina; Schlesewsky, Matthias

2013-09-01

Models of language processing in the human brain often emphasize the prediction of upcoming input-for example in order to explain the rapidity of language understanding. However, the precise mechanisms of prediction are still poorly understood. Forward models, which draw upon the language production system to set up expectations during comprehension, provide a promising approach in this regard. Here, we present an event-related potential (ERP) study on German Sign Language (DGS) which tested the hypotheses of a forward model perspective on prediction. Sign languages involve relatively long transition phases between one sign and the next, which should be anticipated as part of a forward model-based prediction even though they are semantically empty. Native speakers of DGS watched videos of naturally signed DGS sentences which either ended with an expected or a (semantically) unexpected sign. Unexpected signs engendered a biphasic N400-late positivity pattern. Crucially, N400 onset preceded critical sign onset and was thus clearly elicited by properties of the transition phase. The comprehension system thereby clearly anticipated modality-specific information about the realization of the predicted semantic item. These results provide strong converging support for the application of forward models in language comprehension. © 2013 Elsevier Ltd. All rights reserved.
Mathematical modelling of NO emissions from high-temperature air combustion with nitrous oxide mechanism

International Nuclear Information System (INIS)

Yang, Weihong; Blasiak, Wlodzimierz

2005-01-01

A study of the mathematical modelling of NO formation and emissions in a gas-fired regenerative furnace with high-preheated air was performed. The model of NO formation via N 2 O-intermediate mechanism was proposed because of the lower flame temperature in this case. The reaction rates of this new model were calculated basing on the eddy-dissipation-concept. This model accompanied with thermal-NO, prompt-NO and NO reburning models were used to predict NO emissions and formations. The sensitivity of the furnace temperature and the oxygen availability on NO generation rate has been investigated. The predicted results were compared with experimental values. The results show that NO emission formed by N 2 O-intermediate mechanism is of outstanding importance during the high-temperature air combustion (HiTAC) condition. Furthermore, it shows that NO models with N 2 O-route model can give more reasonable profile of NO formation. Additionally, increasing excess air ratio leads to increasing of NO emission in the regenerative furnace. (author)
Thermo-hydro mechanical modeling in unsaturated hard clay: application to nuclear waste storage

International Nuclear Information System (INIS)

Jia, Y.

2006-07-01

This work presents an elastoplastic damage model for argillite in unsaturated conditions. A short resume of experimental investigations is presented in the first part. The results obtained show an important plastic deformation coupled with damage induced by initiation and growth of microcracks. Influences of water content on the mechanical behaviour are also investigated. Based on experimental data and micro-mechanical considerations, a general constitutive model is proposed for the poro-mechanical behavior of argillite in unsaturated conditions. The time dependent creep has also been incorporated in they model. The performance of the model is examined by comparing numerical simulation with experimental data in various load paths under saturated and unsaturated conditions. Finally, the model is applied to hydro-mechanical coupling study of the REP experiment and thermo-hydro-mechanical coupling study of the HE-D experiment. A good agreement is obtained between experimental data and numerical predictions. It has been shown that the proposed model describe correctly the main features of the mechanical behaviour of unsaturated rocks. (author)
Predictive Modeling in Race Walking

Directory of Open Access Journals (Sweden)

Krzysztof Wiktorowicz

2015-01-01

Full Text Available This paper presents the use of linear and nonlinear multivariable models as tools to support training process of race walkers. These models are calculated using data collected from race walkers’ training events and they are used to predict the result over a 3 km race based on training loads. The material consists of 122 training plans for 21 athletes. In order to choose the best model leave-one-out cross-validation method is used. The main contribution of the paper is to propose the nonlinear modifications for linear models in order to achieve smaller prediction error. It is shown that the best model is a modified LASSO regression with quadratic terms in the nonlinear part. This model has the smallest prediction error and simplified structure by eliminating some of the predictors.
Predicting growth of the healthy infant using a genome scale metabolic model.

Science.gov (United States)

Nilsson, Avlant; Mardinoglu, Adil; Nielsen, Jens

2017-01-01

An estimated 165 million children globally have stunted growth, and extensive growth data are available. Genome scale metabolic models allow the simulation of molecular flux over each metabolic enzyme, and are well adapted to analyze biological systems. We used a human genome scale metabolic model to simulate the mechanisms of growth and integrate data about breast-milk intake and composition with the infant's biomass and energy expenditure of major organs. The model predicted daily metabolic fluxes from birth to age 6 months, and accurately reproduced standard growth curves and changes in body composition. The model corroborates the finding that essential amino and fatty acids do not limit growth, but that energy is the main growth limiting factor. Disruptions to the supply and demand of energy markedly affected the predicted growth, indicating that elevated energy expenditure may be detrimental. The model was used to simulate the metabolic effect of mineral deficiencies, and showed the greatest growth reduction for deficiencies in copper, iron, and magnesium ions which affect energy production through oxidative phosphorylation. The model and simulation method were integrated to a platform and shared with the research community. The growth model constitutes another step towards the complete representation of human metabolism, and may further help improve the understanding of the mechanisms underlying stunting.
Adding propensity scores to pure prediction models fails to improve predictive performance

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Amy S. Nowacki

2013-08-01

Full Text Available Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to the lack of differentiation regarding the goals of predictive modeling versus causal inference modeling. Therefore, the purpose of this study is to formally examine the effect of propensity scores on predictive performance. Our hypothesis is that a multivariable regression model that adjusts for all covariates will perform as well as or better than those models utilizing propensity scores with respect to model discrimination and calibration.Methods. The most commonly encountered statistical scenarios for medical prediction (logistic and proportional hazards regression were used to investigate this research question. Random cross-validation was performed 500 times to correct for optimism. The multivariable regression models adjusting for all covariates were compared with models that included adjustment for or weighting with the propensity scores. The methods were compared based on three predictive performance measures: (1 concordance indices; (2 Brier scores; and (3 calibration curves.Results. Multivariable models adjusting for all covariates had the highest average concordance index, the lowest average Brier score, and the best calibration. Propensity score adjustment and inverse probability weighting models without adjustment for all covariates performed worse than full models and failed to improve predictive performance with full covariate adjustment.Conclusion. Propensity score techniques did not improve prediction performance measures beyond multivariable adjustment. Propensity scores are not recommended if the analytical goal is pure prediction modeling.
Using Quantum Mechanics to Predict Shock Properties of Explosives

National Research Council Canada - National Science Library

Romero, N. A; Mattson, W. D; Rice, B. M

2006-01-01

.... As little as ten years ago, quantum mechanical calculations were restricted to predictions of static properties of systems containing tens of atoms, thus limiting first principles explorations to gas...
A mathematical model for describing the mechanical behaviour of root canal instruments.

Science.gov (United States)

Zhang, E W; Cheung, G S P; Zheng, Y F

2011-01-01

The purpose of this study was to establish a general mathematical model for describing the mechanical behaviour of root canal instruments by combining a theoretical analytical approach with a numerical finite-element method. Mathematical formulas representing the longitudinal (taper, helical angle and pitch) and cross-sectional configurations and area, the bending and torsional inertia, the curvature of the boundary point and the (geometry of) loading condition were derived. Torsional and bending stresses and the resultant deformation were expressed mathematically as a function of these geometric parameters, modulus of elasticity of the material and the applied load. As illustrations, three brands of NiTi endodontic files of different cross-sectional configurations (ProTaper, Hero 642, and Mani NRT) were analysed under pure torsion and pure bending situation by entering the model into a finite-element analysis package (ANSYS). Numerical results confirmed that mathematical models were a feasible method to analyse the mechanical properties and predict the stress and deformation for root canal instruments during root canal preparation. Mathematical and numerical model can be a suitable way to examine mechanical behaviours as a criterion of the instrument design and to predict the stress and strain experienced by the endodontic instruments during root canal preparation. © 2010 International Endodontic Journal.

Modeling and prediction of human word search behavior in interactive machine translation

Science.gov (United States)

Ji, Duo; Yu, Bai; Ma, Bin; Ye, Na

2017-12-01

As a kind of computer aided translation method, Interactive Machine Translation technology reduced manual translation repetitive and mechanical operation through a variety of methods, so as to get the translation efficiency, and played an important role in the practical application of the translation work. In this paper, we regarded the behavior of users' frequently searching for words in the translation process as the research object, and transformed the behavior to the translation selection problem under the current translation. The paper presented a prediction model, which is a comprehensive utilization of alignment model, translation model and language model of the searching words behavior. It achieved a highly accurate prediction of searching words behavior, and reduced the switching of mouse and keyboard operations in the users' translation process.
Numerical modelling in non linear fracture mechanics

Directory of Open Access Journals (Sweden)

Viggo Tvergaard

2007-07-01

Full Text Available Some numerical studies of crack propagation are based on using constitutive models that accountfor damage evolution in the material. When a critical damage value has been reached in a materialpoint, it is natural to assume that this point has no more carrying capacity, as is done numerically in the elementvanish technique. In the present review this procedure is illustrated for micromechanically based materialmodels, such as a ductile failure model that accounts for the nucleation and growth of voids to coalescence, and a model for intergranular creep failure with diffusive growth of grain boundary cavities leading to micro-crack formation. The procedure is also illustrated for low cycle fatigue, based on continuum damage mechanics. In addition, the possibility of crack growth predictions for elastic-plastic solids using cohesive zone models to represent the fracture process is discussed.
Gambling and the Reasoned Action Model: Predicting Past Behavior, Intentions, and Future Behavior.

Science.gov (United States)

Dahl, Ethan; Tagler, Michael J; Hohman, Zachary P

2018-03-01

Gambling is a serious concern for society because it is highly addictive and is associated with a myriad of negative outcomes. The current study applied the Reasoned Action Model (RAM) to understand and predict gambling intentions and behavior. Although prior studies have taken a reasoned action approach to understand gambling, no prior study has fully applied the RAM or used the RAM to predict future gambling. Across two studies the RAM was used to predict intentions to gamble, past gambling behavior, and future gambling behavior. In study 1 the model significantly predicted intentions and past behavior in both a college student and Amazon Mechanical Turk sample. In study 2 the model predicted future gambling behavior, measured 2 weeks after initial measurement of the RAM constructs. This study stands as the first to show the utility of the RAM in predicting future gambling behavior. Across both studies, attitudes and perceived normative pressure were the strongest predictors of intentions to gamble. These findings provide increased understanding of gambling and inform the development of gambling interventions based on the RAM.
Theoretical model for cavitation erosion prediction in centrifugal pump impeller

International Nuclear Information System (INIS)

Rayan, M.A.; Mahgob, M.M.; Mostafa, N.H.

1990-01-01

Cavitation is known to have great effects on pump hydraulic and mechanical characteristics. These effects are mainly described by deviation in pump performance, increasing vibration and noise level as well as erosion of blade and casing materials. In the present work, only the hydrodynamic aspect of cavitation was considered. The efforts were directed toward the study of cavitation inception, cavity mechanics and material erosion in order to clarify the macrohydrodynamic aspects of cavitation erosive wear in real machines. As a result of this study, it was found that cavitation damage can be predicted from model data. The obtained theoretical results show good agreement with the experimental results obtained in this investigation and with results of some other investigations. The application of the findings of this work will help the design engineer in predicting the erosion rate, according to the different operating conditions. (author)
Time Prediction Models for Echinococcosis Based on Gray System Theory and Epidemic Dynamics.

Science.gov (United States)

Zhang, Liping; Wang, Li; Zheng, Yanling; Wang, Kai; Zhang, Xueliang; Zheng, Yujian

2017-03-04

Echinococcosis, which can seriously harm human health and animal husbandry production, has become an endemic in the Xinjiang Uygur Autonomous Region of China. In order to explore an effective human Echinococcosis forecasting model in Xinjiang, three grey models, namely, the traditional grey GM(1,1) model, the Grey-Periodic Extensional Combinatorial Model (PECGM(1,1)), and the Modified Grey Model using Fourier Series (FGM(1,1)), in addition to a multiplicative seasonal ARIMA(1,0,1)(1,1,0)₄ model, are applied in this study for short-term predictions. The accuracy of the different grey models is also investigated. The simulation results show that the FGM(1,1) model has a higher performance ability, not only for model fitting, but also for forecasting. Furthermore, considering the stability and the modeling precision in the long run, a dynamic epidemic prediction model based on the transmission mechanism of Echinococcosis is also established for long-term predictions. Results demonstrate that the dynamic epidemic prediction model is capable of identifying the future tendency. The number of human Echinococcosis cases will increase steadily over the next 25 years, reaching a peak of about 1250 cases, before eventually witnessing a slow decline, until it finally ends.
Time Prediction Models for Echinococcosis Based on Gray System Theory and Epidemic Dynamics

Directory of Open Access Journals (Sweden)

Liping Zhang

2017-03-01

Full Text Available Echinococcosis, which can seriously harm human health and animal husbandry production, has become an endemic in the Xinjiang Uygur Autonomous Region of China. In order to explore an effective human Echinococcosis forecasting model in Xinjiang, three grey models, namely, the traditional grey GM(1,1 model, the Grey-Periodic Extensional Combinatorial Model (PECGM(1,1, and the Modified Grey Model using Fourier Series (FGM(1,1, in addition to a multiplicative seasonal ARIMA(1,0,1(1,1,04 model, are applied in this study for short-term predictions. The accuracy of the different grey models is also investigated. The simulation results show that the FGM(1,1 model has a higher performance ability, not only for model fitting, but also for forecasting. Furthermore, considering the stability and the modeling precision in the long run, a dynamic epidemic prediction model based on the transmission mechanism of Echinococcosis is also established for long-term predictions. Results demonstrate that the dynamic epidemic prediction model is capable of identifying the future tendency. The number of human Echinococcosis cases will increase steadily over the next 25 years, reaching a peak of about 1250 cases, before eventually witnessing a slow decline, until it finally ends.
Nonlinear chaotic model for predicting storm surges

Directory of Open Access Journals (Sweden)

M. Siek

2010-09-01

Full Text Available This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables. We implemented the univariate and multivariate chaotic models with direct and multi-steps prediction techniques and optimized these models using an exhaustive search method. The built models were tested for predicting storm surge dynamics for different stormy conditions in the North Sea, and are compared to neural network models. The results show that the chaotic models can generally provide reliable and accurate short-term storm surge predictions.
Modelling heat and mass transfer in bread baking with mechanical deformation

International Nuclear Information System (INIS)

Nicolas, V; Glouannec, P; Ploteau, J-P; Salagnac, P; Jury, V; Boillereaux, L

2012-01-01

In this paper, the thermo-hydric behaviour of bread during baking is studied. A numerical model has been developed with Comsol Multiphysics© software. The model takes into account the heat and mass transfers in the bread and the phenomenon of swelling. This model predicts the evolution of temperature, moisture, gas pressure and deformation in French 'baguette' during baking. Local deformation is included in equations using solid phase conservation and, global deformation is calculated using a viscous mechanic model. Boundary conditions are specified with the sole temperature model and vapour pressure estimation of the oven during baking. The model results are compared with experimental data for a classic baking. Then, the model is analysed according to physical properties of bread and solicitations for a better understanding of the interactions between different mechanisms within the porous matrix.
Finite element modelling of cornea mechanics: a review

Directory of Open Access Journals (Sweden)

Talisa Mohammad Nejad

2014-01-01

Full Text Available The cornea is a transparent tissue in front of the eye that refracts light and facilitates vision. A slight change in the geometry of the cornea remarkably affects the optical power. Because of this sensitivity, biomechanical study of the cornea can reveal much about its performance and function. In vivo and in vitro studies have been conducted to investigate the mechanics of the cornea and determine its characteristics. Numerical techniques such as the finite element method (FEM have been extensively implemented as effective and noninvasive methods for analyzing corneal mechanics and possible disorders. This article reviews the use of FEM for assessing the mechanical behavior of the cornea. Different applications of FEM in corneal disease studies, surgical predictions, impact simulations, and clinical applications have been reviewed. Some suggestions for the future of this type of modeling in the area of corneal mechanics are also discussed.
A simplified model of a mechanical cooling tower with both a fill pack and a coil

Science.gov (United States)

Van Riet, Freek; Steenackers, Gunther; Verhaert, Ivan

2017-11-01

Cooling accounts for a large amount of the global primary energy consumption in buildings and industrial processes. A substantial part of this cooling demand is produced by mechanical cooling towers. Simulations benefit the sizing and integration of cooling towers in overall cooling networks. However, for these simulations fast-to-calculate and easy-to-parametrize models are required. In this paper, a new model is developed for a mechanical draught cooling tower with both a cooling coil and a fill pack. The model needs manufacturers' performance data at only three operational states (at varying air and water flow rates) to be parametrized. The model predicts the cooled, outgoing water temperature. These predictions were compared with experimental data for a wide range of operational states. The model was able to predict the temperature with a maximum absolute error of 0.59°C. The relative error of cooling capacity was mostly between ±5%.
Mechanics, Mechanobiology, and Modeling of Human Abdominal Aorta and Aneurysms

Science.gov (United States)

Humphrey, J.D.; Holzapfel, G.A.

2011-01-01

Biomechanical factors play fundamental roles in the natural history of abdominal aortic aneurysms (AAAs) and their responses to treatment. Advances during the past two decades have increased our understanding of the mechanics and biology of the human abdominal aorta and AAAs, yet there remains a pressing need for considerable new data and resulting patient-specific computational models that can better describe the current status of a lesion and better predict the evolution of lesion geometry, composition, and material properties and thereby improve interventional planning. In this paper, we briefly review data on the structure and function of the human abdominal aorta and aneurysmal wall, past models of the mechanics, and recent growth and remodeling models. We conclude by identifying open problems that we hope will motivate studies to improve our computational modeling and thus general understanding of AAAs. PMID:22189249
Extracting falsifiable predictions from sloppy models.

Science.gov (United States)

Gutenkunst, Ryan N; Casey, Fergal P; Waterfall, Joshua J; Myers, Christopher R; Sethna, James P

2007-12-01

Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of data that best constrain model predictions, makes linear uncertainty approximations dangerous, and introduces computational difficulties in Monte-Carlo uncertainty analysis. We also present a useful test problem and suggest refinements to the standards by which models are communicated.
A Combined Cooperative Braking Model with a Predictive Control Strategy in an Electric Vehicle

Directory of Open Access Journals (Sweden)

Hongqiang Guo

2013-12-01

Full Text Available Cooperative braking with regenerative braking and mechanical braking plays an important role in electric vehicles for energy-saving control. Based on the parallel and the series cooperative braking models, a combined model with a predictive control strategy to get a better cooperative braking performance is presented. The balance problem between the maximum regenerative energy recovery efficiency and the optimum braking stability is solved through an off-line process optimization stream with the collaborative optimization algorithm (CO. To carry out the process optimization stream, the optimal Latin hypercube design (Opt LHD is presented to discrete the continuous design space. To solve the poor real-time problem of the optimization, a high-precision predictive model based on the off-line optimization data of the combined model is built, and a predictive control strategy is proposed and verified through simulation. The simulation results demonstrate that the predictive control strategy and the combined model are reasonable and effective.
A fracture mechanics model for iodine stress corrosion crack propagation in Zircaloy tubing

International Nuclear Information System (INIS)

Crescimanno, P.J.; Campbell, W.R.; Goldberg, I.

1984-01-01

A fracture mechanics model is presented for iodine-induced stress corrosion cracking in Zircaloy tubing. The model utilizes a power law to relate crack extension velocity to stress intensity factor, a hyperbolic tangent function for the influence of iodine concentration, and an exponential function for the influence of temperature and material strength. Comparisons of predicted to measured failure times show that predicted times are within a factor of two of the measured times for a majority of the specimens considered
Researches of fruit quality prediction model based on near infrared spectrum

Science.gov (United States)

Shen, Yulin; Li, Lian

2018-04-01

With the improvement in standards for food quality and safety, people pay more attention to the internal quality of fruits, therefore the measurement of fruit internal quality is increasingly imperative. In general, nondestructive soluble solid content (SSC) and total acid content (TAC) analysis of fruits is vital and effective for quality measurement in global fresh produce markets, so in this paper, we aim at establishing a novel fruit internal quality prediction model based on SSC and TAC for Near Infrared Spectrum. Firstly, the model of fruit quality prediction based on PCA + BP neural network, PCA + GRNN network, PCA + BP adaboost strong classifier, PCA + ELM and PCA + LS_SVM classifier are designed and implemented respectively; then, in the NSCT domain, the median filter and the SavitzkyGolay filter are used to preprocess the spectral signal, Kennard-Stone algorithm is used to automatically select the training samples and test samples; thirdly, we achieve the optimal models by comparing 15 kinds of prediction model based on the theory of multi-classifier competition mechanism, specifically, the non-parametric estimation is introduced to measure the effectiveness of proposed model, the reliability and variance of nonparametric estimation evaluation of each prediction model to evaluate the prediction result, while the estimated value and confidence interval regard as a reference, the experimental results demonstrate that this model can better achieve the optimal evaluation of the internal quality of fruit; finally, we employ cat swarm optimization to optimize two optimal models above obtained from nonparametric estimation, empirical testing indicates that the proposed method can provide more accurate and effective results than other forecasting methods.
Fixed recurrence and slip models better predict earthquake behavior than the time- and slip-predictable models 1: repeating earthquakes

Science.gov (United States)

Rubinstein, Justin L.; Ellsworth, William L.; Chen, Kate Huihsuan; Uchida, Naoki

2012-01-01

The behavior of individual events in repeating earthquake sequences in California, Taiwan and Japan is better predicted by a model with fixed inter-event time or fixed slip than it is by the time- and slip-predictable models for earthquake occurrence. Given that repeating earthquakes are highly regular in both inter-event time and seismic moment, the time- and slip-predictable models seem ideally suited to explain their behavior. Taken together with evidence from the companion manuscript that shows similar results for laboratory experiments we conclude that the short-term predictions of the time- and slip-predictable models should be rejected in favor of earthquake models that assume either fixed slip or fixed recurrence interval. This implies that the elastic rebound model underlying the time- and slip-predictable models offers no additional value in describing earthquake behavior in an event-to-event sense, but its value in a long-term sense cannot be determined. These models likely fail because they rely on assumptions that oversimplify the earthquake cycle. We note that the time and slip of these events is predicted quite well by fixed slip and fixed recurrence models, so in some sense they are time- and slip-predictable. While fixed recurrence and slip models better predict repeating earthquake behavior than the time- and slip-predictable models, we observe a correlation between slip and the preceding recurrence time for many repeating earthquake sequences in Parkfield, California. This correlation is not found in other regions, and the sequences with the correlative slip-predictable behavior are not distinguishable from nearby earthquake sequences that do not exhibit this behavior.
EFFICIENT PREDICTIVE MODELLING FOR ARCHAEOLOGICAL RESEARCH

OpenAIRE

Balla, A.; Pavlogeorgatos, G.; Tsiafakis, D.; Pavlidis, G.

2014-01-01

The study presents a general methodology for designing, developing and implementing predictive modelling for identifying areas of archaeological interest. The methodology is based on documented archaeological data and geographical factors, geospatial analysis and predictive modelling, and has been applied to the identification of possible Macedonian tombs’ locations in Northern Greece. The model was tested extensively and the results were validated using a commonly used predictive gain, which...
Modeling and evaluating of surface roughness prediction in micro-grinding on soda-lime glass considering tool characterization

Science.gov (United States)

Cheng, Jun; Gong, Yadong; Wang, Jinsheng

2013-11-01

The current research of micro-grinding mainly focuses on the optimal processing technology for different materials. However, the material removal mechanism in micro-grinding is the base of achieving high quality processing surface. Therefore, a novel method for predicting surface roughness in micro-grinding of hard brittle materials considering micro-grinding tool grains protrusion topography is proposed in this paper. The differences of material removal mechanism between convention grinding process and micro-grinding process are analyzed. Topography characterization has been done on micro-grinding tools which are fabricated by electroplating. Models of grain density generation and grain interval are built, and new predicting model of micro-grinding surface roughness is developed. In order to verify the precision and application effect of the surface roughness prediction model proposed, a micro-grinding orthogonally experiment on soda-lime glass is designed and conducted. A series of micro-machining surfaces which are 78 nm to 0.98 μm roughness of brittle material is achieved. It is found that experimental roughness results and the predicting roughness data have an evident coincidence, and the component variable of describing the size effects in predicting model is calculated to be 1.5×107 by reverse method based on the experimental results. The proposed model builds a set of distribution to consider grains distribution densities in different protrusion heights. Finally, the characterization of micro-grinding tools which are used in the experiment has been done based on the distribution set. It is concluded that there is a significant coincidence between surface prediction data from the proposed model and measurements from experiment results. Therefore, the effectiveness of the model is demonstrated. This paper proposes a novel method for predicting surface roughness in micro-grinding of hard brittle materials considering micro-grinding tool grains protrusion
Spatial Economics Model Predicting Transport Volume

Directory of Open Access Journals (Sweden)

Lu Bo

2016-10-01

Full Text Available It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.
Evaluation of reduced chemical kinetic mechanisms used for modeling mild combustion for natural gas

Directory of Open Access Journals (Sweden)

Hamdi Mohamed

2009-01-01

Full Text Available A numerical and parametric study was performed to evaluate the potential of reduced chemistry mechanisms to model natural gas chemistry including NOx chemistry under mild combustion mode. Two reduced mechanisms, 5-step and 9-step, were tested against the GRI-Mech3.0 by comparing key species, such as NOx, CO2 and CO, and gas temperature predictions in idealized reactors codes under mild combustion conditions. It is thus concluded that the 9-step mechanism appears to be a promising reduced mechanism that can be used in multi-dimensional codes for modeling mild combustion of natural gas.

Predicting Eating Disorder Group Membership: An Examination and Extension of the Sociocultural Model

Science.gov (United States)

Engler, Patricia A.; Crowther, Janis H.; Dalton, Ginnie; Sanftner, Jennifer L.

2006-01-01

The purpose of this research was to examine and extend portions of the sociocultural model of bulimia nervosa (Stice, E. (1994). Review of the evidence for a sociocultural model of bulimia nervosa and an exploration of the mechanisms of action. "Clinical Psychology Review," 14, 633-661; Stice, E., & Agras, W. S. (1998). Predicting onset and…
Estimation of the physiological mechanical conditioning in vascular tissue engineering by a predictive fluid-structure interaction approach.

Science.gov (United States)

Tresoldi, Claudia; Bianchi, Elena; Pellegata, Alessandro Filippo; Dubini, Gabriele; Mantero, Sara

2017-08-01

The in vitro replication of physiological mechanical conditioning through bioreactors plays a crucial role in the development of functional Small-Caliber Tissue-Engineered Blood Vessels. An in silico scaffold-specific model under pulsatile perfusion provided by a bioreactor was implemented using a fluid-structure interaction (FSI) approach for viscoelastic tubular scaffolds (e.g. decellularized swine arteries, DSA). Results of working pressures, circumferential deformations, and wall shear stress on DSA fell within the desired physiological range and indicated the ability of this model to correctly predict the mechanical conditioning acting on the cells-scaffold system. Consequently, the FSI model allowed us to a priori define the stimulation pattern, driving in vitro physiological maturation of scaffolds, especially with viscoelastic properties.
Prediction Of Abrasive And Diffusive Tool Wear Mechanisms In Machining

Science.gov (United States)

Rizzuti, S.; Umbrello, D.

2011-01-01

Tool wear prediction is regarded as very important task in order to maximize tool performance, minimize cutting costs and improve the quality of workpiece in cutting. In this research work, an experimental campaign was carried out at the varying of cutting conditions with the aim to measure both crater and flank tool wear, during machining of an AISI 1045 with an uncoated carbide tool P40. Parallel a FEM-based analysis was developed in order to study the tool wear mechanisms, taking also into account the influence of the cutting conditions and the temperature reached on the tool surfaces. The results show that, when the temperature of the tool rake surface is lower than the activation temperature of the diffusive phenomenon, the wear rate can be estimated applying an abrasive model. In contrast, in the tool area where the temperature is higher than the diffusive activation temperature, the wear rate can be evaluated applying a diffusive model. Finally, for a temperature ranges within the above cited values an adopted abrasive-diffusive wear model furnished the possibility to correctly evaluate the tool wear phenomena.
Addressing issues associated with evaluating prediction models for survival endpoints based on the concordance statistic.

Science.gov (United States)

Wang, Ming; Long, Qi

2016-09-01

Prediction models for disease risk and prognosis play an important role in biomedical research, and evaluating their predictive accuracy in the presence of censored data is of substantial interest. The standard concordance (c) statistic has been extended to provide a summary measure of predictive accuracy for survival models. Motivated by a prostate cancer study, we address several issues associated with evaluating survival prediction models based on c-statistic with a focus on estimators using the technique of inverse probability of censoring weighting (IPCW). Compared to the existing work, we provide complete results on the asymptotic properties of the IPCW estimators under the assumption of coarsening at random (CAR), and propose a sensitivity analysis under the mechanism of noncoarsening at random (NCAR). In addition, we extend the IPCW approach as well as the sensitivity analysis to high-dimensional settings. The predictive accuracy of prediction models for cancer recurrence after prostatectomy is assessed by applying the proposed approaches. We find that the estimated predictive accuracy for the models in consideration is sensitive to NCAR assumption, and thus identify the best predictive model. Finally, we further evaluate the performance of the proposed methods in both settings of low-dimensional and high-dimensional data under CAR and NCAR through simulations. © 2016, The International Biometric Society.
Quantum Mechanics/Molecular Mechanics Modeling of Drug Metabolism

DEFF Research Database (Denmark)

Lonsdale, Richard; Fort, Rachel M; Rydberg, Patrik

2016-01-01

)-mexiletine in CYP1A2 with hybrid quantum mechanics/molecular mechanics (QM/MM) methods, providing a more detailed and realistic model. Multiple reaction barriers have been calculated at the QM(B3LYP-D)/MM(CHARMM27) level for the direct N-oxidation and H-abstraction/rebound mechanisms. Our calculated barriers......The mechanism of cytochrome P450(CYP)-catalyzed hydroxylation of primary amines is currently unclear and is relevant to drug metabolism; previous small model calculations have suggested two possible mechanisms: direct N-oxidation and H-abstraction/rebound. We have modeled the N-hydroxylation of (R...... indicate that the direct N-oxidation mechanism is preferred and proceeds via the doublet spin state of Compound I. Molecular dynamics simulations indicate that the presence of an ordered water molecule in the active site assists in the binding of mexiletine in the active site...
Prediction of Fracture Behavior in Rock and Rock-like Materials Using Discrete Element Models

Science.gov (United States)

Katsaga, T.; Young, P.

2009-05-01

The study of fracture initiation and propagation in heterogeneous materials such as rock and rock-like materials are of principal interest in the field of rock mechanics and rock engineering. It is crucial to study and investigate failure prediction and safety measures in civil and mining structures. Our work offers a practical approach to predict fracture behaviour using discrete element models. In this approach, the microstructures of materials are presented through the combination of clusters of bonded particles with different inter-cluster particle and bond properties, and intra-cluster bond properties. The geometry of clusters is transferred from information available from thin sections, computed tomography (CT) images and other visual presentation of the modeled material using customized AutoCAD built-in dialog- based Visual Basic Application. Exact microstructures of the tested sample, including fractures, faults, inclusions and void spaces can be duplicated in the discrete element models. Although the microstructural fabrics of rocks and rock-like structures may have different scale, fracture formation and propagation through these materials are alike and will follow similar mechanics. Synthetic material provides an excellent condition for validating the modelling approaches, as fracture behaviours are known with the well-defined composite's properties. Calibration of the macro-properties of matrix material and inclusions (aggregates), were followed with the overall mechanical material responses calibration by adjusting the interfacial properties. The discrete element model predicted similar fracture propagation features and path as that of the real sample material. The path of the fractures and matrix-inclusion interaction was compared using computed tomography images. Initiation and fracture formation in the model and real material were compared using Acoustic Emission data. Analysing the temporal and spatial evolution of AE events, collected during the
In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches

Energy Technology Data Exchange (ETDEWEB)

Singh, Kunwar P., E-mail: kpsingh_52@yahoo.com; Gupta, Shikha

2014-03-15

Ensemble learning approach based decision treeboost (DTB) and decision tree forest (DTF) models are introduced in order to establish quantitative structure–toxicity relationship (QSTR) for the prediction of toxicity of 1450 diverse chemicals. Eight non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals was evaluated using Tanimoto similarity index. Stochastic gradient boosting and bagging algorithms supplemented DTB and DTF models were constructed for classification and function optimization problems using the toxicity end-point in T. pyriformis. Special attention was drawn to prediction ability and robustness of the models, investigated both in external and 10-fold cross validation processes. In complete data, optimal DTB and DTF models rendered accuracies of 98.90%, 98.83% in two-category and 98.14%, 98.14% in four-category toxicity classifications. Both the models further yielded classification accuracies of 100% in external toxicity data of T. pyriformis. The constructed regression models (DTB and DTF) using five descriptors yielded correlation coefficients (R{sup 2}) of 0.945, 0.944 between the measured and predicted toxicities with mean squared errors (MSEs) of 0.059, and 0.064 in complete T. pyriformis data. The T. pyriformis regression models (DTB and DTF) applied to the external toxicity data sets yielded R{sup 2} and MSE values of 0.637, 0.655; 0.534, 0.507 (marine bacteria) and 0.741, 0.691; 0.155, 0.173 (algae). The results suggest for wide applicability of the inter-species models in predicting toxicity of new chemicals for regulatory purposes. These approaches provide useful strategy and robust tools in the screening of ecotoxicological risk or environmental hazard potential of chemicals. - Graphical abstract: Importance of input variables in DTB and DTF classification models for (a) two-category, and (b) four-category toxicity intervals in T. pyriformis data. Generalization and predictive abilities of the
In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches

International Nuclear Information System (INIS)

Singh, Kunwar P.; Gupta, Shikha

2014-01-01

Ensemble learning approach based decision treeboost (DTB) and decision tree forest (DTF) models are introduced in order to establish quantitative structure–toxicity relationship (QSTR) for the prediction of toxicity of 1450 diverse chemicals. Eight non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals was evaluated using Tanimoto similarity index. Stochastic gradient boosting and bagging algorithms supplemented DTB and DTF models were constructed for classification and function optimization problems using the toxicity end-point in T. pyriformis. Special attention was drawn to prediction ability and robustness of the models, investigated both in external and 10-fold cross validation processes. In complete data, optimal DTB and DTF models rendered accuracies of 98.90%, 98.83% in two-category and 98.14%, 98.14% in four-category toxicity classifications. Both the models further yielded classification accuracies of 100% in external toxicity data of T. pyriformis. The constructed regression models (DTB and DTF) using five descriptors yielded correlation coefficients (R 2 ) of 0.945, 0.944 between the measured and predicted toxicities with mean squared errors (MSEs) of 0.059, and 0.064 in complete T. pyriformis data. The T. pyriformis regression models (DTB and DTF) applied to the external toxicity data sets yielded R 2 and MSE values of 0.637, 0.655; 0.534, 0.507 (marine bacteria) and 0.741, 0.691; 0.155, 0.173 (algae). The results suggest for wide applicability of the inter-species models in predicting toxicity of new chemicals for regulatory purposes. These approaches provide useful strategy and robust tools in the screening of ecotoxicological risk or environmental hazard potential of chemicals. - Graphical abstract: Importance of input variables in DTB and DTF classification models for (a) two-category, and (b) four-category toxicity intervals in T. pyriformis data. Generalization and predictive abilities of the
Multiscale mechanics of TRIP-assisted multiphase steels: II. Micromechanical modelling

International Nuclear Information System (INIS)

Lani, F.; Furnemont, Q.; Van Rompaey, T.; Delannay, F.; Jacques, P.J.; Pardoen, T.

2007-01-01

The stress and strain partitioning between the different phases of transformation-induced plasticity (TRIP)-aided multiphase steels is evaluated using a mean field homogenization approach. The change of the austenite volume fraction under straining is predicted using a micromechanics-based criterion for the martensitic transformation adapted to the case of small, isolated, transforming austenite grains. The parameters of the model are identified from the mechanical response and transformation kinetics measured under uniaxial tension for two steels differing essentially by the austenite stability. The model is validated by comparing the predictions with tests performed under different loading conditions: pure shear, intermediate biaxial and equibiaxial. An analysis of the effect of the austenite stability on strength and ductility provides guidelines for optimizing properties according to the stress state
Rolling force prediction for strip casting using theoretical model and artificial intelligence

Institute of Scientific and Technical Information of China (English)

CAO Guang-ming; LI Cheng-gang; ZHOU Guo-ping; LIU Zhen-yu; WU Di; WANG Guo-dong; LIU Xiang-hua

2010-01-01

Rolling force for strip casting of 1Cr17 ferritic stainless steel was predicted using theoretical model and artificial intelligence.Solution zone was classified into two parts by kiss point position during casting strip.Navier-Stokes equation in fluid mechanics and stream function were introduced to analyze the rheological property of liquid zone and mushy zone,and deduce the analytic equation of unit compression stress distribution.The traditional hot rolling model was still used in the solid zone.Neural networks based on feedforward training algorithm in Bayesian regularization were introduced to build model for kiss point position.The results show that calculation accuracy for verification data of 94.67% is in the range of+7.0%,which indicates that the predicting accuracy of this model is very high.
Modeling and Analysis of Mechanical Properties of Aluminium Alloy (A413 Processed through Squeeze Casting Route Using Artificial Neural Network Model and Statistical Technique

Directory of Open Access Journals (Sweden)

R. Soundararajan

2015-01-01

Full Text Available Artificial Neural Network (ANN approach was used for predicting and analyzing the mechanical properties of A413 aluminum alloy produced by squeeze casting route. The experiments are carried out with different controlled input variables such as squeeze pressure, die preheating temperature, and melt temperature as per Full Factorial Design (FFD. The accounted absolute process variables produce a casting with pore-free and ideal fine grain dendritic structure resulting in good mechanical properties such as hardness, ultimate tensile strength, and yield strength. As a primary objective, a feed forward back propagation ANN model has been developed with different architectures for ensuring the definiteness of the values. The developed model along with its predicted data was in good agreement with the experimental data, inferring the valuable performance of the optimal model. From the work it was ascertained that, for castings produced by squeeze casting route, the ANN is an alternative method for predicting the mechanical properties and appropriate results can be estimated rather than measured, thereby reducing the testing time and cost. As a secondary objective, quantitative and statistical analysis was performed in order to evaluate the effect of process parameters on the mechanical properties of the castings.
Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

Directory of Open Access Journals (Sweden)

Jing Lu

2014-11-01

Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.
Mechanical model for filament buckling and growth by phase ordering.

Science.gov (United States)

Rey, Alejandro D; Abukhdeir, Nasser M

2008-02-05

A mechanical model of open filament shape and growth driven by phase ordering is formulated. For a given phase-ordering driving force, the model output is the filament shape evolution and the filament end-point kinematics. The linearized model for the slope of the filament is the Cahn-Hilliard model of spinodal decomposition, where the buckling corresponds to concentration fluctuations. Two modes are predicted: (i) sequential growth and buckling and (ii) simultaneous buckling and growth. The relation among the maximum buckling rate, filament tension, and matrix viscosity is given. These results contribute to ongoing work in smectic A filament buckling.
Early Family Relationships Predict Children’s Emotion Regulation and Defense Mechanisms

Directory of Open Access Journals (Sweden)

Jallu Lindblom

2016-12-01

Full Text Available Early family relationships have been suggested to influence the development of children’s affect regulation, involving both emotion regulation and defense mechanisms. However, we lack research on the specific family predictors for these two forms of affect regulation, which have been conceptualized to differ in their functions and accessibility to consciousness. Accordingly, we examine how the (a quality and (b timing of family relationships during infancy predict child’s later emotion regulation and defense mechanisms. Parents (N = 703 reported autonomy and intimacy in marital and parenting relationships at the child’s ages of 2 and 12 months, and the child’s use of emotion regulation and immature and neurotic defenses at 7 to 8 years. As hypothesized, the results showed that functional early family relationships predicted children’s efficient emotion regulation, whereas dysfunctional relationships predicted reliance on defense mechanisms in middle childhood. Further, results showed a timing effect for neurotic defenses, partially confirming our hypothesis of early infancy being an especially important period for the development of defense mechanisms. The findings are discussed from the viewpoints of attachment and family dynamics, emotional self-awareness, and sense of security.
Predicting growth of the healthy infant using a genome scale metabolic model

DEFF Research Database (Denmark)

Nilsson, Avlant; Mardinoglu, Adil; Nielsen, Jens

2017-01-01

to simulate the mechanisms of growth and integrate data about breast-milk intake and composition with the infant's biomass and energy expenditure of major organs. The model predicted daily metabolic fluxes from birth to age 6 months, and accurately reproduced standard growth curves and changes in body...
Pattern-oriented modelling: a 'multi-scope' for predictive systems ecology.

Science.gov (United States)

Grimm, Volker; Railsback, Steven F

2012-01-19

Modern ecology recognizes that modelling systems across scales and at multiple levels-especially to link population and ecosystem dynamics to individual adaptive behaviour-is essential for making the science predictive. 'Pattern-oriented modelling' (POM) is a strategy for doing just this. POM is the multi-criteria design, selection and calibration of models of complex systems. POM starts with identifying a set of patterns observed at multiple scales and levels that characterize a system with respect to the particular problem being modelled; a model from which the patterns emerge should contain the right mechanisms to address the problem. These patterns are then used to (i) determine what scales, entities, variables and processes the model needs, (ii) test and select submodels to represent key low-level processes such as adaptive behaviour, and (iii) find useful parameter values during calibration. Patterns are already often used in these ways, but a mini-review of applications of POM confirms that making the selection and use of patterns more explicit and rigorous can facilitate the development of models with the right level of complexity to understand ecological systems and predict their response to novel conditions.
Incorporating uncertainty in predictive species distribution modelling.

Science.gov (United States)

Beale, Colin M; Lennon, Jack J

2012-01-19

Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.
A study of deterministic models for quantum mechanics

International Nuclear Information System (INIS)

Sutherland, R.

1980-01-01

A theoretical investigation is made into the difficulties encountered in constructing a deterministic model for quantum mechanics and into the restrictions that can be placed on the form of such a model. The various implications of the known impossibility proofs are examined. A possible explanation for the non-locality required by Bell's proof is suggested in terms of backward-in-time causality. The efficacy of the Kochen and Specker proof is brought into doubt by showing that there is a possible way of avoiding its implications in the only known physically realizable situation to which it applies. A new thought experiment is put forward to show that a particle's predetermined momentum and energy values cannot satisfy the laws of momentum and energy conservation without conflicting with the predictions of quantum mechanics. Attention is paid to a class of deterministic models for which the individual outcomes of measurements are not dependent on hidden variables associated with the measuring apparatus and for which the hidden variables of a particle do not need to be randomized after each measurement
CADRE-SS, an in Silico Tool for Predicting Skin Sensitization Potential Based on Modeling of Molecular Interactions.

Science.gov (United States)

Kostal, Jakub; Voutchkova-Kostal, Adelina

2016-01-19

Using computer models to accurately predict toxicity outcomes is considered to be a major challenge. However, state-of-the-art computational chemistry techniques can now be incorporated in predictive models, supported by advances in mechanistic toxicology and the exponential growth of computing resources witnessed over the past decade. The CADRE (Computer-Aided Discovery and REdesign) platform relies on quantum-mechanical modeling of molecular interactions that represent key biochemical triggers in toxicity pathways. Here, we present an external validation exercise for CADRE-SS, a variant developed to predict the skin sensitization potential of commercial chemicals. CADRE-SS is a hybrid model that evaluates skin permeability using Monte Carlo simulations, assigns reactive centers in a molecule and possible biotransformations via expert rules, and determines reactivity with skin proteins via quantum-mechanical modeling. The results were promising with an overall very good concordance of 93% between experimental and predicted values. Comparison to performance metrics yielded by other tools available for this endpoint suggests that CADRE-SS offers distinct advantages for first-round screenings of chemicals and could be used as an in silico alternative to animal tests where permissible by legislative programs.
Modeling the microstructurally dependent mechanical properties of poly(ester-urethane-urea)s.

Science.gov (United States)

Warren, P Daniel; Sycks, Dalton G; McGrath, Dominic V; Vande Geest, Jonathan P

2013-12-01

Poly(ester-urethane-urea) (PEUU) is one of many synthetic biodegradable elastomers under scrutiny for biomedical and soft tissue applications. The goal of this study was to investigate the effect of the experimental parameters on mechanical properties of PEUUs following exposure to different degrading environments, similar to that of the human body, using linear regression, producing one predictive model. The model utilizes two independent variables of poly(caprolactone) (PCL) type and copolymer crystallinity to predict the dependent variable of maximum tangential modulus (MTM). Results indicate that comparisons between PCLs at different degradation states are statistically different (p < 0.0003), while the difference between experimental and predicted average MTM is statistically negligible (p < 0.02). The linear correlation between experimental and predicted MTM values is R(2) = 0.75. Copyright © 2013 Wiley Periodicals, Inc., a Wiley Company.

Comment on ''a classical model of EPR experiment with quantum mechanical correlations and Bell inequalities''

International Nuclear Information System (INIS)

Aspect, A.

1986-01-01

The author states that ''It is impossible to mimick the quantum mechanical predictions for the EPR correlations, with a reasonable classical-looking model, in the spirit of Einstein's ideas''. The author feels that if he is wrong somebody could make a classical model (i.e. following the laws of classical physics) mimicking all the quantum mechanical predictions for the EPR correlations. He attempts to show that it is not the case for Barut's model for the following reasons: the first version of his model is classical, but doesn't mimick at all an EPR type experiment; and by reinterpretation one can get a model that does mimick the experiment, but this model is no longer ''reasonably classical looking'' since it involves negative probabilities. The claim is put in the form of a challenge. It is shown that the model under discussion can be reinterpreted by adding a chip converting the continuous outputs into two-valved outputs
Predictive user modeling with actionable attributes

NARCIS (Netherlands)

Zliobaite, I.; Pechenizkiy, M.

2013-01-01

Different machine learning techniques have been proposed and used for modeling individual and group user needs, interests and preferences. In the traditional predictive modeling instances are described by observable variables, called attributes. The goal is to learn a model for predicting the target
Proteochemometric model for predicting the inhibition of penicillin-binding proteins

Science.gov (United States)

Nabu, Sunanta; Nantasenamat, Chanin; Owasirikul, Wiwat; Lawung, Ratana; Isarankura-Na-Ayudhya, Chartchalerm; Lapins, Maris; Wikberg, Jarl E. S.; Prachayasittikul, Virapong

2015-02-01

Neisseria gonorrhoeae infection threatens to become an untreatable sexually transmitted disease in the near future owing to the increasing emergence of N. gonorrhoeae strains with reduced susceptibility and resistance to the extended-spectrum cephalosporins (ESCs), i.e. ceftriaxone and cefixime, which are the last remaining option for first-line treatment of gonorrhea. Alteration of the penA gene, encoding penicillin-binding protein 2 (PBP2), is the main mechanism conferring penicillin resistance including reduced susceptibility and resistance to ESCs. To predict and investigate putative amino acid mutations causing β-lactam resistance particularly for ESCs, we applied proteochemometric modeling to generalize N. gonorrhoeae susceptibility data for predicting the interaction of PBP2 with therapeutic β-lactam antibiotics. This was afforded by correlating publicly available data on antimicrobial susceptibility of wild-type and mutant N. gonorrhoeae strains for penicillin-G, cefixime and ceftriaxone with 50 PBP2 protein sequence data using partial least-squares projections to latent structures. The generated model revealed excellent predictability ( R 2 = 0.91, Q 2 = 0.77, Q Ext 2 = 0.78). Moreover, our model identified amino acid mutations in PBP2 with the highest impact on antimicrobial susceptibility and provided information on physicochemical properties of amino acid mutations affecting antimicrobial susceptibility. Our model thus provided insight into the physicochemical basis for resistance development in PBP2 suggesting its use for predicting and monitoring novel PBP2 mutations that may emerge in the future.
A predictive framework for evaluating models of semantic organization in free recall

Science.gov (United States)

Morton, Neal W; Polyn, Sean M.

2016-01-01

Research in free recall has demonstrated that semantic associations reliably influence the organization of search through episodic memory. However, the specific structure of these associations and the mechanisms by which they influence memory search remain unclear. We introduce a likelihood-based model-comparison technique, which embeds a model of semantic structure within the context maintenance and retrieval (CMR) model of human memory search. Within this framework, model variants are evaluated in terms of their ability to predict the specific sequence in which items are recalled. We compare three models of semantic structure, latent semantic analysis (LSA), global vectors (GloVe), and word association spaces (WAS), and find that models using WAS have the greatest predictive power. Furthermore, we find evidence that semantic and temporal organization is driven by distinct item and context cues, rather than a single context cue. This finding provides important constraint for theories of memory search. PMID:28331243
Predictions of NO{sub x} formation in an NH{sub 3}-doped syngas flame using CFD combined with a detailed reaction mechanism

Energy Technology Data Exchange (ETDEWEB)

Brink, A; Norstroem, T; Kilpinen, P; Hupa, M [Aabo Akademi, Turku (Finland). Combustion Chemistry Research Group

1998-12-31

The formation of NO{sub x} in a CO/H{sub 2}/CH{sub 4}/NH{sub 3} jet in a co-flowing air stream was modeled by use of CFD combined with a comprehensive detailed reaction mechanism. The comprehensive mechanism involved 340 reversible elementary reactions between 55 species. Three different approaches to include the detailed reaction mechanism were tested. In approach I, all chemistry was described with the comprehensive mechanism. In approaches IIa and IIb the comprehensive mechanism was used in post-processing calculations of the nitrogen chemistry. In approach IIa, the temperatures of the reacting structures obtained in the main calculations were used, whereas in approach IIb, the inlet temperatures to the reacting structures were taken from the main calculation. In approach IIIa and IIIb, empirical reaction mechanisms describing the nitrogen chemistry were tested. The turbulence-chemistry interaction was accounted for with a new model, which combines the Eddy-Dissipation Concept with a model based on the `Exchange by Interaction with the Mean`. There was a clear difference between the computed results and the measured ones. The use of approach I resulted in an obvious overprediction of the lift-off height. The predicted molar NO{sub x} yield with the approaches IIa and IIb were 89 % and 85 %, respectively, whereas a yield of 23 % had been measured. With the empirical mechanisms used in approach IIIa, a similar NO{sub x} yield was predicted as with approaches IIa and IIb. With IIIb the predicted NO{sub x} yield was 40 %. However, in this case 67 % of the NH{sub 3} remained unreacted. The reason for the large difference between the calculated NO{sub x} yield and the measured one reported in the literature is a poor modeling of the initial part of the fuel jet. A possible reason for this is the coarse grid. (author) 15 refs.
Predictions of NO{sub x} formation in an NH{sub 3}-doped syngas flame using CFD combined with a detailed reaction mechanism

Energy Technology Data Exchange (ETDEWEB)

Brink, A.; Norstroem, T.; Kilpinen, P.; Hupa, M. [Aabo Akademi, Turku (Finland). Combustion Chemistry Research Group

1997-12-31

The formation of NO{sub x} in a CO/H{sub 2}/CH{sub 4}/NH{sub 3} jet in a co-flowing air stream was modeled by use of CFD combined with a comprehensive detailed reaction mechanism. The comprehensive mechanism involved 340 reversible elementary reactions between 55 species. Three different approaches to include the detailed reaction mechanism were tested. In approach I, all chemistry was described with the comprehensive mechanism. In approaches IIa and IIb the comprehensive mechanism was used in post-processing calculations of the nitrogen chemistry. In approach IIa, the temperatures of the reacting structures obtained in the main calculations were used, whereas in approach IIb, the inlet temperatures to the reacting structures were taken from the main calculation. In approach IIIa and IIIb, empirical reaction mechanisms describing the nitrogen chemistry were tested. The turbulence-chemistry interaction was accounted for with a new model, which combines the Eddy-Dissipation Concept with a model based on the `Exchange by Interaction with the Mean`. There was a clear difference between the computed results and the measured ones. The use of approach I resulted in an obvious overprediction of the lift-off height. The predicted molar NO{sub x} yield with the approaches IIa and IIb were 89 % and 85 %, respectively, whereas a yield of 23 % had been measured. With the empirical mechanisms used in approach IIIa, a similar NO{sub x} yield was predicted as with approaches IIa and IIb. With IIIb the predicted NO{sub x} yield was 40 %. However, in this case 67 % of the NH{sub 3} remained unreacted. The reason for the large difference between the calculated NO{sub x} yield and the measured one reported in the literature is a poor modeling of the initial part of the fuel jet. A possible reason for this is the coarse grid. (author) 15 refs.
Cognitive Model of Trust Dynamics Predicts Human Behavior within and between Two Games of Strategic Interaction with Computerized Confederate Agents.

Science.gov (United States)

Collins, Michael G; Juvina, Ion; Gluck, Kevin A

2016-01-01

When playing games of strategic interaction, such as iterated Prisoner's Dilemma and iterated Chicken Game, people exhibit specific within-game learning (e.g., learning a game's optimal outcome) as well as transfer of learning between games (e.g., a game's optimal outcome occurring at a higher proportion when played after another game). The reciprocal trust players develop during the first game is thought to mediate transfer of learning effects. Recently, a computational cognitive model using a novel trust mechanism has been shown to account for human behavior in both games, including the transfer between games. We present the results of a study in which we evaluate the model's a priori predictions of human learning and transfer in 16 different conditions. The model's predictive validity is compared against five model variants that lacked a trust mechanism. The results suggest that a trust mechanism is necessary to explain human behavior across multiple conditions, even when a human plays against a non-human agent. The addition of a trust mechanism to the other learning mechanisms within the cognitive architecture, such as sequence learning, instance-based learning, and utility learning, leads to better prediction of the empirical data. It is argued that computational cognitive modeling is a useful tool for studying trust development, calibration, and repair.
A new lifetime estimation model for a quicker LED reliability prediction

Science.gov (United States)

Hamon, B. H.; Mendizabal, L.; Feuillet, G.; Gasse, A.; Bataillou, B.

2014-09-01

LED reliability and lifetime prediction is a key point for Solid State Lighting adoption. For this purpose, one hundred and fifty LEDs have been aged for a reliability analysis. LEDs have been grouped following nine current-temperature stress conditions. Stress driving current was fixed between 350mA and 1A and ambient temperature between 85C and 120°C. Using integrating sphere and I(V) measurements, a cross study of the evolution of electrical and optical characteristics has been done. Results show two main failure mechanisms regarding lumen maintenance. The first one is the typically observed lumen depreciation and the second one is a much more quicker depreciation related to an increase of the leakage and non radiative currents. Models of the typical lumen depreciation and leakage resistance depreciation have been made using electrical and optical measurements during the aging tests. The combination of those models allows a new method toward a quicker LED lifetime prediction. These two models have been used for lifetime predictions for LEDs.
Analysis of algae growth mechanism and water bloom prediction under the effect of multi-affecting factor.

Science.gov (United States)

Wang, Li; Wang, Xiaoyi; Jin, Xuebo; Xu, Jiping; Zhang, Huiyan; Yu, Jiabin; Sun, Qian; Gao, Chong; Wang, Lingbin

2017-03-01

The formation process of algae is described inaccurately and water blooms are predicted with a low precision by current methods. In this paper, chemical mechanism of algae growth is analyzed, and a correlation analysis of chlorophyll-a and algal density is conducted by chemical measurement. Taking into account the influence of multi-factors on algae growth and water blooms, the comprehensive prediction method combined with multivariate time series and intelligent model is put forward in this paper. Firstly, through the process of photosynthesis, the main factors that affect the reproduction of the algae are analyzed. A compensation prediction method of multivariate time series analysis based on neural network and Support Vector Machine has been put forward which is combined with Kernel Principal Component Analysis to deal with dimension reduction of the influence factors of blooms. Then, Genetic Algorithm is applied to improve the generalization ability of the BP network and Least Squares Support Vector Machine. Experimental results show that this method could better compensate the prediction model of multivariate time series analysis which is an effective way to improve the description accuracy of algae growth and prediction precision of water blooms.
Prediction of mechanical and wear properties of 6026 aluminum alloy waste to be used in prosthetics limbs

Science.gov (United States)

Arbilei, Marwan N.

2018-05-01

This paper aimed to recycle high power electrical wires west in prosthetics limbs manufacturing. The effect of grain size on mechanical properties (Hardness and Tensile Strength), and wear resistance of commercial 6026 T9 Aluminum alloys that used in electrical industry have been modeled to be predicted. Six sets of samples were prepared with different annealing heat treatment parameters, (300,350 and 400)°C with (1 and 2) hours. Each treatment gained different grain sizes (23-71) μm and evenly HV (61-169) values. The grain size that produced from heat treatments was ranged from. Tensile properties regarding HV have been reviewed and all data haven collected to create a mathematical model showing the relation between Tensile strength and Hardness. The Sliding wear tests applied with (3 and 8) N with five periods (20-100) minutes. Multiple regression model prepared for predicting the values of weight loss for wear process. The model was tested and validated for the properties. The main purpose of this research is to provide an effective and accurate way to predict weight loose rate in wear process.
Measurements of entanglement over a kilometric distance to test superluminal models of Quantum Mechanics: preliminary results.

Science.gov (United States)

Cocciaro, B.; Faetti, S.; Fronzoni, L.

2017-08-01

As shown in the EPR paper (Einstein, Podolsky e Rosen, 1935), Quantum Mechanics is a non-local Theory. The Bell theorem and the successive experiments ruled out the possibility of explaining quantum correlations using only local hidden variables models. Some authors suggested that quantum correlations could be due to superluminal communications that propagate isotropically with velocity vt > c in a preferred reference frame. For finite values of vt and in some special cases, Quantum Mechanics and superluminal models lead to different predictions. So far, no deviations from the predictions of Quantum Mechanics have been detected and only lower bounds for the superluminal velocities vt have been established. Here we describe a new experiment that increases the maximum detectable superluminal velocities and we give some preliminary results.
Recent progress in sorption mechanisms and models

International Nuclear Information System (INIS)

Fedoroff, M.; Lefevre, G.

2005-01-01

Full text of publication follows: Sorption-desorption phenomena play an important role in the migration of radioactive species in surface and underground waters. In order to predict the transport of these species, we need a good knowledge of sorption processes and data, together with reliable models able to be included in transport calculation. Traditional approaches based on experimentally determined distribution coefficients (Kd) and sorption isotherms have a limited predictive capability, since they are very sensitive to the numerous parameters characterizing the solution and the solid. Models based on thermodynamic equilibria were developed to account for the influence these parameters: the ion exchange model and the surface complexation models (2-pK mono-site, 1-pK multi-site, with several different electrostatic models: CCM, DLM, BSM, TLM,...). Although these models are very useful, studies performed in recent years showed that they have important theoretical and experimental limitations, which result in the fact that we must be very careful when we use them for extrapolating sorption data to long term and to large natural systems. Among all problems which can be found are: the possibility to fit a set of experimental data with different models, sometimes bad adequacy with the real sorption processes, some theoretical limitations such as a rigorous definition of reference and standard states in surface equilibria, slow kinetics which prevent from equilibrium achievement, irreversibility, solubility and evolution of solid phases... Through the increase of the number of sensitive spectroscopic methods, we are now able to know more about sorption processes at the atomic scale. Models such as the 1-pK CD-MUSIC model can account for the influence of orientation of the faces of the solid. More and more examples of the influence of this orientation on the sorption properties are known. Calculations performed by 'ab initio' modeling is also useful to predict the
Photochemical modeling in California with two chemical mechanisms: model intercomparison and response to emission reductions.

Science.gov (United States)

Cai, Chenxia; Kelly, James T; Avise, Jeremy C; Kaduwela, Ajith P; Stockwell, William R

2011-05-01

An updated version of the Statewide Air Pollution Research Center (SAPRC) chemical mechanism (SAPRC07C) was implemented into the Community Multiscale Air Quality (CMAQ) version 4.6. CMAQ simulations using SAPRC07C and the previously released version, SAPRC99, were performed and compared for an episode during July-August, 2000. Ozone (O3) predictions of the SAPRC07C simulation are generally lower than those of the SAPRC99 simulation in the key areas of central and southern California, especially in areas where modeled concentrations are greater than the federal 8-hr O3 standard of 75 parts per billion (ppb) and/or when the volatile organic compound (VOC)/nitrogen oxides (NOx) ratio is less than 13. The relative changes of ozone production efficiency (OPE) against the VOC/NOx ratio at 46 sites indicate that the OPE is reduced in SAPRC07C compared with SAPRC99 at most sites by as much as approximately 22%. The SAPRC99 and SAPRC07C mechanisms respond similarly to 20% reductions in anthropogenic VOC emissions. The response of the mechanisms to 20% NOx emissions reductions can be grouped into three cases. In case 1, in which both mechanisms show a decrease in daily maximum 8-hr O3 concentration with decreasing NOx emissions, the O3 decrease in SAPRC07C is smaller. In case 2, in which both mechanisms show an increase in O3 with decreasing NOx emissions, the O3 increase is larger in SAPRC07C. In case 3, SAPRC07C simulates an increase in O3 in response to reduced NOx emissions whereas SAPRC99 simulates a decrease in O3 for the same region. As a result, the areas where NOx controls would be disbeneficial are spatially expanded in SAPRC07C. Although the results presented here are valuable for understanding differences in predictions and model response for SAPRC99 and SAPRC07C, the study did not evaluate the impact of mechanism differences in the context of the U.S. Environmental Protection Agency's guidance for using numerical models in demonstrating air quality attainment
MJO prediction skill of the subseasonal-to-seasonal (S2S) prediction models

Science.gov (United States)

Son, S. W.; Lim, Y.; Kim, D.

2017-12-01

The Madden-Julian Oscillation (MJO), the dominant mode of tropical intraseasonal variability, provides the primary source of tropical and extratropical predictability on subseasonal to seasonal timescales. To better understand its predictability, this study conducts quantitative evaluation of MJO prediction skill in the state-of-the-art operational models participating in the subseasonal-to-seasonal (S2S) prediction project. Based on bivariate correlation coefficient of 0.5, the S2S models exhibit MJO prediction skill ranging from 12 to 36 days. These prediction skills are affected by both the MJO amplitude and phase errors, the latter becoming more important with forecast lead times. Consistent with previous studies, the MJO events with stronger initial amplitude are typically better predicted. However, essentially no sensitivity to the initial MJO phase is observed. Overall MJO prediction skill and its inter-model spread are further related with the model mean biases in moisture fields and longwave cloud-radiation feedbacks. In most models, a dry bias quickly builds up in the deep tropics, especially across the Maritime Continent, weakening horizontal moisture gradient. This likely dampens the organization and propagation of MJO. Most S2S models also underestimate the longwave cloud-radiation feedbacks in the tropics, which may affect the maintenance of the MJO convective envelop. In general, the models with a smaller bias in horizontal moisture gradient and longwave cloud-radiation feedbacks show a higher MJO prediction skill, suggesting that improving those processes would enhance MJO prediction skill.
Nitrogen chemistry in combustion and gasification - mechanisms and modeling

International Nuclear Information System (INIS)

Kilpinen, P.; Hupa, M.

1998-01-01

The objective of this work has been to increase the understanding of the complex details of gaseous emission formation in energy production techniques based on combustion and/or gasification. The aim has also been to improve the accuracy of mathematical furnace models when they are used for predicting emissions. The main emphasis has been on nitrogen oxides (NO x , N 2 O). The work supports development of cleaner and more efficient combustion technology. The main emphasis has been on combustion systems that are based on fluidized bed technology including both atmospheric and pressurized conditions (BFBC, CFBC, PFBC/G). The work has consisted of advanced theoretical modeling and of experiments in laboratory devices that have partly been made in collaboration with other LIEKKI projects. Two principal modeling tools have been used: detailed homogeneous chemical kinetic modeling and computational fluid dynamic simulation. In this report, the most important results of the following selected items will be presented: (1) Extension of a detailed kinetic nitrogen and hydrocarbon oxidation mechanism into elevated pressure, and parametric studies on: effect of pressure on fuel-nitrogen oxidation under PFBC conditions, effect of pressure on selective non-catalytic NO x reduction under PFBC conditions, effect of different oxidizers on hot-gas cleaning of ammonia by means of selective oxidation in gasification gas. (2) Extension of the above mechanism to include chlorine reactions at atmospheric pressure, and parametric studies on: effect of HCl on CO burn-out in FBC combustion of waste. (3) Development of more accurate emission prediction models: incorporation of more accurate submodels on hydrocarbon oxidation into CFD furnace models, and evaluation of different concepts describing the interaction between turbulence and chemical reaction, development of a mechanistic detailed 1.5-dimensional emission model for circulating fluidized bed combustors. (orig.) 14 refs
What Is the Predictive Value of Animal Models for Vaccine Efficacy in Humans? Consideration of Strategies to Improve the Value of Animal Models.

Science.gov (United States)

Herati, Ramin Sedaghat; Wherry, E John

2018-04-02

Animal models are an essential feature of the vaccine design toolkit. Although animal models have been invaluable in delineating the mechanisms of immune function, their precision in predicting how well specific vaccines work in humans is often suboptimal. There are, of course, many obvious species differences that may limit animal models from predicting all details of how a vaccine works in humans. However, careful consideration of which animal models may have limitations should also allow more accurate interpretations of animal model data and more accurate predictions of what is to be expected in clinical trials. In this article, we examine some of the considerations that might be relevant to cross-species extrapolation of vaccine-related immune responses for the prediction of how vaccines will perform in humans. Copyright © 2018 Cold Spring Harbor Laboratory Press; all rights reserved.
Survey on damage mechanics models for fatigue life prediction

NARCIS (Netherlands)

Silitonga, S.; Maljaars, J.; Soetens, F.; Snijder, H.H.

2013-01-01

Engineering methods to predict the fatigue life of structures have been available since the beginning of the 20th century. However, a practical problem arises from complex loading conditions and a significant concern is the accuracy of the methods under variable amplitude loading. This paper
Evaluating Predictive Uncertainty of Hyporheic Exchange Modelling

Science.gov (United States)

Chow, R.; Bennett, J.; Dugge, J.; Wöhling, T.; Nowak, W.

2017-12-01

Hyporheic exchange is the interaction of water between rivers and groundwater, and is difficult to predict. One of the largest contributions to predictive uncertainty for hyporheic fluxes have been attributed to the representation of heterogeneous subsurface properties. This research aims to evaluate which aspect of the subsurface representation - the spatial distribution of hydrofacies or the model for local-scale (within-facies) heterogeneity - most influences the predictive uncertainty. Also, we seek to identify data types that help reduce this uncertainty best. For this investigation, we conduct a modelling study of the Steinlach River meander, in Southwest Germany. The Steinlach River meander is an experimental site established in 2010 to monitor hyporheic exchange at the meander scale. We use HydroGeoSphere, a fully integrated surface water-groundwater model, to model hyporheic exchange and to assess the predictive uncertainty of hyporheic exchange transit times (HETT). A highly parameterized complex model is built and treated as `virtual reality', which is in turn modelled with simpler subsurface parameterization schemes (Figure). Then, we conduct Monte-Carlo simulations with these models to estimate the predictive uncertainty. Results indicate that: Uncertainty in HETT is relatively small for early times and increases with transit times. Uncertainty from local-scale heterogeneity is negligible compared to uncertainty in the hydrofacies distribution. Introducing more data to a poor model structure may reduce predictive variance, but does not reduce predictive bias. Hydraulic head observations alone cannot constrain the uncertainty of HETT, however an estimate of hyporheic exchange flux proves to be more effective at reducing this uncertainty. Figure: Approach for evaluating predictive model uncertainty. A conceptual model is first developed from the field investigations. A complex model (`virtual reality') is then developed based on that conceptual model
A New Material Constitutive Model for Predicting Cladding Failure

Energy Technology Data Exchange (ETDEWEB)

Rashid, Joe; Dunham, Robert [ANATECH Corp., San Diego, CA (United States); Rashid, Mark [University of California Davis, Davis, CA (United States); Machiels, Albert [EPRI, Palo Alto, CA (United States)

2009-06-15

An important issue in fuel performance and safety evaluations is the characterization of the effects of hydrides on cladding mechanical response and failure behavior. The hydride structure formed during power operation transforms the cladding into a complex multi-material composite, with through-thickness concentration profile that causes cladding ductility to vary by more than an order of magnitude between ID and OD. However, current practice of mechanical property testing treats the cladding as a homogeneous material characterized by a single stress-strain curve, regardless of its hydride morphology. Consequently, as irradiation conditions and hydrides evolution change, new material property testing is required, which results in a state of continuous need for valid material property data. A recently developed constitutive model, treats the cladding as a multi-material composite in which the metal and the hydride platelets are treated as separate material phases with their own elastic-plastic and fracture properties and interacting at their interfaces with appropriate constraint conditions between them to ensure strain and stress compatibility. An essential feature of the model is a multi-phase damage formulation that models the complex interaction between the hydride phases and the metal matrix and the coupled effect of radial and circumferential hydrides on cladding stress-strain response. This gives the model the capability of directly predicting cladding failure progression during the loading event and, as such, provides a unique tool for constructing failure criteria analytically where none could be developed by conventional material testing. Implementation of the model in a fuel behavior code provides the capability to predict in-reactor operational failures due to PCI or missing pellet surfaces (MPS) without having to rely on failure criteria. Even, a stronger motivation for use of the model is in the transportation accidents analysis of spent fuel
Prediction of thermal and mechanical stress-strain responses of TMC's subjected to complex TMF histories

Science.gov (United States)

Johnson, W. S.; Mirdamadi, M.

1994-01-01

This paper presents an experimental and analytical evaluation of cross-plied laminates of Ti-15V-3Cr-3Al-3Sn (Ti-15-3) matrix reinforced with continuous silicon-carbide fibers (SCS-6) subjected to a complex TMF loading profile. Thermomechanical fatigue test techniques were developed to conduct a simulation of a generic hypersonic flight profile. A micromechanical analysis was used. The analysis predicts the stress-strain response of the laminate and of the constituents in each ply during thermal and mechanical cycling by using only constituent properties as input. The fiber was modeled as elastic with transverse orthotropic and temperature-dependent properties. The matrix was modeled using a thermoviscoplastic constitutive relation. The fiber transverse modulus was reduced in the analysis to simulate the fiber-matrix interface failures. Excellent correlation was found between measured and predicted laminate stress-strain response due to generic hypersonic flight profile when fiber debonding was modeled.

Modeling, robust and distributed model predictive control for freeway networks

NARCIS (Netherlands)

Liu, S.

2016-01-01

In Model Predictive Control (MPC) for traffic networks, traffic models are crucial since they are used as prediction models for determining the optimal control actions. In order to reduce the computational complexity of MPC for traffic networks, macroscopic traffic models are often used instead of
Unified model to predict flexural shear behavior of externally bonded RC beams

International Nuclear Information System (INIS)

Colotti, V.; Spadea, G.; Swamy, R.N.

2006-01-01

Structural strengthening with externally bonded reinforcement is now recognized as a cost-effective, structurally sound and practically efficient method of rehabilitating deteriorating and damaged reinforced concrete beams. There is now an urgent need to develop a sound engineering basis which can predict the failure loads of all such strengthened beams in a reliable and consistent manner. Existing models to predict the behavior at ultimate of strengthened beams suffer from many limitations and weaknesses. This paper presents a unified global model, based on the Strut-and-Tie approach, to predict the failure loads of reinforced concrete beams strengthened for flexure and/or shear. This structural model is based on rational engineering principles, considers all the possible failure modes, and incorporates the load transfer mechanism bond to reflect the debonding phenomena which has a dominant influence on the failure process of plated beams. The model is validated against about 200 strengthened beam test reported in the literature and failing in flexure and/or shear, involving a large number of structural variables and steel, carbon and glass fiber reinforced polymer laminates as reinforcing medium. (author)
Staying Power of Churn Prediction Models

NARCIS (Netherlands)

Risselada, Hans; Verhoef, Peter C.; Bijmolt, Tammo H. A.

In this paper, we study the staying power of various churn prediction models. Staying power is defined as the predictive performance of a model in a number of periods after the estimation period. We examine two methods, logit models and classification trees, both with and without applying a bagging
Multi-physics Model for the Aging Prediction of a Vanadium Redox Flow Battery System

International Nuclear Information System (INIS)

Merei, Ghada; Adler, Sophie; Magnor, Dirk; Sauer, Dirk Uwe

2015-01-01

Highlights: • Present a multi-physics model of vanadium redox-flow battery. • This model is essential for aging prediction. • It is applicable for VRB system of different power and capacity ratings. • Good results comparing with current research in this field. - Abstract: The all-vanadium redox-flow battery is an attractive candidate to compensate the fluctuations of non-dispatchable renewable energy generation. While several models for vanadium redox batteries have been described yet, no model has been published, which is adequate for the aging prediction. Therefore, the present paper presents a multi-physics model which determines all parameters that are essential for an aging prediction. In a following paper, the corresponding aging model of vanadium redox flow battery (VRB) is described. The model combines existing models for the mechanical losses and temperature development with new approaches for the batteries side reactions. The model was implemented in Matlab/Simulink. The modeling results presented in the paper prove to be consistent with the experimental results of other research groups
Model-Based Fatigue Prognosis of Fiber-Reinforced Laminates Exhibiting Concurrent Damage Mechanisms

Science.gov (United States)

Corbetta, M.; Sbarufatti, C.; Saxena, A.; Giglio, M.; Goebel, K.

2016-01-01

Prognostics of large composite structures is a topic of increasing interest in the field of structural health monitoring for aerospace, civil, and mechanical systems. Along with recent advancements in real-time structural health data acquisition and processing for damage detection and characterization, model-based stochastic methods for life prediction are showing promising results in the literature. Among various model-based approaches, particle-filtering algorithms are particularly capable in coping with uncertainties associated with the process. These include uncertainties about information on the damage extent and the inherent uncertainties of the damage propagation process. Some efforts have shown successful applications of particle filtering-based frameworks for predicting the matrix crack evolution and structural stiffness degradation caused by repetitive fatigue loads. Effects of other damage modes such as delamination, however, are not incorporated in these works. It is well established that delamination and matrix cracks not only co-exist in most laminate structures during the fatigue degradation process but also affect each other's progression. Furthermore, delamination significantly alters the stress-state in the laminates and accelerates the material degradation leading to catastrophic failure. Therefore, the work presented herein proposes a particle filtering-based framework for predicting a structure's remaining useful life with consideration of multiple co-existing damage-mechanisms. The framework uses an energy-based model from the composite modeling literature. The multiple damage-mode model has been shown to suitably estimate the energy release rate of cross-ply laminates as affected by matrix cracks and delamination modes. The model is also able to estimate the reduction in stiffness of the damaged laminate. This information is then used in the algorithms for life prediction capabilities. First, a brief summary of the energy-based damage model
Mechanistic variables can enhance predictive models of endotherm distributions: The American pika under current, past, and future climates

Science.gov (United States)

Mathewson, Paul; Moyer-Horner, Lucas; Beever, Erik; Briscoe, Natalie; Kearney, Michael T.; Yahn, Jeremiah; Porter, Warren P.

2017-01-01

How climate constrains species’ distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat-balance model, to convert macroclimate data to pika-specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8–19% less habitat loss in response to annual temperature increases of ~3–5 °C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect
Mechanistic variables can enhance predictive models of endotherm distributions: the American pika under current, past, and future climates.

Science.gov (United States)

Mathewson, Paul D; Moyer-Horner, Lucas; Beever, Erik A; Briscoe, Natalie J; Kearney, Michael; Yahn, Jeremiah M; Porter, Warren P

2017-03-01

How climate constrains species' distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat-balance model, to convert macroclimate data to pika-specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8-19% less habitat loss in response to annual temperature increases of ~3-5 °C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect
Micromechanical Models of Mechanical Response of High Performance Fibre Reinforced Cement Composites

DEFF Research Database (Denmark)

Li, V. C.; Mihashi, H.; Alwan, J.

1996-01-01

generation of FRC with high performance and economical viability, is in sight. However, utilization of micromechanical models for a more comprehensive set of important HPFRCC properties awaits further investigations into fundamental mechanisms governing composite properties, as well as intergrative efforts......The state-of-the-art in micromechanical modeling of the mechanical response of HPFRCC is reviewed. Much advances in modeling has been made over the last decade to the point that certain properties of composites can be carefully designed using the models as analytic tools. As a result, a new...... across responses to different load types. Further, micromechanical models for HPFRCC behavior under complex loading histories, including those in fracture, fatigue and multuaxial loading are urgently needed in order to optimize HPFRCC microstrcuctures and enable predictions of such material in structures...
Prediction of radionuclide migration in the geosphere: is the porous-flow model adequate

International Nuclear Information System (INIS)

Neretnieks, I.

1982-01-01

Practically all models used today to describe radionuclide migration in geologic media are based on the concept of flow in porous media. Recently it has been questioned if Fickian dispersion is the most important dispersion mechanism. Field observations of dispersion indicate that the dispersion coefficient increases with observation distance. This should not be the case in a homogeneous porous medium. For a medium with essentially independent channels, on the other hand, such an effect can be predicted. In some calculated examples it is shown that the use of the Fickian dispersion mechanism will very seriously underestimate the radionuclide concentration at a point downstream if the spreading mechanism in reality is channelling. The consequences of the often-used assumption that the interaction between the radionuclides and the rock is instantaneous is also discussed. It has been shown that in sparsely fissured crystalline rock the whole rock mass will not be able to participate in the sorption reactions, because the radionuclides will not have time to penetrate all through the large blocks. On the other hand, the assumption that only the surface of the fissures interacts with the radionuclides is likely to be an extremely conservative assumption in view of some recent diffusion experiments in crystalline rocks performed in our laboratories and at the Canadian Geologic Survey. Some experimental results on radionuclide migration in a single natural fissure under well-controlled conditions in the laboratory are also presented and interpreted using a model which includes channelling, surface sorption, diffusion in the rock matrix and sorption in the rock matrix. Some implications of these mechanisms in predicting radionuclide migration in the geosphere are discussed and compared with what a porous-flow model would predict
Prediction Models for Dynamic Demand Response

Energy Technology Data Exchange (ETDEWEB)

Aman, Saima; Frincu, Marc; Chelmis, Charalampos; Noor, Muhammad; Simmhan, Yogesh; Prasanna, Viktor K.

2015-11-02

As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D²R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D²R, which we address in this paper. Our first contribution is the formal definition of D²R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D²R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D²R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D²R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D²R. Also, prediction models require just few days’ worth of data indicating that small amounts of
Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models

Science.gov (United States)

Spiliopoulou, Athina; Nagy, Reka; Bermingham, Mairead L.; Huffman, Jennifer E.; Hayward, Caroline; Vitart, Veronique; Rudan, Igor; Campbell, Harry; Wright, Alan F.; Wilson, James F.; Pong-Wong, Ricardo; Agakov, Felix; Navarro, Pau; Haley, Chris S.

2015-01-01

We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge. PMID:25918167
Computational modeling of elastic properties of carbon nanotube/polymer composites with interphase regions. Part II: Mechanical modeling

KAUST Repository

Han, Fei

2014-01-01

We present two modeling approaches for predicting the macroscopic elastic properties of carbon nanotubes/polymer composites with thick interphase regions at the nanotube/matrix frontier. The first model is based on local continuum mechanics; the second one is based on hybrid local/non-local continuum mechanics. The key computational issues, including the peculiar homogenization technique and treatment of periodical boundary conditions in the non-local continuum model, are clarified. Both models are implemented through a three-dimensional geometric representation of the carbon nanotubes network, which has been detailed in Part I. Numerical results are shown and compared for both models in order to test convergence and sensitivity toward input parameters. It is found that both approaches provide similar results in terms of homogenized quantities but locally can lead to very different microscopic fields. © 2013 Elsevier B.V. All rights reserved.
Accuracy assessment of landslide prediction models

International Nuclear Information System (INIS)

Othman, A N; Mohd, W M N W; Noraini, S

2014-01-01

The increasing population and expansion of settlements over hilly areas has greatly increased the impact of natural disasters such as landslide. Therefore, it is important to developed models which could accurately predict landslide hazard zones. Over the years, various techniques and models have been developed to predict landslide hazard zones. The aim of this paper is to access the accuracy of landslide prediction models developed by the authors. The methodology involved the selection of study area, data acquisition, data processing and model development and also data analysis. The development of these models are based on nine different landslide inducing parameters i.e. slope, land use, lithology, soil properties, geomorphology, flow accumulation, aspect, proximity to river and proximity to road. Rank sum, rating, pairwise comparison and AHP techniques are used to determine the weights for each of the parameters used. Four (4) different models which consider different parameter combinations are developed by the authors. Results obtained are compared to landslide history and accuracies for Model 1, Model 2, Model 3 and Model 4 are 66.7, 66.7%, 60% and 22.9% respectively. From the results, rank sum, rating and pairwise comparison can be useful techniques to predict landslide hazard zones
Poisson Mixture Regression Models for Heart Disease Prediction.

Science.gov (United States)

Mufudza, Chipo; Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.
Poisson Mixture Regression Models for Heart Disease Prediction

Science.gov (United States)

Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611
Comparisons of Faulting-Based Pavement Performance Prediction Models

Directory of Open Access Journals (Sweden)

Weina Wang

2017-01-01

Full Text Available Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR model, artificial neural network (ANN model, and Markov Chain (MC model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.
Use of Artificial Neural Network Models to Predict Indicator Organism Concentrations in an Urban Watershed

Science.gov (United States)

Mas, D. M.; Ahlfeld, D. P.

2004-05-01

Forecasting stream water quality is important for numerous aspects of resource protection and management. Fecal coliform and enteroccocus are primary indicator organisms used to assess potential pathogen contamination. Consequently, modeling the occurrence and concentration of fecal coliform and enterococcus is an important tool in watershed management. In addition, analyzing the relationship between model input and predicted indicator organisms is useful for elucidating possible sources of contamination and mechanisms of transport. While many process-based, statistical, and empirical models exist for water quality prediction, artificial neural network (ANN) models are increasingly being used for forecasting of water resources variables because ANNs are often capable of modeling complex systems for which behavioral rules are either unknown or difficult to simulate. The performance of ANNs compared to more established modeling approaches such as multiple linear regression (MLR) remains an importance research question. Data collected the U.S. Geological Survey in the lower Charles River in Massachusetts, USA in 1999-2000 was examined to determine correlation between various water quality constituents and indicator organisms and to explore the relationship between rainfall characteristics and indicator organism concentrations. Using the results of the statistical analysis to guide the selection of explanatory variables, MLR was performed to develop predictive equations for wet weather and dry weather conditions. The results show that the best-performing predictor variables are generally consistent for both indicator organisms considered. In addition, the regression equations show increasing indicator organism concentrations as a function of suspended sediment concentrations and length of time since last precipitation event, suggesting accumulation and wash off as a key mechanism of pathogen transport under wet weather conditions. This research also presents the
Unreachable Setpoints in Model Predictive Control

DEFF Research Database (Denmark)

Rawlings, James B.; Bonné, Dennis; Jørgensen, John Bagterp

2008-01-01

In this work, a new model predictive controller is developed that handles unreachable setpoints better than traditional model predictive control methods. The new controller induces an interesting fast/slow asymmetry in the tracking response of the system. Nominal asymptotic stability of the optimal...... steady state is established for terminal constraint model predictive control (MPC). The region of attraction is the steerable set. Existing analysis methods for closed-loop properties of MPC are not applicable to this new formulation, and a new analysis method is developed. It is shown how to extend...
Model Predictive Control for Linear Complementarity and Extended Linear Complementarity Systems

Directory of Open Access Journals (Sweden)

Bambang Riyanto

2005-11-01

Full Text Available In this paper, we propose model predictive control method for linear complementarity and extended linear complementarity systems by formulating optimization along prediction horizon as mixed integer quadratic program. Such systems contain interaction between continuous dynamics and discrete event systems, and therefore, can be categorized as hybrid systems. As linear complementarity and extended linear complementarity systems finds applications in different research areas, such as impact mechanical systems, traffic control and process control, this work will contribute to the development of control design method for those areas as well, as shown by three given examples.
Novel Uses of In Vitro Data to Develop Quantitative Biological Activity Relationship Models for in Vivo Carcinogenicity Prediction.

Science.gov (United States)

Pradeep, Prachi; Povinelli, Richard J; Merrill, Stephen J; Bozdag, Serdar; Sem, Daniel S

2015-04-01

The availability of large in vitro datasets enables better insight into the mode of action of chemicals and better identification of potential mechanism(s) of toxicity. Several studies have shown that not all in vitro assays can contribute as equal predictors of in vivo carcinogenicity for development of hybrid Quantitative Structure Activity Relationship (QSAR) models. We propose two novel approaches for the use of mechanistically relevant in vitro assay data in the identification of relevant biological descriptors and development of Quantitative Biological Activity Relationship (QBAR) models for carcinogenicity prediction. We demonstrate that in vitro assay data can be used to develop QBAR models for in vivo carcinogenicity prediction via two case studies corroborated with firm scientific rationale. The case studies demonstrate the similarities between QBAR and QSAR modeling in: (i) the selection of relevant descriptors to be used in the machine learning algorithm, and (ii) the development of a computational model that maps chemical or biological descriptors to a toxic endpoint. The results of both the case studies show: (i) improved accuracy and sensitivity which is especially desirable under regulatory requirements, and (ii) overall adherence with the OECD/REACH guidelines. Such mechanism based models can be used along with QSAR models for prediction of mechanistically complex toxic endpoints. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Evolving chemometric models for predicting dynamic process parameters in viscose production

Energy Technology Data Exchange (ETDEWEB)

Cernuda, Carlos [Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz (Austria); Lughofer, Edwin, E-mail: edwin.lughofer@jku.at [Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz (Austria); Suppan, Lisbeth [Kompetenzzentrum Holz GmbH, St. Peter-Str. 25, 4021 Linz (Austria); Roeder, Thomas; Schmuck, Roman [Lenzing AG, 4860 Lenzing (Austria); Hintenaus, Peter [Software Research Center, Paris Lodron University Salzburg (Austria); Maerzinger, Wolfgang [i-RED Infrarot Systeme GmbH, Linz (Austria); Kasberger, Juergen [Recendt GmbH, Linz (Austria)

2012-05-06

Highlights: Black-Right-Pointing-Pointer Quality assurance of process parameters in viscose production. Black-Right-Pointing-Pointer Automatic prediction of spin-bath concentrations based on FTNIR spectra. Black-Right-Pointing-Pointer Evolving chemometric models for efficiently handling changing system dynamics over time (no time-intensive re-calibration needed). Black-Right-Pointing-Pointer Significant reduction of huge errors produced by statistical state-of-the-art calibration methods. Black-Right-Pointing-Pointer Sufficient flexibility achieved by gradual forgetting mechanisms. - Abstract: In viscose production, it is important to monitor three process parameters in order to assure a high quality of the final product: the concentrations of H{sub 2}SO{sub 4}, Na{sub 2}SO{sub 4} and Z{sub n}SO{sub 4}. During on-line production these process parameters usually show a quite high dynamics depending on the fiber type that is produced. Thus, conventional chemometric models, which are trained based on collected calibration spectra from Fourier transform near infrared (FT-NIR) measurements and kept fixed during the whole life-time of the on-line process, show a quite imprecise and unreliable behavior when predicting the concentrations of new on-line data. In this paper, we are demonstrating evolving chemometric models which are able to adapt automatically to varying process dynamics by updating their inner structures and parameters in a single-pass incremental manner. These models exploit the Takagi-Sugeno fuzzy model architecture, being able to model flexibly different degrees of non-linearities implicitly contained in the mapping between near infrared spectra (NIR) and reference values. Updating the inner structures is achieved by moving the position of already existing local regions and by evolving (increasing non-linearity) or merging (decreasing non-linearity) new local linear predictors on demand, which are guided by distance-based and similarity criteria. Gradual
Modelling and predicting of commercial property attendance basing on the estimation of its attraction for consumers (by example of shopping malls

Directory of Open Access Journals (Sweden)

Varvara Sergeyevna Spirina

2015-03-01

Full Text Available Objective to research and elaborate an economicmathematical model of predicting of commercial property attendance by the example of shopping malls based on the estimation of its attraction for consumers. Methods the methodological and theoretical basis for the work was composed of the rules and techniques of elaborating the qualimetry and matrix mechanisms of complex estimation necessary for the estimation and aggregation of factors influencing the choice of a consumersrsquo group among many alternative property venues. Results two mechanisms are elaborated for the complex estimation of commercial property which is necessary to evaluate their attraction for consumers and to predict attendance. By the example of two large shopping malls in Perm Russia it is shown that using both mechanisms in the economicmathematical model of commercial property attendance increases the accuracy of its predictions compared to the traditional Huff model. The reliability of the results is confirmed by the coincidence of the results of calculation and the actual poll data on the shopping malls attendance. Scientific novelty a multifactor model of commercial property attraction for consumers was elaborated by the example of shopping malls parameters of complex estimation mechanisms are defined namely eight parameters influencing the choice of a shopping mall by consumers. The model differs from the traditional Huff model by the number of factors influencing the choice of a shopping mall by consumers and by the higher accuracy of predicting its attendance. Practical significance the economicmathematical models able to predict commercial property attendance can be used for efficient planning of measures to attract consumers to preserve and develop competitive advantages of commercial property. nbsp
A mechanical model for surface layer formation on self-lubricating ceramic composites

NARCIS (Netherlands)

Song, Jiupeng; Valefi, Mahdiar; de Rooij, Matthias B.; Schipper, Dirk J.

2010-01-01

To predict the thickness of a self-lubricating layer on the contact surface of ceramic composite material containing a soft phase during dry sliding test, a mechanical model was built to calculate the material transfer of the soft second phase in the composite to the surface. The tribological test,
A network security situation prediction model based on wavelet neural network with optimized parameters

Directory of Open Access Journals (Sweden)

Haibo Zhang

2016-08-01

Full Text Available The security incidents ion networks are sudden and uncertain, it is very hard to precisely predict the network security situation by traditional methods. In order to improve the prediction accuracy of the network security situation, we build a network security situation prediction model based on Wavelet Neural Network (WNN with optimized parameters by the Improved Niche Genetic Algorithm (INGA. The proposed model adopts WNN which has strong nonlinear ability and fault-tolerance performance. Also, the parameters for WNN are optimized through the adaptive genetic algorithm (GA so that WNN searches more effectively. Considering the problem that the adaptive GA converges slowly and easily turns to the premature problem, we introduce a novel niche technology with a dynamic fuzzy clustering and elimination mechanism to solve the premature convergence of the GA. Our final simulation results show that the proposed INGA-WNN prediction model is more reliable and effective, and it achieves faster convergence-speed and higher prediction accuracy than the Genetic Algorithm-Wavelet Neural Network (GA-WNN, Genetic Algorithm-Back Propagation Neural Network (GA-BPNN and WNN.
Intracochlear pressure measurements in scala media inform models of cochlear mechanics

Science.gov (United States)

Kale, Sushrut; Olson, Elizabeth S.

2015-12-01

In the classic view of cochlear mechanics, the cochlea is comprised of two identical fluid chambers separated by the cochlear partition (CP). In this view the traveling wave pressures in the two chambers mirror each other; they are equal in magnitude and opposite in phase. A fast pressure mode adds approximately uniformly. More recent models of cochlear mechanics take into account the structural complexity of the CP and the resulting additional mechanical modes would lead to distinct (non-symmetric) patterns of pressure and motion on the two sides of the CP. However, there was little to no physiological data that explored these predictions. To this aim, we measured intracochlear fluid pressure in scala media (SM), including measurements close to the sensory tissue, using miniaturized pressure sensors (˜ 80 μm outer diameter). Measurements were made in-vivo from the basal cochlear turn in gerbils. SM pressure was measured at two longitudinal locations in different preparations. In a subset of the experiments SM and ST (scala tympani) pressures were measured at the same longitudinal location. Traveling wave pressures were observed in both SM and ST, and showed the relative phase predicted by the classical theory. In addition, SM pressure showed spatial variations that had not been observed in ST, which points to a relatively complex CP motion on the SM side. These data both underscore the first-order validity of the classic cochlear traveling wave model, and open a new view to CP mechanics.
Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

Science.gov (United States)

Wallach, Daniel; Thorburn, Peter; Asseng, Senthold; Challinor, Andrew J.; Ewert, Frank; Jones, James W.; Rotter, Reimund; Ruane, Alexander

2016-01-01

Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.
Risk terrain modeling predicts child maltreatment.

Science.gov (United States)

Daley, Dyann; Bachmann, Michael; Bachmann, Brittany A; Pedigo, Christian; Bui, Minh-Thuy; Coffman, Jamye

2016-12-01

As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children. Copyright Â© 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.
A quantitative prediction model of SCC rate for nuclear structure materials in high temperature water based on crack tip creep strain rate

International Nuclear Information System (INIS)

Yang, F.Q.; Xue, H.; Zhao, L.Y.; Fang, X.R.

2014-01-01

Highlights: • Creep is considered to be the primary mechanical factor of crack tip film degradation. • The prediction model of SCC rate is based on crack tip creep strain rate. • The SCC rate calculated at the secondary stage of creep is recommended. • The effect of stress intensity factor on SCC growth rate is discussed. - Abstract: The quantitative prediction of stress corrosion cracking (SCC) of structure materials is essential in safety assessment of nuclear power plants. A new quantitative prediction model is proposed by combining the Ford–Andresen model, a crack tip creep model and an elastic–plastic finite element method. The creep at the crack tip is considered to be the primary mechanical factor of protective film degradation, and the creep strain rate at the crack tip is suggested as primary mechanical factor in predicting the SCC rate. The SCC rates at secondary stage of creep are recommended when using the approach introduced in this study to predict the SCC rates of materials in high temperature water. The proposed approach can be used to understand the SCC crack growth in structural materials of light water reactors
PGT: A Statistical Approach to Prediction and Mechanism Design

Science.gov (United States)

Wolpert, David H.; Bono, James W.

One of the biggest challenges facing behavioral economics is the lack of a single theoretical framework that is capable of directly utilizing all types of behavioral data. One of the biggest challenges of game theory is the lack of a framework for making predictions and designing markets in a manner that is consistent with the axioms of decision theory. An approach in which solution concepts are distribution-valued rather than set-valued (i.e. equilibrium theory) has both capabilities. We call this approach Predictive Game Theory (or PGT). This paper outlines a general Bayesian approach to PGT. It also presents one simple example to illustrate the way in which this approach differs from equilibrium approaches in both prediction and mechanism design settings.
Case studies in archaeological predictive modelling

NARCIS (Netherlands)

Verhagen, Jacobus Wilhelmus Hermanus Philippus

2007-01-01

In this thesis, a collection of papers is put together dealing with various quantitative aspects of predictive modelling and archaeological prospection. Among the issues covered are the effects of survey bias on the archaeological data used for predictive modelling, and the complexities of testing
The representatives of the various intersubchannel transfer mechanisms and their effects on the predictions of the ASSERT-4 subchannel code

Energy Technology Data Exchange (ETDEWEB)

Tye, P [Ecole Polytechnique, Montreal, PQ (Canada)

1994-12-31

In this paper, effects of that the constitutive relations used to represent some of the intersubchannel transfer mechanisms have on the predictions of the ASSERT-4 subchannel code for horizontal flows are examined. In particular the choices made in the representation of the gravity driven phase separation phenomena, which is unique to the horizontal fuel channel arrangement seen in CANDU reactors, are analyzed. This is done by comparing the predictions of the ASSERT-4 subchannel code with experimental data on void fraction, mass flow rate, and pressure drop obtained for two horizontal interconnected subchannels. ASSERT-4, the subchannel code used by the Canadian nuclear industry, uses an advanced drift flux model which permits departure from both thermal and mechanical equilibrium between the phases to be accurately modeled. In particular ASSERT-4 contains models for the buoyancy effects which cause phase separation between adjacent subchannels in horizontal flows. This feature, which is of great importance in the subchannel analysis of CANDU reactors, is implemented in the constitutive relationship for the relative velocity required by the conservation equations. In order to, as much as is physically possible, isolate different inter-subchannel transfer mechanisms, three different subchannel orientations are analyzed. These are: the two subchannels at the same elevation, the high void subchannel below the low void subchannel, and the high void subchannel above the low void subchannel. It is observed that for all three subchannel orientations ASSERT-4 does a reasonably good job of predicting the experimental trends. However, certain modifications to the representation of the gravitational phase separation effects which seem to improve the overall predictions are suggested. (author). 12 refs., 12 figs.
Implementation of a phenomenological DNB prediction model based on macroscale boiling flow processes in PWR fuel bundles

International Nuclear Information System (INIS)

Mohitpour, Maryam; Jahanfarnia, Gholamreza; Shams, Mehrzad

2014-01-01

Highlights: • A numerical framework was developed to mechanistically predict DNB in PWR bundles. • The DNB evaluation module was incorporated into the two-phase flow solver module. • Three-dimensional two-fluid model was the basis of two-phase flow solver module. • Liquid sublayer dryout model was adapted as CHF-triggering mechanism in DNB module. • Ability of DNB modeling approach was studied based on PSBT DNB tests in rod bundle. - Abstract: In this study, a numerical framework, comprising of a two-phase flow subchannel solver module and a Departure from Nucleate Boiling (DNB) evaluation module, was developed to mechanistically predict DNB in rod bundles of Pressurized Water Reactor (PWR). In this regard, the liquid sublayer dryout model was adapted as the Critical Heat Flux (CHF) triggering mechanism to reduce the dependency of the model on empirical correlations in the DNB evaluation module. To predict local flow boiling processes, a three-dimensional two-fluid formalism coupled with heat conduction was selected as the basic tool for the development of the two-phase flow subchannel analysis solver. Evaluation of the DNB modeling approach was performed against OECD/NRC NUPEC PWR Bundle tests (PSBT Benchmark) which supplied an extensive database for the development of truly mechanistic and consistent models for boiling transition and CHF. The results of the analyses demonstrated the need for additional assessment of the subcooled boiling model and the bulk condensation model implemented in the two-phase flow solver module. The proposed model slightly under-predicts the DNB power in comparison with the ones obtained from steady-state benchmark measurements. However, this prediction is acceptable compared with other codes. Another point about the DNB prediction model is that it has a conservative behavior. Examination of the axial and radial position of the first detected DNB using code-to-code comparisons on the basis of PSBT data indicated that the our
Kinematic Hardening: Characterization, Modeling and Impact on Springback Prediction

International Nuclear Information System (INIS)

Alves, J. L.; Bouvier, S.; Jomaa, M.; Billardon, R.; Oliveira, M. C.; Menezes, L. F.

2007-01-01

The constitutive modeling of the materials' mechanical behavior, usually carried out using a phenomenological constitutive model, i.e., a yield criterion associated to the isotropic and kinematic hardening laws, is of paramount importance in the FEM simulation of the sheet metal forming processes, as well as in the springback prediction. Among others, the kinematic behavior of the yield surface plays an essential role, since it is indispensable to describe the Bauschinger effect, i.e., the materials' answer to the multiple tension-compression cycles to which material points are submitted during the forming process. Several laws are usually used to model and describe the kinematic hardening, namely: a) the Prager's law, which describes a linear evolution of the kinematic hardening with the plastic strain rate tensor b) the Frederick-Armstrong non-linear kinematic hardening, basically a non-linear law with saturation; and c) a more advanced physically-based law, similar to the previous one but sensitive to the strain path changes. In the present paper a mixed kinematic hardening law (linear + non-linear behavior) is proposed and its implementation into a static fully-implicit FE code is described. The material parameters identification for sheet metals using different strategies, and the classical Bauschinger loading tests (i.e. in-plane forward and reverse monotonic loading), are addressed, and their impact on springback prediction evaluated. Some numerical results concerning the springback prediction of the Numisheet'05 Benchmark no. 3 are briefly presented to emphasize the importance of a correct modeling and identification of the kinematic hardening behavior
High Precision Clock Bias Prediction Model in Clock Synchronization System

Directory of Open Access Journals (Sweden)

Zan Liu

2016-01-01

Full Text Available Time synchronization is a fundamental requirement for many services provided by a distributed system. Clock calibration through the time signal is the usual way to realize the synchronization among the clocks used in the distributed system. The interference to time signal transmission or equipment failures may bring about failure to synchronize the time. To solve this problem, a clock bias prediction module is paralleled in the clock calibration system. And for improving the precision of clock bias prediction, the first-order grey model with one variable (GM(1,1 model is proposed. In the traditional GM(1,1 model, the combination of parameters determined by least squares criterion is not optimal; therefore, the particle swarm optimization (PSO is used to optimize GM(1,1 model. At the same time, in order to avoid PSO getting stuck at local optimization and improve its efficiency, the mechanisms that double subgroups and nonlinear decreasing inertia weight are proposed. In order to test the precision of the improved model, we design clock calibration experiments, where time signal is transferred via radio and wired channel, respectively. The improved model is built on the basis of clock bias acquired in the experiments. The results show that the improved model is superior to other models both in precision and in stability. The precision of improved model increased by 66.4%~76.7%.
Quantum chemical aided prediction of the thermal decomposition mechanisms and temperatures of ionic liquids

International Nuclear Information System (INIS)

Kroon, Maaike C.; Buijs, Wim; Peters, Cor J.; Witkamp, Geert-Jan

2007-01-01

The long-term thermal stability of ionic liquids is of utmost importance for their industrial application. Although the thermal decomposition temperatures of various ionic liquids have been measured previously, experimental data on the thermal decomposition mechanisms and kinetics are scarce. It is desirable to develop quantitative chemical tools that can predict thermal decomposition mechanisms and temperatures (kinetics) of ionic liquids. In this work ab initio quantum chemical calculations (DFT-B3LYP) have been used to predict thermal decomposition mechanisms, temperatures and the activation energies of the thermal breakdown reactions. These quantum chemical calculations proved to be an excellent method to predict the thermal stability of various ionic liquids
Properties predictive modeling through the concept of a hybrid interphase existing between phases in contact

Science.gov (United States)

Portan, D. V.; Papanicolaou, G. C.

2018-02-01

From practical point of view, predictive modeling based on the physics of composite material behavior is wealth generating; by guiding material system selection and process choices, by cutting down on experimentation and associated costs; and by speeding up the time frame from the research stage to the market place. The presence of areas with different properties and the existence of an interphase between them have a pronounced influence on the behavior of a composite system. The Viscoelastic Hybrid Interphase Model (VHIM), considers the existence of a non-homogeneous viscoelastic and anisotropic interphase having properties depended on the degree of adhesion between the two phases in contact. The model applies for any physical/mechanical property (e.g. mechanical, thermal, electrical and/or biomechanical). Knowing the interphasial variation of a specific property one can predict the corresponding macroscopic behavior of the composite. Moreover, the model acts as an algorithm and a two-way approach can be used: (i) phases in contact may be chosen to get the desired properties of the final composite system or (ii) the initial phases in contact determine the final behavior of the composite system, that can be approximately predicted. The VHIM has been proven, amongst others, to be extremely useful in biomaterial designing for improved contact with human tissues.
Neutrino nucleosynthesis in supernovae: Shell model predictions

International Nuclear Information System (INIS)

Haxton, W.C.

1989-01-01

Almost all of the 3 · 10 53 ergs liberated in a core collapse supernova is radiated as neutrinos by the cooling neutron star. I will argue that these neutrinos interact with nuclei in the ejected shells of the supernovae to produce new elements. It appears that this nucleosynthesis mechanism is responsible for the galactic abundances of 7 Li, 11 B, 19 F, 138 La, and 180 Ta, and contributes significantly to the abundances of about 15 other light nuclei. I discuss shell model predictions for the charged and neutral current allowed and first-forbidden responses of the parent nuclei, as well as the spallation processes that produce the new elements. 18 refs., 1 fig., 1 tab
Fingerprint verification prediction model in hand dermatitis.

Science.gov (United States)

Lee, Chew K; Chang, Choong C; Johor, Asmah; Othman, Puwira; Baba, Roshidah

2015-07-01

Hand dermatitis associated fingerprint changes is a significant problem and affects fingerprint verification processes. This study was done to develop a clinically useful prediction model for fingerprint verification in patients with hand dermatitis. A case-control study involving 100 patients with hand dermatitis. All patients verified their thumbprints against their identity card. Registered fingerprints were randomized into a model derivation and model validation group. Predictive model was derived using multiple logistic regression. Validation was done using the goodness-of-fit test. The fingerprint verification prediction model consists of a major criterion (fingerprint dystrophy area of ≥ 25%) and two minor criteria (long horizontal lines and long vertical lines). The presence of the major criterion predicts it will almost always fail verification, while presence of both minor criteria and presence of one minor criterion predict high and low risk of fingerprint verification failure, respectively. When none of the criteria are met, the fingerprint almost always passes the verification. The area under the receiver operating characteristic curve was 0.937, and the goodness-of-fit test showed agreement between the observed and expected number (P = 0.26). The derived fingerprint verification failure prediction model is validated and highly discriminatory in predicting risk of fingerprint verification in patients with hand dermatitis. © 2014 The International Society of Dermatology.
Estimation of Mechanical Signals in Induction Motors using the Recursive Prediction Error Method

DEFF Research Database (Denmark)

Børsting, H.; Knudsen, Morten; Rasmussen, Henrik

1993-01-01

Sensor feedback of mechanical quantities for control applications in induction motors is troublesome and relative expensive. In this paper a recursive prediction error (RPE) method has successfully been used to estimate the angular rotor speed ........Sensor feedback of mechanical quantities for control applications in induction motors is troublesome and relative expensive. In this paper a recursive prediction error (RPE) method has successfully been used to estimate the angular rotor speed .....
Prediction model for carbonation depth of concrete subjected to freezing-thawing cycles

Science.gov (United States)

Xiao, Qian Hui; Li, Qiang; Guan, Xiao; Xian Zou, Ying

2018-03-01

Through the indoor simulation test of the concrete durability under the coupling effect of freezing-thawing and carbonation, the variation regularity of concrete neutralization depth under freezing-thawing and carbonation was obtained. Based on concrete carbonation mechanism, the relationship between the air diffusion coefficient and porosity in concrete was analyzed and the calculation method of porosity in Portland cement concrete and fly ash cement concrete was investigated, considering the influence of the freezing-thawing damage on the concrete diffusion coefficient. Finally, a prediction model of carbonation depth of concrete under freezing-thawing circumstance was established. The results obtained using this prediction model agreed well with the experimental test results, and provided a theoretical reference and basis for the concrete durability analysis under multi-factor environments.

Finding Furfural Hydrogenation Catalysts via Predictive Modelling.

Science.gov (United States)

Strassberger, Zea; Mooijman, Maurice; Ruijter, Eelco; Alberts, Albert H; Maldonado, Ana G; Orru, Romano V A; Rothenberg, Gadi

2010-09-10

We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre throughout the reaction. Deuterium-labelling studies showed a secondary isotope effect (k(H):k(D)=1.5). Further mechanistic studies showed that this transfer hydrogenation follows the so-called monohydride pathway. Using these data, we built a predictive model for 13 of the catalysts, based on 2D and 3D molecular descriptors. We tested and validated the model using the remaining five catalysts (cross-validation, R(2)=0.913). Then, with this model, the conversion and selectivity were predicted for four completely new ruthenium-carbene complexes. These four catalysts were then synthesized and tested. The results were within 3% of the model's predictions, demonstrating the validity and value of predictive modelling in catalyst optimization.
Model Predictive Control for Smart Energy Systems

DEFF Research Database (Denmark)

Halvgaard, Rasmus

pumps, heat tanks, electrical vehicle battery charging/discharging, wind farms, power plants). 2.Embed forecasting methodologies for the weather (e.g. temperature, solar radiation), the electricity consumption, and the electricity price in a predictive control system. 3.Develop optimization algorithms....... Chapter 3 introduces Model Predictive Control (MPC) including state estimation, filtering and prediction for linear models. Chapter 4 simulates the models from Chapter 2 with the certainty equivalent MPC from Chapter 3. An economic MPC minimizes the costs of consumption based on real electricity prices...... that determined the flexibility of the units. A predictive control system easily handles constraints, e.g. limitations in power consumption, and predicts the future behavior of a unit by integrating predictions of electricity prices, consumption, and weather variables. The simulations demonstrate the expected...
Shear design and assessment of reinforced and prestressed concrete beams based on a mechanical model

OpenAIRE

Marí Bernat, Antonio Ricardo; Bairán García, Jesús Miguel; Cladera Bohigas, Antoni; Oller Ibars, Eva

2016-01-01

Safe and economical design and assessment of reinforced (RC) and prestressed concrete (PC) beams requires the availability of accurate but simple formulations which adequately capture the structural response. In this paper, a mechanical model for the prediction of the shear-flexural strength of PC and RC members with rectangular, I, or T sections, with and without shear reinforcement, is presented. The model is based on the principles of concrete mechanics and on assumptions supported by the ...
Predicting tube repair at French nuclear steam generators using statistical modeling

Energy Technology Data Exchange (ETDEWEB)

Mathon, C., E-mail: cedric.mathon@edf.fr [EDF Generation, Basic Design Department (SEPTEN), 69628 Villeurbanne (France); Chaudhary, A. [EDF Generation, Basic Design Department (SEPTEN), 69628 Villeurbanne (France); Gay, N.; Pitner, P. [EDF Generation, Nuclear Operation Division (UNIE), Saint-Denis (France)

2014-04-01

Electricité de France (EDF) currently operates a total of 58 Nuclear Pressurized Water Reactors (PWR) which are composed of 34 units of 900 MWe, 20 units of 1300 MWe and 4 units of 1450 MWe. This report provides an overall status of SG tube bundles on the 1300 MWe units. These units are 4 loop reactors using the AREVA 68/19 type SG model which are equipped either with Alloy 600 thermally treated (TT) tubes or Alloy 690 TT tubes. As of 2011, the effective full power years of operation (EFPY) ranges from 13 to 20 and during this time, the main degradation mechanisms observed on SG tubes are primary water stress corrosion cracking (PWSCC) and wear at anti-vibration bars (AVB) level. Statistical models have been developed for each type of degradation in order to predict the growth rate and number of affected tubes. Additional plugging is also performed to prevent other degradations such as tube wear due to foreign objects or high-cycle flow-induced fatigue. The contribution of these degradation mechanisms on the rate of tube plugging is described. The results from the statistical models are then used in predicting the long-term life of the steam generators and therefore providing a useful tool toward their effective life management and possible replacement.
Mechanisms for integration of information models across related domains

Science.gov (United States)

Atkinson, Rob

2010-05-01

It is well recognised that there are opportunities and challenges in cross-disciplinary data integration. A significant barrier, however, is creating a conceptual model of the combined domains and the area of integration. For example, a groundwater domain application may require information from several related domains: geology, hydrology, water policy, etc. Each domain may have its own data holdings and conceptual models, but these will share various common concepts (eg. The concept of an aquifer). These areas of semantic overlap present significant challenges, firstly to choose a single representation (model) of a concept that appears in multiple disparate models,, then to harmonise these other models with the single representation. In addition, models may exist at different levels of abstraction depending on how closely aligned they are with a particular implementation. This makes it hard for modellers in one domain to introduce elements from another domain without either introducing a specific style of implementation, or conversely dealing with a set of abstract patterns that are hard to integrate with existing implementations. Models are easier to integrate if they are broken down into small units, with common concepts implemented using common models from well-known, and predictably managed shared libraries. This vision however requires development of a set of mechanisms (tools and procedures) for implementing and exploiting libraries of model components. These mechanisms need to handle publication, discovery, subscription, versioning and implementation of models in different forms. In this presentation a coherent suite of such mechanisms is proposed, using a scenario based on re-use of geosciences models. This approach forms the basis of a comprehensive strategy to empower domain modellers to create more interoperable systems. The strategy address a range of concerns and practice, and includes methodologies, an accessible toolkit, improvements to available
Pattern-oriented modelling: a ‘multi-scope’ for predictive systems ecology

Science.gov (United States)

Grimm, Volker; Railsback, Steven F.

2012-01-01

Modern ecology recognizes that modelling systems across scales and at multiple levels—especially to link population and ecosystem dynamics to individual adaptive behaviour—is essential for making the science predictive. ‘Pattern-oriented modelling’ (POM) is a strategy for doing just this. POM is the multi-criteria design, selection and calibration of models of complex systems. POM starts with identifying a set of patterns observed at multiple scales and levels that characterize a system with respect to the particular problem being modelled; a model from which the patterns emerge should contain the right mechanisms to address the problem. These patterns are then used to (i) determine what scales, entities, variables and processes the model needs, (ii) test and select submodels to represent key low-level processes such as adaptive behaviour, and (iii) find useful parameter values during calibration. Patterns are already often used in these ways, but a mini-review of applications of POM confirms that making the selection and use of patterns more explicit and rigorous can facilitate the development of models with the right level of complexity to understand ecological systems and predict their response to novel conditions. PMID:22144392
Mathematical modelling in solid mechanics

CERN Document Server

Sofonea, Mircea; Steigmann, David

2017-01-01

This book presents new research results in multidisciplinary fields of mathematical and numerical modelling in mechanics. The chapters treat the topics: mathematical modelling in solid, fluid and contact mechanics nonconvex variational analysis with emphasis to nonlinear solid and structural mechanics numerical modelling of problems with non-smooth constitutive laws, approximation of variational and hemivariational inequalities, numerical analysis of discrete schemes, numerical methods and the corresponding algorithms, applications to mechanical engineering numerical aspects of non-smooth mechanics, with emphasis on developing accurate and reliable computational tools mechanics of fibre-reinforced materials behaviour of elasto-plastic materials accounting for the microstructural defects definition of structural defects based on the differential geometry concepts or on the atomistic basis interaction between phase transformation and dislocations at nano-scale energetic arguments bifurcation and post-buckling a...
Accelerated testing of space mechanisms

Science.gov (United States)

Murray, S. Frank; Heshmat, Hooshang

1995-01-01

This report contains a review of various existing life prediction techniques used for a wide range of space mechanisms. Life prediction techniques utilized in other non-space fields such as turbine engine design are also reviewed for applicability to many space mechanism issues. The development of new concepts on how various tribological processes are involved in the life of the complex mechanisms used for space applications are examined. A 'roadmap' for the complete implementation of a tribological prediction approach for complex mechanical systems including standard procedures for test planning, analytical models for life prediction and experimental verification of the life prediction and accelerated testing techniques are discussed. A plan is presented to demonstrate a method for predicting the life and/or performance of a selected space mechanism mechanical component.
Active Tension Network model reveals an exotic mechanical state realized in epithelial tissues

Science.gov (United States)

Noll, Nicholas; Mani, Madhav; Heemskerk, Idse; Streicha, Sebastian; Shraiman, Boris

Mechanical interactions play a crucial role in epithelial morphogenesis, yet understanding the complex mechanisms through which stress and deformation affect cell behavior remains an open problem. Here we formulate and analyze the Active Tension Network (ATN) model, which assumes that mechanical balance of cells is dominated by cortical tension and introduces tension dependent active remodeling of the cortex. We find that ATNs exhibit unusual mechanical properties: i) ATN behaves as a fluid at short times, but at long times it supports external tension, like a solid; ii) its mechanical equilibrium state has extensive degeneracy associated with a discrete conformal - ''isogonal'' - deformation of cells. ATN model predicts a constraint on equilibrium cell geometry, which we demonstrate to hold in certain epithelial tissues. We further show that isogonal modes are observed in a fruit fly embryo, accounting for the striking variability of apical area of ventral cells and helping understand the early phase of gastrulation. Living matter realizes new and exotic mechanical states, understanding which helps understand biological phenomena.
Coupling between cracking and permeability, a model for structure service life prediction

International Nuclear Information System (INIS)

Lasne, M.; Gerard, B.; Breysse, D.

1993-01-01

Many authors have chosen permeability coefficients (permeation, diffusion) as a reference for material durability and for structure service life prediction. When we look for designing engineered barriers for radioactive waste storage we find these macroscopic parameters very essential. In order to work with a predictive model of transfer properties evolution in a porous media (concrete, mortar, rock) we introduce a 'micro-macro' hierarchical model of permeability whose data are the total porosity and the pore size distribution. In spite of the simplicity of the model (very small CPU time consuming) comparative studies show predictive results for sound cement pastes, mortars and concretes. Associated to these works we apply a model of damage due to hydration processes at early ages to a container as a preliminary underproject for the definitive storage of Low Level radioactive Waste (LLW). Data are geometry, cement properties and damage measurement of concrete. This model takes into account the mechanical property of the concrete maturation (volumic variations during cement hydration can damage the structures). Some local microcracking can appear and affect the long term durability. Following these works we introduce our research program for the concrete cracking analysis. An experimental campaign is designed in order to determine damage-cracking-porosity-permeability coupling. (authors). 12 figs., 16 refs
Prediction skill of rainstorm events over India in the TIGGE weather prediction models

Science.gov (United States)

Karuna Sagar, S.; Rajeevan, M.; Vijaya Bhaskara Rao, S.; Mitra, A. K.

2017-12-01

Extreme rainfall events pose a serious threat of leading to severe floods in many countries worldwide. Therefore, advance prediction of its occurrence and spatial distribution is very essential. In this paper, an analysis has been made to assess the skill of numerical weather prediction models in predicting rainstorms over India. Using gridded daily rainfall data set and objective criteria, 15 rainstorms were identified during the monsoon season (June to September). The analysis was made using three TIGGE (THe Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble) models. The models considered are the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centre for Environmental Prediction (NCEP) and the UK Met Office (UKMO). Verification of the TIGGE models for 43 observed rainstorm days from 15 rainstorm events has been made for the period 2007-2015. The comparison reveals that rainstorm events are predictable up to 5 days in advance, however with a bias in spatial distribution and intensity. The statistical parameters like mean error (ME) or Bias, root mean square error (RMSE) and correlation coefficient (CC) have been computed over the rainstorm region using the multi-model ensemble (MME) mean. The study reveals that the spread is large in ECMWF and UKMO followed by the NCEP model. Though the ensemble spread is quite small in NCEP, the ensemble member averages are not well predicted. The rank histograms suggest that the forecasts are under prediction. The modified Contiguous Rain Area (CRA) technique was used to verify the spatial as well as the quantitative skill of the TIGGE models. Overall, the contribution from the displacement and pattern errors to the total RMSE is found to be more in magnitude. The volume error increases from 24 hr forecast to 48 hr forecast in all the three models.
Meta-analysis of high-latitude nitrogen-addition and warming studies implies ecological mechanisms overlooked by land models

Science.gov (United States)

Bouskill, N. J.; Riley, W. J.; Tang, J. Y.

2014-12-01

Accurate representation of ecosystem processes in land models is crucial for reducing predictive uncertainty in energy and greenhouse gas feedbacks with the climate. Here we describe an observational and modeling meta-analysis approach to benchmark land models, and apply the method to the land model CLM4.5 with two versions of belowground biogeochemistry. We focused our analysis on the aboveground and belowground responses to warming and nitrogen addition in high-latitude ecosystems, and identified absent or poorly parameterized mechanisms in CLM4.5. While the two model versions predicted similar soil carbon stock trajectories following both warming and nitrogen addition, other predicted variables (e.g., belowground respiration) differed from observations in both magnitude and direction, indicating that CLM4.5 has inadequate underlying mechanisms for representing high-latitude ecosystems. On the basis of observational synthesis, we attribute the model-observation differences to missing representations of microbial dynamics, aboveground and belowground coupling, and nutrient cycling, and we use the observational meta-analysis to discuss potential approaches to improving the current models. However, we also urge caution concerning the selection of data sets and experiments for meta-analysis. For example, the concentrations of nitrogen applied in the synthesized field experiments (average = 72 kg ha-1 yr-1) are many times higher than projected soil nitrogen concentrations (from nitrogen deposition and release during mineralization), which precludes a rigorous evaluation of the model responses to likely nitrogen perturbations. Overall, we demonstrate that elucidating ecological mechanisms via meta-analysis can identify deficiencies in ecosystem models and empirical experiments.
Modeling of thermo-mechanical and irradiation behavior of mixed oxide fuel for sodium fast reactors

International Nuclear Information System (INIS)

Karahan, Aydin; Buongiorno, Jacopo

2010-01-01

An engineering code to model the irradiation behavior of UO 2 -PuO 2 mixed oxide fuel pins in sodium-cooled fast reactors was developed. The code was named fuel engineering and structural analysis tool (FEAST-OXIDE). FEAST-OXIDE has several modules working in coupled form with an explicit numerical algorithm. These modules describe: (1) fission gas release and swelling, (2) fuel chemistry and restructuring, (3) temperature distribution, (4) fuel-clad chemical interaction and (5) fuel-clad mechanical analysis. Given the fuel pin geometry, composition and irradiation history, FEAST-OXIDE can analyze fuel and cladding thermo-mechanical behavior at both steady-state and design-basis transient scenarios. The code was written in FORTRAN-90 program language. The mechanical analysis module implements the LIFE algorithm. Fission gas release and swelling behavior is described by the OGRES and NEFIG models. However, the original OGRES model has been extended to include the effects of joint oxide gain (JOG) formation on fission gas release and swelling. A detailed fuel chemistry model has been included to describe the cesium radial migration and JOG formation, oxygen and plutonium radial distribution and the axial migration of cesium. The fuel restructuring model includes the effects of as-fabricated porosity migration, irradiation-induced fuel densification, grain growth, hot pressing and fuel cracking and relocation. Finally, a kinetics model is included to predict the clad wastage formation. FEAST-OXIDE predictions have been compared to the available FFTF, EBR-II and JOYO databases, as well as the LIFE-4 code predictions. The agreement was found to be satisfactory for steady-state and slow-ramp over-power accidents.
Modeling of thermo-mechanical and irradiation behavior of mixed oxide fuel for sodium fast reactors

Energy Technology Data Exchange (ETDEWEB)

Karahan, Aydin, E-mail: karahan@mit.ed [Center for Advanced Nuclear Energy Systems, Nuclear Science and Engineering Department, Massachusetts Institute of Technology, MA (United States); Buongiorno, Jacopo [Center for Advanced Nuclear Energy Systems, Nuclear Science and Engineering Department, Massachusetts Institute of Technology, MA (United States)

2010-01-31

An engineering code to model the irradiation behavior of UO{sub 2}-PuO{sub 2} mixed oxide fuel pins in sodium-cooled fast reactors was developed. The code was named fuel engineering and structural analysis tool (FEAST-OXIDE). FEAST-OXIDE has several modules working in coupled form with an explicit numerical algorithm. These modules describe: (1) fission gas release and swelling, (2) fuel chemistry and restructuring, (3) temperature distribution, (4) fuel-clad chemical interaction and (5) fuel-clad mechanical analysis. Given the fuel pin geometry, composition and irradiation history, FEAST-OXIDE can analyze fuel and cladding thermo-mechanical behavior at both steady-state and design-basis transient scenarios. The code was written in FORTRAN-90 program language. The mechanical analysis module implements the LIFE algorithm. Fission gas release and swelling behavior is described by the OGRES and NEFIG models. However, the original OGRES model has been extended to include the effects of joint oxide gain (JOG) formation on fission gas release and swelling. A detailed fuel chemistry model has been included to describe the cesium radial migration and JOG formation, oxygen and plutonium radial distribution and the axial migration of cesium. The fuel restructuring model includes the effects of as-fabricated porosity migration, irradiation-induced fuel densification, grain growth, hot pressing and fuel cracking and relocation. Finally, a kinetics model is included to predict the clad wastage formation. FEAST-OXIDE predictions have been compared to the available FFTF, EBR-II and JOYO databases, as well as the LIFE-4 code predictions. The agreement was found to be satisfactory for steady-state and slow-ramp over-power accidents.
Probabilistic Fatigue Life Prediction of Bridge Cables Based on Multiscaling and Mesoscopic Fracture Mechanics

Directory of Open Access Journals (Sweden)

Zhongxiang Liu

2016-04-01

Full Text Available Fatigue fracture of bridge stay-cables is usually a multiscale process as the crack grows from micro-scale to macro-scale. Such a process, however, is highly uncertain. In order to make a rational prediction of the residual life of bridge cables, a probabilistic fatigue approach is proposed, based on a comprehensive vehicle load model, finite element analysis and multiscaling and mesoscopic fracture mechanics. Uncertainties in both material properties and external loads are considered. The proposed method is demonstrated through the fatigue life prediction of cables of the Runyang Cable-Stayed Bridge in China, and it is found that cables along the bridge spans may have significantly different fatigue lives, and due to the variability, some of them may have shorter lives than those as expected from the design.
Model for the prediction of subsurface strata movement due to underground mining

Science.gov (United States)

Cheng, Jianwei; Liu, Fangyuan; Li, Siyuan

2017-12-01

The problem of ground control stability due to large underground mining operations is often associated with large movements and deformations of strata. It is a complicated problem, and can induce severe safety or environmental hazards either at the surface or in strata. Hence, knowing the subsurface strata movement characteristics, and making any subsidence predictions in advance, are desirable for mining engineers to estimate any damage likely to affect the ground surface or subsurface strata. Based on previous research findings, this paper broadly applies a surface subsidence prediction model based on the influence function method to subsurface strata, in order to predict subsurface stratum movement. A step-wise prediction model is proposed, to investigate the movement of underground strata. The model involves a dynamic iteration calculation process to derive the movements and deformations for each stratum layer; modifications to the influence method function are also made for more precise calculations. The critical subsidence parameters, incorporating stratum mechanical properties and the spatial relationship of interest at the mining level, are thoroughly considered, with the purpose of improving the reliability of input parameters. Such research efforts can be very helpful to mining engineers’ understanding of the moving behavior of all strata over underground excavations, and assist in making any damage mitigation plan. In order to check the reliability of the model, two methods are carried out and cross-validation applied. One is to use a borehole TV monitor recording to identify the progress of subsurface stratum bedding and caving in a coal mine, the other is to conduct physical modelling of the subsidence in underground strata. The results of these two methods are used to compare with theoretical results calculated by the proposed mathematical model. The testing results agree well with each other, and the acceptable accuracy and reliability of the
Predicting climate-induced range shifts: model differences and model reliability.

Science.gov (United States)

Joshua J. Lawler; Denis White; Ronald P. Neilson; Andrew R. Blaustein

2006-01-01

Predicted changes in the global climate are likely to cause large shifts in the geographic ranges of many plant and animal species. To date, predictions of future range shifts have relied on a variety of modeling approaches with different levels of model accuracy. Using a common data set, we investigated the potential implications of alternative modeling approaches for...
A Binaural Grouping Model for Predicting Speech Intelligibility in Multitalker Environments

Directory of Open Access Journals (Sweden)

Jing Mi

2016-09-01

Full Text Available Spatially separating speech maskers from target speech often leads to a large intelligibility improvement. Modeling this phenomenon has long been of interest to binaural-hearing researchers for uncovering brain mechanisms and for improving signal-processing algorithms in hearing-assistive devices. Much of the previous binaural modeling work focused on the unmasking enabled by binaural cues at the periphery, and little quantitative modeling has been directed toward the grouping or source-separation benefits of binaural processing. In this article, we propose a binaural model that focuses on grouping, specifically on the selection of time-frequency units that are dominated by signals from the direction of the target. The proposed model uses Equalization-Cancellation (EC processing with a binary decision rule to estimate a time-frequency binary mask. EC processing is carried out to cancel the target signal and the energy change between the EC input and output is used as a feature that reflects target dominance in each time-frequency unit. The processing in the proposed model requires little computational resources and is straightforward to implement. In combination with the Coherence-based Speech Intelligibility Index, the model is applied to predict the speech intelligibility data measured by Marrone et al. The predicted speech reception threshold matches the pattern of the measured data well, even though the predicted intelligibility improvements relative to the colocated condition are larger than some of the measured data, which may reflect the lack of internal noise in this initial version of the model.
A Binaural Grouping Model for Predicting Speech Intelligibility in Multitalker Environments.

Science.gov (United States)

Mi, Jing; Colburn, H Steven

2016-10-03

Spatially separating speech maskers from target speech often leads to a large intelligibility improvement. Modeling this phenomenon has long been of interest to binaural-hearing researchers for uncovering brain mechanisms and for improving signal-processing algorithms in hearing-assistive devices. Much of the previous binaural modeling work focused on the unmasking enabled by binaural cues at the periphery, and little quantitative modeling has been directed toward the grouping or source-separation benefits of binaural processing. In this article, we propose a binaural model that focuses on grouping, specifically on the selection of time-frequency units that are dominated by signals from the direction of the target. The proposed model uses Equalization-Cancellation (EC) processing with a binary decision rule to estimate a time-frequency binary mask. EC processing is carried out to cancel the target signal and the energy change between the EC input and output is used as a feature that reflects target dominance in each time-frequency unit. The processing in the proposed model requires little computational resources and is straightforward to implement. In combination with the Coherence-based Speech Intelligibility Index, the model is applied to predict the speech intelligibility data measured by Marrone et al. The predicted speech reception threshold matches the pattern of the measured data well, even though the predicted intelligibility improvements relative to the colocated condition are larger than some of the measured data, which may reflect the lack of internal noise in this initial version of the model. © The Author(s) 2016.
Modeling and control design of a stand alone wind energy conversion system based on functional model predictive control

Energy Technology Data Exchange (ETDEWEB)

Kassem, Ahmed M. [Beni-Suef University, Electrical Dept., Beni Suef (Egypt)

2012-09-15

This paper investigates the application of the model predictive control (MPC) approach to control the voltage and frequency of a stand alone wind generation system. This scheme consists of a wind turbine which drives an induction generator feeding an isolated load. A static VAR compensator is connected at the induction generator terminals to regulate the load voltage. The rotor speed, and thereby the load frequency are controlled via adjusting the mechanical power input using the blade pitch-angle. The MPC is used to calculate the optimal control actions including system constraints. To alleviate computational effort and to reduce numerical problems, particularly in large prediction horizon, an exponentially weighted functional model predictive control (FMPC) is employed. Digital simulations have been carried out in order to validate the effectiveness of the proposed scheme. The proposed controller has been tested through step changes in the wind speed and the load impedance. Simulation results show that adequate performance of the proposed wind energy scheme has been achieved. Moreover, this scheme is robust against the parameters variation and eliminates the influence of modeling and measurement errors. (orig.)

Predictive Mechanisms Are Not Involved the Same Way during Human-Human vs. Human-Machine Interactions: A Review

Directory of Open Access Journals (Sweden)

Aïsha Sahaï

2017-10-01

Full Text Available Nowadays, interactions with others do not only involve human peers but also automated systems. Many studies suggest that the motor predictive systems that are engaged during action execution are also involved during joint actions with peers and during other human generated action observation. Indeed, the comparator model hypothesis suggests that the comparison between a predicted state and an estimated real state enables motor control, and by a similar functioning, understanding and anticipating observed actions. Such a mechanism allows making predictions about an ongoing action, and is essential to action regulation, especially during joint actions with peers. Interestingly, the same comparison process has been shown to be involved in the construction of an individual's sense of agency, both for self-generated and observed other human generated actions. However, the implication of such predictive mechanisms during interactions with machines is not consensual, probably due to the high heterogeneousness of the automata used in the experimentations, from very simplistic devices to full humanoid robots. The discrepancies that are observed during human/machine interactions could arise from the absence of action/observation matching abilities when interacting with traditional low-level automata. Consistently, the difficulties to build a joint agency with this kind of machines could stem from the same problem. In this context, we aim to review the studies investigating predictive mechanisms during social interactions with humans and with automated artificial systems. We will start by presenting human data that show the involvement of predictions in action control and in the sense of agency during social interactions. Thereafter, we will confront this literature with data from the robotic field. Finally, we will address the upcoming issues in the field of robotics related to automated systems aimed at acting as collaborative agents.
Model predictive control classical, robust and stochastic

CERN Document Server

Kouvaritakis, Basil

2016-01-01

For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...
Computational thermofracture mechanics and life prediction

International Nuclear Information System (INIS)

Hsu Tairan

1992-01-01

This paper will present computational techniques used for the prediction of the thermofracture behaviour of structures subject to either monotonic or cyclic combined thermal and mechanical loadings. Two specific areas will be dealt with in the paper. (1) The Time-invariant thermofracture of leaking pipelines with non-uniform temperature fields; in this case, the induced non-uniform temperature fields near leaking cracks have shown to be significant. The severity of these temperature fields on the thermofracture behaviour of the pipeline will be demonstrated by a numerical example. (2) Thermomechanical creep fracture of structures: Recent developments, including those of the author's own work, on cyclic creep-fracture using damage theory will be presented. Long 'hold' and 'dwell' times, which occur in the actual operations of nuclear power plant components have been shown to have a significant effect on the overall creep-fracture behaviour of the material. Constitutive laws, which include most of these effects, have been incorporated into the existing TEPSAC code for the prediction of crack growth in solids under cyclic creep loadings. The effectiveness of using the damage parameters as fracture criteria, and the presence of plastic deformation in the overall results will be assessed. (orig.)
Comparative Analysis of Soft Computing Models in Prediction of Bending Rigidity of Cotton Woven Fabrics

Science.gov (United States)

Guruprasad, R.; Behera, B. K.

2015-10-01

Quantitative prediction of fabric mechanical properties is an essential requirement for design engineering of textile and apparel products. In this work, the possibility of prediction of bending rigidity of cotton woven fabrics has been explored with the application of Artificial Neural Network (ANN) and two hybrid methodologies, namely Neuro-genetic modeling and Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling. For this purpose, a set of cotton woven grey fabrics was desized, scoured and relaxed. The fabrics were then conditioned and tested for bending properties. With the database thus created, a neural network model was first developed using back propagation as the learning algorithm. The second model was developed by applying a hybrid learning strategy, in which genetic algorithm was first used as a learning algorithm to optimize the number of neurons and connection weights of the neural network. The Genetic algorithm optimized network structure was further allowed to learn using back propagation algorithm. In the third model, an ANFIS modeling approach was attempted to map the input-output data. The prediction performances of the models were compared and a sensitivity analysis was reported. The results show that the prediction by neuro-genetic and ANFIS models were better in comparison with that of back propagation neural network model.
Modeling of the mechanical behavior of austenitic stainless steels under pure fatigue and fatigue relaxation loadings

International Nuclear Information System (INIS)

Hajjaji-Rachdi, Fatima

2015-01-01

Austenitic stainless steels are potential candidates for structural components of sodium-cooled fast neutron reactors. Many of these components will be subjected to cyclic loadings including long hold times (1 month) under creep or relaxation at high temperature. These hold times are unattainable experimentally. The aim of the present study is to propose mechanical models which take into account the involved mechanisms and their interactions during such complex loadings. First, an experimental study of the pure fatigue and fatigue-relaxation behavior of 316L(N) at 500 C has been carried out with very long hold times (10 h and 50 h) compared with the ones studied in literature. Tensile tests at 600 C with different applied strain rates have been undertaken in order to study the dynamic strain ageing phenomenon. Before focusing on more complex loadings, the mean field homogenization approach has been used to predict the mechanical behavior of different FCC metals and alloys under low cycle fatigue at room temperature. Both Hill-Hutchinson and Kroener models have been used. Next, a physically-based model based on dislocation densities has been developed and its parameters measured. The model allows predictions in a qualitative agreement with experimental data for tensile loadings. Finally, this model has been enriched to take into account visco-plasticity, dislocation climb and interaction between dislocations and solute atoms, which are influent during creep-fatigue or fatigue relaxation at high temperature. The proposed model uses three adjustable parameters only and allows rather accurate prediction of the behavior of 316L(N) steel under tensile loading and relaxation. (author) [fr
Experimental study and constitutive modeling of the viscoelastic mechanical properties of the human prolapsed vaginal tissue.

Science.gov (United States)

Peña, Estefania; Calvo, B; Martínez, M A; Martins, P; Mascarenhas, T; Jorge, R M N; Ferreira, A; Doblaré, M

2010-02-01

In this paper, the viscoelastic mechanical properties of vaginal tissue are investigated. Using previous results of the authors on the mechanical properties of biological soft tissues and newly experimental data from uniaxial tension tests, a new model for the viscoelastic mechanical properties of the human vaginal tissue is proposed. The structural model seems to be sufficiently accurate to guarantee its application to prediction of reliable stress distributions, and is suitable for finite element computations. The obtained results may be helpful in the design of surgical procedures with autologous tissue or prostheses.
Model predictive Controller for Mobile Robot

OpenAIRE

Alireza Rezaee

2017-01-01

This paper proposes a Model Predictive Controller (MPC) for control of a P2AT mobile robot. MPC refers to a group of controllers that employ a distinctly identical model of process to predict its future behavior over an extended prediction horizon. The design of a MPC is formulated as an optimal control problem. Then this problem is considered as linear quadratic equation (LQR) and is solved by making use of Ricatti equation. To show the effectiveness of the proposed method this controller is...
Deep Predictive Models in Interactive Music

OpenAIRE

Martin, Charles P.; Ellefsen, Kai Olav; Torresen, Jim

2018-01-01

Automatic music generation is a compelling task where much recent progress has been made with deep learning models. In this paper, we ask how these models can be integrated into interactive music systems; how can they encourage or enhance the music making of human users? Musical performance requires prediction to operate instruments, and perform in groups. We argue that predictive models could help interactive systems to understand their temporal context, and ensemble behaviour. Deep learning...
Prediction of Proper Temperatures for the Hot Stamping Process Based on the Kinetics Models

Science.gov (United States)

Samadian, P.; Parsa, M. H.; Mirzadeh, H.

2015-02-01

Nowadays, the application of kinetics models for predicting microstructures of steels subjected to thermo-mechanical treatments has increased to minimize direct experimentation, which is costly and time consuming. In the current work, the final microstructures of AISI 4140 steel sheets after the hot stamping process were predicted using the Kirkaldy and Li kinetics models combined with new thermodynamically based models in order for the determination of the appropriate process temperatures. In this way, the effect of deformation during hot stamping on the Ae3, Acm, and Ae1 temperatures was considered, and then the equilibrium volume fractions of phases at different temperatures were calculated. Moreover, the ferrite transformation rate equations of the Kirkaldy and Li models were modified by a term proposed by Åkerström to consider the influence of plastic deformation. Results showed that the modified Kirkaldy model is satisfactory for the determination of appropriate austenitization temperatures for the hot stamping process of AISI 4140 steel sheets because of agreeable microstructure predictions in comparison with the experimental observations.
Risk prediction model: Statistical and artificial neural network approach

Science.gov (United States)

Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

2017-04-01

Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.
Accuracy of depolarization and delay spread predictions using advanced ray-based modeling in indoor scenarios

Directory of Open Access Journals (Sweden)

Mani Francesco

2011-01-01

Full Text Available Abstract This article investigates the prediction accuracy of an advanced deterministic propagation model in terms of channel depolarization and frequency selectivity for indoor wireless propagation. In addition to specular reflection and diffraction, the developed ray tracing tool considers penetration through dielectric blocks and/or diffuse scattering mechanisms. The sensitivity and prediction accuracy analysis is based on two measurement campaigns carried out in a warehouse and an office building. It is shown that the implementation of diffuse scattering into RT significantly increases the accuracy of the cross-polar discrimination prediction, whereas the delay-spread prediction is only marginally improved.
Evaluation of CASP8 model quality predictions

KAUST Repository

Cozzetto, Domenico

2009-01-01

The model quality assessment problem consists in the a priori estimation of the overall and per-residue accuracy of protein structure predictions. Over the past years, a number of methods have been developed to address this issue and CASP established a prediction category to evaluate their performance in 2006. In 2008 the experiment was repeated and its results are reported here. Participants were invited to infer the correctness of the protein models submitted by the registered automatic servers. Estimates could apply to both whole models and individual amino acids. Groups involved in the tertiary structure prediction categories were also asked to assign local error estimates to each predicted residue in their own models and their results are also discussed here. The correlation between the predicted and observed correctness measures was the basis of the assessment of the results. We observe that consensus-based methods still perform significantly better than those accepting single models, similarly to what was concluded in the previous edition of the experiment. © 2009 WILEY-LISS, INC.
Confocal arthroscopy-based patient-specific constitutive models of cartilaginous tissues - II: prediction of reaction force history of meniscal cartilage specimens.

Science.gov (United States)

Taylor, Zeike A; Kirk, Thomas B; Miller, Karol

2007-10-01

The theoretical framework developed in a companion paper (Part I) is used to derive estimates of mechanical response of two meniscal cartilage specimens. The previously developed framework consisted of a constitutive model capable of incorporating confocal image-derived tissue microstructural data. In the present paper (Part II) fibre and matrix constitutive parameters are first estimated from mechanical testing of a batch of specimens similar to, but independent from those under consideration. Image analysis techniques which allow estimation of tissue microstructural parameters form confocal images are presented. The constitutive model and image-derived structural parameters are then used to predict the reaction force history of the two meniscal specimens subjected to partially confined compression. The predictions are made on the basis of the specimens' individual structural condition as assessed by confocal microscopy and involve no tuning of material parameters. Although the model does not reproduce all features of the experimental curves, as an unfitted estimate of mechanical response the prediction is quite accurate. In light of the obtained results it is judged that more general non-invasive estimation of tissue mechanical properties is possible using the developed framework.
Dynamical response of multi-walled carbon nanotube resonators based on continuum mechanics modeling for mass sensing applications

Energy Technology Data Exchange (ETDEWEB)

Choi, Myungseok; Olshevskiy, Alexander; Kim, Chang-Wan [Konkuk University, Seoul (Korea, Republic of); Eom, Kilho [Sungkyunkwan University, Suwon (Korea, Republic of); Gwak, Kwanwoong [Sejong University, Seoul (Korea, Republic of); Dai, Mai Duc [Ho Chi Minh City University of Technology and Education, Ho Chi Minh (Viet Nam)

2017-05-15

Carbon nanotube (CNT) has recently received much attention due to its excellent electromechanical properties, indicating that CNT can be employed for development of Nanoelectromechanical system (NEMS) such as nanomechanical resonators. For effective design of CNT-based resonators, it is required to accurately predict the vibration behavior of CNT resonators as well as their frequency response to mass adsorption. In this work, we have studied the vibrational behavior of Multi-walled CNT (MWCNT) resonators by using a continuum mechanics modeling that was implemented in Finite element method (FEM). In particular, we consider a transversely isotropic hollow cylinder solid model with Finite element (FE) implementation for modeling the vibration behavior of Multi-walled CNT (MWCNT) resonators. It is shown that our continuum mechanics model provides the resonant frequencies of various MWCNTs being comparable to those obtained from experiments. Moreover, we have investigated the frequency response of MWCNT resonators to mass adsorption by using our continuum model with FE implementation. Our study sheds light on our continuum mechanics model that is useful in predicting not only the vibration behavior of MWCNT resonators but also their sensing performance for further effective design of MWCNT- based NEMS devices.
An application of liquid sublayer dryout mechanism to the prediction of critical heat flux under low pressure and low velocity conditions in round tubes

International Nuclear Information System (INIS)

Lee, Kwang-Won; Yang, Jae-Young; Baik, Se-Jin

1997-01-01

Based on several experimental evidences for nucleate boiling in annular film and the existence of residual liquid film flow rate at the critical heat flux (CHF) location, the liquid sublayer dryout (LSD) mechanism under annular film is firstly introduced to evaluate the CHF data at low pressure and low velocity (LPLV) conditions, which would not be predicted by a normal annular film dryout (AFD) model. In this study, the CHF occurrence due to annular film separation or breaking down is phenomenologically modelled by applying the LSD mechanism to this situation. In this LSD mechanism, the liquid sublayer thickness, the incoming liquid velocity to the liquid sublayer, and the axial distance from the onset of annular flow to the CHF location are used as the phenomena-controlling parameters. From the model validation on the 1406 CHF data points ranging over P = 0.1 - 2 MPa, G = 4 - 499 kg/m 2 s, L/D = 4 - 402, most of CHF data (more than 1000 points) are predicted within ±30% error bounds by the LSD mechanism. However, some calculation results that critical qualities are less than 0.4 are considerably overestimated by this mechanism. These overpredictions seem to be caused by inadequate CHF mechanism classification criteria and an insufficient consideration of the flow instability effect on CHF. Further studies for a new classification criterion screening the CHF data affected by flow instabilities and a new bubble detachment model for LPLV conditions are needed to improve the model accuracy. (author)
Predictive models of moth development

Science.gov (United States)

Degree-day models link ambient temperature to insect life-stages, making such models valuable tools in integrated pest management. These models increase management efficacy by predicting pest phenology. In Wisconsin, the top insect pest of cranberry production is the cranberry fruitworm, Acrobasis v...
Mechanism-Based Modeling of Gastric Emptying Rate and Gallbladder Emptying in Response to Caloric Intake

DEFF Research Database (Denmark)

Guiastrennec, B; Sonne, David Peick; Hansen, M

2016-01-01

Bile acids released postprandially modify the rate and extent of absorption of lipophilic compounds. The present study aimed to predict gastric emptying (GE) rate and gallbladder emptying (GBE) patterns in response to caloric intake. A mechanism-based model for GE, cholecystokinin plasma concentr......Bile acids released postprandially modify the rate and extent of absorption of lipophilic compounds. The present study aimed to predict gastric emptying (GE) rate and gallbladder emptying (GBE) patterns in response to caloric intake. A mechanism-based model for GE, cholecystokinin plasma...... concentrations, and GBE was developed on data from 33 patients with type 2 diabetes and 33 matched nondiabetic individuals who were administered various test drinks. A feedback action of the caloric content entering the proximal small intestine was identified for the rate of GE. The cholecystokinin...
Model Prediction Control For Water Management Using Adaptive Prediction Accuracy

NARCIS (Netherlands)

Tian, X.; Negenborn, R.R.; Van Overloop, P.J.A.T.M.; Mostert, E.

2014-01-01

In the field of operational water management, Model Predictive Control (MPC) has gained popularity owing to its versatility and flexibility. The MPC controller, which takes predictions, time delay and uncertainties into account, can be designed for multi-objective management problems and for
External validation of structure-biodegradation relationship (SBR) models for predicting the biodegradability of xenobiotics.

Science.gov (United States)

Devillers, J; Pandard, P; Richard, B

2013-01-01

Biodegradation is an important mechanism for eliminating xenobiotics by biotransforming them into simple organic and inorganic products. Faced with the ever growing number of chemicals available on the market, structure-biodegradation relationship (SBR) and quantitative structure-biodegradation relationship (QSBR) models are increasingly used as surrogates of the biodegradation tests. Such models have great potential for a quick and cheap estimation of the biodegradation potential of chemicals. The Estimation Programs Interface (EPI) Suite™ includes different models for predicting the potential aerobic biodegradability of organic substances. They are based on different endpoints, methodologies and/or statistical approaches. Among them, Biowin 5 and 6 appeared the most robust, being derived from the largest biodegradation database with results obtained only from the Ministry of International Trade and Industry (MITI) test. The aim of this study was to assess the predictive performances of these two models from a set of 356 chemicals extracted from notification dossiers including compatible biodegradation data. Another set of molecules with no more than four carbon atoms and substituted by various heteroatoms and/or functional groups was also embodied in the validation exercise. Comparisons were made with the predictions obtained with START (Structural Alerts for Reactivity in Toxtree). Biowin 5 and Biowin 6 gave satisfactorily prediction results except for the prediction of readily degradable chemicals. A consensus model built with Biowin 1 allowed the diminution of this tendency.
Shell Tectonics: A Mechanical Model for Strike-slip Displacement on Europa

Science.gov (United States)

Rhoden, Alyssa Rose; Wurman, Gilead; Huff, Eric M.; Manga, Michael; Hurford, Terry A.

2012-01-01

We introduce a new mechanical model for producing tidally-driven strike-slip displacement along preexisting faults on Europa, which we call shell tectonics. This model differs from previous models of strike-slip on icy satellites by incorporating a Coulomb failure criterion, approximating a viscoelastic rheology, determining the slip direction based on the gradient of the tidal shear stress rather than its sign, and quantitatively determining the net offset over many orbits. This model allows us to predict the direction of net displacement along faults and determine relative accumulation rate of displacement. To test the shell tectonics model, we generate global predictions of slip direction and compare them with the observed global pattern of strike-slip displacement on Europa in which left-lateral faults dominate far north of the equator, right-lateral faults dominate in the far south, and near-equatorial regions display a mixture of both types of faults. The shell tectonics model reproduces this global pattern. Incorporating a small obliquity into calculations of tidal stresses, which are used as inputs to the shell tectonics model, can also explain regional differences in strike-slip fault populations. We also discuss implications for fault azimuths, fault depth, and Europa's tectonic history.

Modeling mechanical inhomogeneities in small populations of proliferating monolayers and spheroids.

Science.gov (United States)

Lejeune, Emma; Linder, Christian

2018-06-01

Understanding the mechanical behavior of multicellular monolayers and spheroids is fundamental to tissue culture, organism development, and the early stages of tumor growth. Proliferating cells in monolayers and spheroids experience mechanical forces as they grow and divide and local inhomogeneities in the mechanical microenvironment can cause individual cells within the multicellular system to grow and divide at different rates. This differential growth, combined with cell division and reorganization, leads to residual stress. Multiple different modeling approaches have been taken to understand and predict the residual stresses that arise in growing multicellular systems, particularly tumor spheroids. Here, we show that by using a mechanically robust agent-based model constructed with the peridynamic framework, we gain a better understanding of residual stresses in multicellular systems as they grow from a single cell. In particular, we focus on small populations of cells (1-100 s) where population behavior is highly stochastic and prior investigation has been limited. We compare the average strain energy density of cells in monolayers and spheroids using different growth and division rules and find that, on average, cells in spheroids have a higher strain energy density than cells in monolayers. We also find that cells in the interior of a growing spheroid are, on average, in compression. Finally, we demonstrate the importance of accounting for stochastic fluctuations in the mechanical environment, particularly when the cellular response to mechanical cues is nonlinear. The results presented here serve as a starting point for both further investigation with agent-based models, and for the incorporation of major findings from agent-based models into continuum scale models when explicit representation of individual cells is not computationally feasible.
Modelling of the photooxidation of toluene: conceptual ideas for validating detailed mechanisms

Directory of Open Access Journals (Sweden)

V. Wagner

2003-01-01

Full Text Available Toluene photooxidation is chosen as an example to examine how simulations of smog-chamber experiments can be used to unravel shortcomings in detailed mechanisms and to provide information on complex reaction systems that will be crucial for the design of future validation experiments. The mechanism used in this study is extracted from the Master Chemical Mechanism Version 3 (MCM v3 and has been updated with new modules for cresol and g-dicarbonyl chemistry. Model simulations are carried out for a toluene-NOx experiment undertaken at the European Photoreactor (EUPHORE. The comparison of the simulation with the experimental data reveals two fundamental shortcomings in the mechanism: OH production is too low by about 80%, and the ozone concentration at the end of the experiment is over-predicted by 55%. The radical budget was analysed to identify the key intermediates governing the radical transformation in the toluene system. Ring-opening products, particularly conjugated g-dicarbonyls, were identified as dominant radical sources in the early stages of the experiment. The analysis of the time evolution of radical production points to a missing OH source that peaks when the system reaches highest reactivity. First generation products are also of major importance for the ozone production in the system. The analysis of the radical budget suggests two options to explain the concurrent under-prediction of OH and over-prediction of ozone in the model: 1 missing oxidation processes that produce or regenerate OH without or with little NO to NO2 conversion or 2 NO3 chemistry that sequesters reactive nitrogen oxides into stable nitrogen compounds and at the same time produces peroxy radicals. Sensitivity analysis was employed to identify significant contributors to ozone production and it is shown how this technique, in combination with ozone isopleth plots, can be used for the design of validation experiments.
Predicting water main failures using Bayesian model averaging and survival modelling approach

International Nuclear Information System (INIS)

Kabir, Golam; Tesfamariam, Solomon; Sadiq, Rehan

2015-01-01

To develop an effective preventive or proactive repair and replacement action plan, water utilities often rely on water main failure prediction models. However, in predicting the failure of water mains, uncertainty is inherent regardless of the quality and quantity of data used in the model. To improve the understanding of water main failure, a Bayesian framework is developed for predicting the failure of water mains considering uncertainties. In this study, Bayesian model averaging method (BMA) is presented to identify the influential pipe-dependent and time-dependent covariates considering model uncertainties whereas Bayesian Weibull Proportional Hazard Model (BWPHM) is applied to develop the survival curves and to predict the failure rates of water mains. To accredit the proposed framework, it is implemented to predict the failure of cast iron (CI) and ductile iron (DI) pipes of the water distribution network of the City of Calgary, Alberta, Canada. Results indicate that the predicted 95% uncertainty bounds of the proposed BWPHMs capture effectively the observed breaks for both CI and DI water mains. Moreover, the performance of the proposed BWPHMs are better compare to the Cox-Proportional Hazard Model (Cox-PHM) for considering Weibull distribution for the baseline hazard function and model uncertainties. - Highlights: • Prioritize rehabilitation and replacements (R/R) strategies of water mains. • Consider the uncertainties for the failure prediction. • Improve the prediction capability of the water mains failure models. • Identify the influential and appropriate covariates for different models. • Determine the effects of the covariates on failure
Mechanical characterization and modeling for anodes and cathodes in lithium-ion batteries

Science.gov (United States)

Wang, Lubing; Yin, Sha; Zhang, Chao; Huan, Yong; Xu, Jun

2018-07-01

Mechanical properties of electrode materials have significant influence over electrochemical properties as well as mechanical integrity of lithium-ion battery cells. Here, anode and cathode in a commercially available 18650 NCA (Nickel Cobalt Aluminum Oxide)/graphite cell were comprehensively studied by tensile tests considering material anisotropy, SOC (state of charge), strain rate and electrolyte content. Results showed that the mechanical properties of both electrodes were highly dependent on strain rate and electrolyte content; however, anode was SOC dependent while cathode was not. Besides, coupled effects of strain rate and SOC of anodes were also discussed. SEM (scanning electron microscope) images of surfaces and cross-sections of electrodes showed the fracture morphology. In addition, mechanical behavior of Cu foil separated from anode with different SOC values were studied and compared. Finally, constitutive models of electrodes considering both strain rate and anisotropy effects were established. This study reveals the relationship between electrochemical dependent mechanical behavior of the electrodes. The established mechanical models of electrodes can be applied to the numerical computation of battery cells. Results are essential to predict the mechanical responses as well as the deformation of battery cell under various loading conditions, facilitating safer battery design and manufacturing.
Data for prediction of mechanical properties of aspen flakeboards

Science.gov (United States)

C. G. Carll; P. Wang

1983-01-01

This research compared two methods of producing flakeboards with uniform density distribution (which could then be used to predict bending properties of flakeboards with density gradients). One of the methods was suspected of producing weak boards because it involved exertion of high pressures on cold mats. Although differences were found in mechanical properties of...
Testing the predictive power of nuclear mass models

International Nuclear Information System (INIS)

Mendoza-Temis, J.; Morales, I.; Barea, J.; Frank, A.; Hirsch, J.G.; Vieyra, J.C. Lopez; Van Isacker, P.; Velazquez, V.

2008-01-01

A number of tests are introduced which probe the ability of nuclear mass models to extrapolate. Three models are analyzed in detail: the liquid drop model, the liquid drop model plus empirical shell corrections and the Duflo-Zuker mass formula. If predicted nuclei are close to the fitted ones, average errors in predicted and fitted masses are similar. However, the challenge of predicting nuclear masses in a region stabilized by shell effects (e.g., the lead region) is far more difficult. The Duflo-Zuker mass formula emerges as a powerful predictive tool
Integrative modelling reveals mechanisms linking productivity and plant species richness.

Science.gov (United States)

Grace, James B; Anderson, T Michael; Seabloom, Eric W; Borer, Elizabeth T; Adler, Peter B; Harpole, W Stanley; Hautier, Yann; Hillebrand, Helmut; Lind, Eric M; Pärtel, Meelis; Bakker, Jonathan D; Buckley, Yvonne M; Crawley, Michael J; Damschen, Ellen I; Davies, Kendi F; Fay, Philip A; Firn, Jennifer; Gruner, Daniel S; Hector, Andy; Knops, Johannes M H; MacDougall, Andrew S; Melbourne, Brett A; Morgan, John W; Orrock, John L; Prober, Suzanne M; Smith, Melinda D

2016-01-21

How ecosystem productivity and species richness are interrelated is one of the most debated subjects in the history of ecology. Decades of intensive study have yet to discern the actual mechanisms behind observed global patterns. Here, by integrating the predictions from multiple theories into a single model and using data from 1,126 grassland plots spanning five continents, we detect the clear signals of numerous underlying mechanisms linking productivity and richness. We find that an integrative model has substantially higher explanatory power than traditional bivariate analyses. In addition, the specific results unveil several surprising findings that conflict with classical models. These include the isolation of a strong and consistent enhancement of productivity by richness, an effect in striking contrast with superficial data patterns. Also revealed is a consistent importance of competition across the full range of productivity values, in direct conflict with some (but not all) proposed models. The promotion of local richness by macroecological gradients in climatic favourability, generally seen as a competing hypothesis, is also found to be important in our analysis. The results demonstrate that an integrative modelling approach leads to a major advance in our ability to discern the underlying processes operating in ecological systems.
Comparison of Prediction-Error-Modelling Criteria

DEFF Research Database (Denmark)

Jørgensen, John Bagterp; Jørgensen, Sten Bay

2007-01-01

Single and multi-step prediction-error-methods based on the maximum likelihood and least squares criteria are compared. The prediction-error methods studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model, which is a r...
Prediction Model and Principle of End-of-Life Threshold for Lithium Ion Batteries Based on Open Circuit Voltage Drifts

International Nuclear Information System (INIS)

Cui, Yingzhi; Yang, Jie; Du, Chunyu; Zuo, Pengjian; Gao, Yunzhi; Cheng, Xinqun; Ma, Yulin; Yin, Geping

2017-01-01

Highlights: •Open circuit voltage evolution over ageing of lithium ion batteries is deciphered. •The mechanism responsible for the end-of-life (EOL) threshold is elaborated. •A new prediction model of EOL threshold with improved accuracy is developed. •This EOL prediction model is promising for the applications in electric vehicles. -- Abstract: The end-of-life (EOL) of a lithium ion battery (LIB) is defined as the time point when the LIB can no longer provide sufficient power or energy to accomplish its intended function. Generally, the EOL occurs abruptly when the degradation of a LIB reaches the threshold. Therefore, current prediction methods of EOL by extrapolating the early degradation behavior often result in significant errors. To address this problem, this paper analyzes the reason for the EOL threshold of a LIB with shallow depth of discharge. It is found that the sudden appearance of EOL threshold results from the drift of open circuit voltage (OCV) at the end of both shallow depth and full discharges. Further, a new EOL threshold prediction model with highly improved accuracy is developed based on the OCV drifts and their evolution mechanism, which can effectively avoid the misjudgment of EOL threshold. The accuracy of this EOL threshold prediction model is verified by comparing with experimental results. The EOL threshold prediction model can be applied to other battery chemistry systems and its possible application in electric vehicles is finally discussed.
Foundation Settlement Prediction Based on a Novel NGM Model

Directory of Open Access Journals (Sweden)

Peng-Yu Chen

2014-01-01

Full Text Available Prediction of foundation or subgrade settlement is very important during engineering construction. According to the fact that there are lots of settlement-time sequences with a nonhomogeneous index trend, a novel grey forecasting model called NGM (1,1,k,c model is proposed in this paper. With an optimized whitenization differential equation, the proposed NGM (1,1,k,c model has the property of white exponential law coincidence and can predict a pure nonhomogeneous index sequence precisely. We used two case studies to verify the predictive effect of NGM (1,1,k,c model for settlement prediction. The results show that this model can achieve excellent prediction accuracy; thus, the model is quite suitable for simulation and prediction of approximate nonhomogeneous index sequence and has excellent application value in settlement prediction.
Electrostatic ion thrusters - towards predictive modeling

Energy Technology Data Exchange (ETDEWEB)

Kalentev, O.; Matyash, K.; Duras, J.; Lueskow, K.F.; Schneider, R. [Ernst-Moritz-Arndt Universitaet Greifswald, D-17489 (Germany); Koch, N. [Technische Hochschule Nuernberg Georg Simon Ohm, Kesslerplatz 12, D-90489 Nuernberg (Germany); Schirra, M. [Thales Electronic Systems GmbH, Soeflinger Strasse 100, D-89077 Ulm (Germany)

2014-02-15

The development of electrostatic ion thrusters so far has mainly been based on empirical and qualitative know-how, and on evolutionary iteration steps. This resulted in considerable effort regarding prototype design, construction and testing and therefore in significant development and qualification costs and high time demands. For future developments it is anticipated to implement simulation tools which allow for quantitative prediction of ion thruster performance, long-term behavior and space craft interaction prior to hardware design and construction. Based on integrated numerical models combining self-consistent kinetic plasma models with plasma-wall interaction modules a new quality in the description of electrostatic thrusters can be reached. These open the perspective for predictive modeling in this field. This paper reviews the application of a set of predictive numerical modeling tools on an ion thruster model of the HEMP-T (High Efficiency Multi-stage Plasma Thruster) type patented by Thales Electron Devices GmbH. (copyright 2014 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)
Studies on sulfate attack: Mechanisms, test methods, and modeling

Science.gov (United States)

Santhanam, Manu

concentration of the solution were seen to change the rate and mechanism of the attack in both sodium and magnesium sulfate solutions. The test results from these experiments were used to generate models for prediction of physical properties such as expansion and mass change, which could be used either for service life predictions, or for designing more reliable laboratory tests. Lastly, mechanisms for the attack by sodium and magnesium sulfate were proposed, based on the observations in the various studies. These mechanisms were able to simplify the understanding of the sulfate attack phenomenon.
Predictive validation of an influenza spread model.

Directory of Open Access Journals (Sweden)

Ayaz Hyder

Full Text Available BACKGROUND: Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. METHODS AND FINDINGS: We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998-1999. Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type. Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. CONCLUSIONS: Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve
Predictive Validation of an Influenza Spread Model

Science.gov (United States)

Hyder, Ayaz; Buckeridge, David L.; Leung, Brian

2013-01-01

Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive
Identification and design of novel polymer-based mechanical transducers: A nano-structural model for thin film indentation

Energy Technology Data Exchange (ETDEWEB)

Villanueva, Joshua; Huang, Qian; Sirbuly, Donald J., E-mail: dsirbuly@ucsd.edu [Department of NanoEngineering, University of California San Diego, La Jolla, California 92093 (United States)

2014-09-14

Mechanical characterization is important for understanding small-scale systems and developing devices, particularly at the interface of biology, medicine, and nanotechnology. Yet, monitoring sub-surface forces is challenging with current technologies like atomic force microscopes (AFMs) or optical tweezers due to their probe sizes and sophisticated feedback mechanisms. An alternative transducer design relying on the indentation mechanics of a compressible thin polymer would be an ideal system for more compact and versatile probes, facilitating measurements in situ or in vivo. However, application-specific tuning of a polymer's mechanical properties can be burdensome via experimental optimization. Therefore, efficient transducer design requires a fundamental understanding of how synthetic parameters such as the molecular weight and grafting density influence the bulk material properties that determine the force response. In this work, we apply molecular-level polymer scaling laws to a first order elastic foundation model, relating the conformational state of individual polymer chains to the macroscopic compression of thin film systems. A parameter sweep analysis was conducted to observe predicted model trends under various system conditions and to understand how nano-structural elements influence the material stiffness. We validate the model by comparing predicted force profiles to experimental AFM curves for a real polymer system and show that it has reasonable predictive power for initial estimates of the force response, displaying excellent agreement with experimental force curves. We also present an analysis of the force sensitivity of an example transducer system to demonstrate identification of synthetic protocols based on desired mechanical properties. These results highlight the usefulness of this simple model as an aid for the design of a new class of compact and tunable nanomechanical force transducers.
Identification and design of novel polymer-based mechanical transducers: A nano-structural model for thin film indentation

International Nuclear Information System (INIS)

Villanueva, Joshua; Huang, Qian; Sirbuly, Donald J.

2014-01-01

Mechanical characterization is important for understanding small-scale systems and developing devices, particularly at the interface of biology, medicine, and nanotechnology. Yet, monitoring sub-surface forces is challenging with current technologies like atomic force microscopes (AFMs) or optical tweezers due to their probe sizes and sophisticated feedback mechanisms. An alternative transducer design relying on the indentation mechanics of a compressible thin polymer would be an ideal system for more compact and versatile probes, facilitating measurements in situ or in vivo. However, application-specific tuning of a polymer's mechanical properties can be burdensome via experimental optimization. Therefore, efficient transducer design requires a fundamental understanding of how synthetic parameters such as the molecular weight and grafting density influence the bulk material properties that determine the force response. In this work, we apply molecular-level polymer scaling laws to a first order elastic foundation model, relating the conformational state of individual polymer chains to the macroscopic compression of thin film systems. A parameter sweep analysis was conducted to observe predicted model trends under various system conditions and to understand how nano-structural elements influence the material stiffness. We validate the model by comparing predicted force profiles to experimental AFM curves for a real polymer system and show that it has reasonable predictive power for initial estimates of the force response, displaying excellent agreement with experimental force curves. We also present an analysis of the force sensitivity of an example transducer system to demonstrate identification of synthetic protocols based on desired mechanical properties. These results highlight the usefulness of this simple model as an aid for the design of a new class of compact and tunable nanomechanical force transducers.
Integrating geophysics and hydrology for reducing the uncertainty of groundwater model predictions and improved prediction performance

DEFF Research Database (Denmark)

Christensen, Nikolaj Kruse; Christensen, Steen; Ferre, Ty

the integration of geophysical data in the construction of a groundwater model increases the prediction performance. We suggest that modelers should perform a hydrogeophysical “test-bench” analysis of the likely value of geophysics data for improving groundwater model prediction performance before actually...... and the resulting predictions can be compared with predictions from the ‘true’ model. By performing this analysis we expect to give the modeler insight into how the uncertainty of model-based prediction can be reduced.......A major purpose of groundwater modeling is to help decision-makers in efforts to manage the natural environment. Increasingly, it is recognized that both the predictions of interest and their associated uncertainties should be quantified to support robust decision making. In particular, decision...
Predictive Surface Complexation Modeling

Energy Technology Data Exchange (ETDEWEB)

Sverjensky, Dimitri A. [Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Earth and Planetary Sciences

2016-11-29

Surface complexation plays an important role in the equilibria and kinetics of processes controlling the compositions of soilwaters and groundwaters, the fate of contaminants in groundwaters, and the subsurface storage of CO₂ and nuclear waste. Over the last several decades, many dozens of individual experimental studies have addressed aspects of surface complexation that have contributed to an increased understanding of its role in natural systems. However, there has been no previous attempt to develop a model of surface complexation that can be used to link all the experimental studies in order to place them on a predictive basis. Overall, my research has successfully integrated the results of the work of many experimentalists published over several decades. For the first time in studies of the geochemistry of the mineral-water interface, a practical predictive capability for modeling has become available. The predictive correlations developed in my research now enable extrapolations of experimental studies to provide estimates of surface chemistry for systems not yet studied experimentally and for natural and anthropogenically perturbed systems.
NOx PREDICTION FOR FBC BOILERS USING EMPIRICAL MODELS

Directory of Open Access Journals (Sweden)

Jiří Štefanica

2014-02-01

Full Text Available Reliable prediction of NOx emissions can provide useful information for boiler design and fuel selection. Recently used kinetic prediction models for FBC boilers are overly complex and require large computing capacity. Even so, there are many uncertainties in the case of FBC boilers. An empirical modeling approach for NOx prediction has been used exclusively for PCC boilers. No reference is available for modifying this method for FBC conditions. This paper presents possible advantages of empirical modeling based prediction of NOx emissions for FBC boilers, together with a discussion of its limitations. Empirical models are reviewed, and are applied to operation data from FBC boilers used for combusting Czech lignite coal or coal-biomass mixtures. Modifications to the model are proposed in accordance with theoretical knowledge and prediction accuracy.
Novel patch modelling method for efficient simulation and prediction uncertainty analysis of multi-scale groundwater flow and transport processes

Science.gov (United States)

Sreekanth, J.; Moore, Catherine

2018-04-01

The application of global sensitivity and uncertainty analysis techniques to groundwater models of deep sedimentary basins are typically challenged by large computational burdens combined with associated numerical stability issues. The highly parameterized approaches required for exploring the predictive uncertainty associated with the heterogeneous hydraulic characteristics of multiple aquifers and aquitards in these sedimentary basins exacerbate these issues. A novel Patch Modelling Methodology is proposed for improving the computational feasibility of stochastic modelling analysis of large-scale and complex groundwater models. The method incorporates a nested groundwater modelling framework that enables efficient simulation of groundwater flow and transport across multiple spatial and temporal scales. The method also allows different processes to be simulated within different model scales. Existing nested model methodologies are extended by employing 'joining predictions' for extrapolating prediction-salient information from one model scale to the next. This establishes a feedback mechanism supporting the transfer of information from child models to parent models as well as parent models to child models in a computationally efficient manner. This feedback mechanism is simple and flexible and ensures that while the salient small scale features influencing larger scale prediction are transferred back to the larger scale, this does not require the live coupling of models. This method allows the modelling of multiple groundwater flow and transport processes using separate groundwater models that are built for the appropriate spatial and temporal scales, within a stochastic framework, while also removing the computational burden associated with live model coupling. The utility of the method is demonstrated by application to an actual large scale aquifer injection scheme in Australia.

Prediction of pipeline corrosion rate based on grey Markov models

International Nuclear Information System (INIS)

Chen Yonghong; Zhang Dafa; Peng Guichu; Wang Yuemin

2009-01-01

Based on the model that combined by grey model and Markov model, the prediction of corrosion rate of nuclear power pipeline was studied. Works were done to improve the grey model, and the optimization unbiased grey model was obtained. This new model was used to predict the tendency of corrosion rate, and the Markov model was used to predict the residual errors. In order to improve the prediction precision, rolling operation method was used in these prediction processes. The results indicate that the improvement to the grey model is effective and the prediction precision of the new model combined by the optimization unbiased grey model and Markov model is better, and the use of rolling operation method may improve the prediction precision further. (authors)
Sweat loss prediction using a multi-model approach.

Science.gov (United States)

Xu, Xiaojiang; Santee, William R

2011-07-01

A new multi-model approach (MMA) for sweat loss prediction is proposed to improve prediction accuracy. MMA was computed as the average of sweat loss predicted by two existing thermoregulation models: i.e., the rational model SCENARIO and the empirical model Heat Strain Decision Aid (HSDA). Three independent physiological datasets, a total of 44 trials, were used to compare predictions by MMA, SCENARIO, and HSDA. The observed sweat losses were collected under different combinations of uniform ensembles, environmental conditions (15-40°C, RH 25-75%), and exercise intensities (250-600 W). Root mean square deviation (RMSD), residual plots, and paired t tests were used to compare predictions with observations. Overall, MMA reduced RMSD by 30-39% in comparison with either SCENARIO or HSDA, and increased the prediction accuracy to 66% from 34% or 55%. Of the MMA predictions, 70% fell within the range of mean observed value ± SD, while only 43% of SCENARIO and 50% of HSDA predictions fell within the same range. Paired t tests showed that differences between observations and MMA predictions were not significant, but differences between observations and SCENARIO or HSDA predictions were significantly different for two datasets. Thus, MMA predicted sweat loss more accurately than either of the two single models for the three datasets used. Future work will be to evaluate MMA using additional physiological data to expand the scope of populations and conditions.
Appplication of a general fluid mechanics program to NTP system modeling

International Nuclear Information System (INIS)

Lee, S.K.

1993-01-01

An effort is currently underway at NASA and the Department of Energy (DOE) to develop an accurate model for predicting nuclear thermal propulsion (NTP) system performance. The objective of the effort is to develop several levels of computer programs which vary in detail and complexity according to user's needs. The current focus is on the Level 1 steady-state, parametric system model. This system model will combine a general fluid mechanics program, SAFSIM, with the ability to analyze turbines, pumps, nozzles, and reactor physics. SAFSIM (System Analysis Flow SIMulator) is a FORTRAN computer program that simulates integrated performance of systems involving fluid mechanics, heat transfer, and reactor dynamics. SAFSIM has the versatility to allow simulation of almost any system, including a nuclear reactor system. The focus of this paper is the validation of SAFSIM's capabilities as a base computational engine for a nuclear thermal propulsion system model. Validation is being accomplished by modeling of a nuclear engine test using SAFSIM and comparing the results to known experimental data
Research on prediction of agricultural machinery total power based on grey model optimized by genetic algorithm

Science.gov (United States)

Xie, Yan; Li, Mu; Zhou, Jin; Zheng, Chang-zheng

2009-07-01

Agricultural machinery total power is an important index to reflex and evaluate the level of agricultural mechanization. It is the power source of agricultural production, and is the main factors to enhance the comprehensive agricultural production capacity expand production scale and increase the income of the farmers. Its demand is affected by natural, economic, technological and social and other "grey" factors. Therefore, grey system theory can be used to analyze the development of agricultural machinery total power. A method based on genetic algorithm optimizing grey modeling process is introduced in this paper. This method makes full use of the advantages of the grey prediction model and characteristics of genetic algorithm to find global optimization. So the prediction model is more accurate. According to data from a province, the GM (1, 1) model for predicting agricultural machinery total power was given based on the grey system theories and genetic algorithm. The result indicates that the model can be used as agricultural machinery total power an effective tool for prediction.
Mechanical characterization and modelling of the heavy tungsten allow IT180

CERN Document Server

Scapin, M

2015-01-01

In this work, the mechanical characterization and the consequent material modeling of the tungsten alloy INERMET® IT180 were performed. The material is actually used in the collimation system of the Large Hadron Collider at CERN and several studies are currently under development in order to be able to numerically predict the material damage in case of energy beamimpact, but to do this, a confident strength model has to be obtained. This is the basis of this work, in which a test campaign in compression and tension at different strain-rates and tempe...
Reduced combustion mechanism for C1-C4 hydrocarbons and its application in computational fluid dynamics flare modeling.

Science.gov (United States)

Damodara, Vijaya; Chen, Daniel H; Lou, Helen H; Rasel, Kader M A; Richmond, Peyton; Wang, Anan; Li, Xianchang

2017-05-01

Emissions from flares constitute unburned hydrocarbons, carbon monoxide (CO), soot, and other partially burned and altered hydrocarbons along with carbon dioxide (CO 2 ) and water. Soot or visible smoke is of particular concern for flare operators/regulatory agencies. The goal of the study is to develop a computational fluid dynamics (CFD) model capable of predicting flare combustion efficiency (CE) and soot emission. Since detailed combustion mechanisms are too complicated for (CFD) application, a 50-species reduced mechanism, LU 3.0.1, was developed. LU 3.0.1 is capable of handling C 4 hydrocarbons and soot precursor species (C 2 H 2 , C 2 H 4 , C 6 H 6 ). The new reduced mechanism LU 3.0.1 was first validated against experimental performance indicators: laminar flame speed, adiabatic flame temperature, and ignition delay. Further, CFD simulations using LU 3.0.1 were run to predict soot emission and CE of air-assisted flare tests conducted in 2010 in Tulsa, Oklahoma, using ANSYS Fluent software. Results of non-premixed probability density function (PDF) model and eddy dissipation concept (EDC) model are discussed. It is also noteworthy that when used in conjunction with the EDC turbulence-chemistry model, LU 3.0.1 can reasonably predict volatile organic compound (VOC) emissions as well. A reduced combustion mechanism containing 50 C 1 -C 4 species and soot precursors has been developed and validated against experimental data. The combustion mechanism is then employed in the computational fluid dynamics (CFD) of modeling of soot emission and combustion efficiency (CE) of controlled flares for which experimental soot and CE data are available. The validated CFD modeling tools are useful for oil, gas, and chemical industries to comply with U.S. Environmental Protection Agency's (EPA) mandate to achieve smokeless flaring with a high CE.
Numerical investigation of ethanol fuelled HCCI engine using stochastic reactor model. Part 1: Development of a new reduced ethanol oxidation mechanism

International Nuclear Information System (INIS)

Maurya, Rakesh Kumar; Akhil, Nekkanti

2016-01-01

Highlights: • Stochastic reactor model used for numerical study of HCCI engine. • New reduced oxidation mechanism with NOx developed (47 species and 272 reactions). • Mechanism predicts cylinder pressure and heat release with sufficient accuracy. • Mechanism was able to capture the trend in NO x emission with sufficient accuracy. - Abstract: Ethanol is considered a potential biofuel for internal combustion engines. In this study, homogeneous charge compression ignition (HCCI) simulations of ethanol engine experiments were performed using stochastic reactor model (SRM). Detailed ethanol oxidation mechanism is developed by including NO x reaction in existing detailed oxidation mechanism with 57 species and 383 reactions. Detailed ethanol mechanism with NO x used in this study contains 76 species and 495 reactions. This mechanism was reduced by direct relation graph (DRG) method, which was validated with the experimental results. Existing Lu’s 40-species skeletal mechanism with NO formation were also compared with detailed and reduced mechanisms for predicting maximum cylinder pressure, maximum heat release rate and crank angle position of maximum cylinder pressure in HCCI engine. Reduced mechanism developed in this study exhibited the best resemblance with the experimental data. This reduced mechanism was also validated by measured engine cylinder pressure curves and measured ignition delays in constant volume reactors. The results showed that reduced mechanism is capable of predicting HCCI engine performance parameters with sufficient accuracy. Sensitivity analysis was conducted to determine the influential reactions in ethanol oxidation. Results also show that detailed and reduced mechanism was able to predict NO x emission in good agreement with the corresponding experimental data.
Finding Furfural Hydrogenation Catalysts via Predictive Modelling

Science.gov (United States)

Strassberger, Zea; Mooijman, Maurice; Ruijter, Eelco; Alberts, Albert H; Maldonado, Ana G; Orru, Romano V A; Rothenberg, Gadi

2010-01-01

Abstract We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre throughout the reaction. Deuterium-labelling studies showed a secondary isotope effect (kH:kD=1.5). Further mechanistic studies showed that this transfer hydrogenation follows the so-called monohydride pathway. Using these data, we built a predictive model for 13 of the catalysts, based on 2D and 3D molecular descriptors. We tested and validated the model using the remaining five catalysts (cross-validation, R2=0.913). Then, with this model, the conversion and selectivity were predicted for four completely new ruthenium-carbene complexes. These four catalysts were then synthesized and tested. The results were within 3% of the model’s predictions, demonstrating the validity and value of predictive modelling in catalyst optimization. PMID:23193388
Alcator C-Mod predictive modeling

International Nuclear Information System (INIS)

Pankin, Alexei; Bateman, Glenn; Kritz, Arnold; Greenwald, Martin; Snipes, Joseph; Fredian, Thomas

2001-01-01

Predictive simulations for the Alcator C-mod tokamak [I. Hutchinson et al., Phys. Plasmas 1, 1511 (1994)] are carried out using the BALDUR integrated modeling code [C. E. Singer et al., Comput. Phys. Commun. 49, 275 (1988)]. The results are obtained for temperature and density profiles using the Multi-Mode transport model [G. Bateman et al., Phys. Plasmas 5, 1793 (1998)] as well as the mixed-Bohm/gyro-Bohm transport model [M. Erba et al., Plasma Phys. Controlled Fusion 39, 261 (1997)]. The simulated discharges are characterized by very high plasma density in both low and high modes of confinement. The predicted profiles for each of the transport models match the experimental data about equally well in spite of the fact that the two models have different dimensionless scalings. Average relative rms deviations are less than 8% for the electron density profiles and 16% for the electron and ion temperature profiles
Clinical Predictive Modeling Development and Deployment through FHIR Web Services.

Science.gov (United States)

Khalilia, Mohammed; Choi, Myung; Henderson, Amelia; Iyengar, Sneha; Braunstein, Mark; Sun, Jimeng

2015-01-01

Clinical predictive modeling involves two challenging tasks: model development and model deployment. In this paper we demonstrate a software architecture for developing and deploying clinical predictive models using web services via the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The services enable model development using electronic health records (EHRs) stored in OMOP CDM databases and model deployment for scoring individual patients through FHIR resources. The MIMIC2 ICU dataset and a synthetic outpatient dataset were transformed into OMOP CDM databases for predictive model development. The resulting predictive models are deployed as FHIR resources, which receive requests of patient information, perform prediction against the deployed predictive model and respond with prediction scores. To assess the practicality of this approach we evaluated the response and prediction time of the FHIR modeling web services. We found the system to be reasonably fast with one second total response time per patient prediction.
Modeling mechanical effects on promotion and retardation of martensitic transformation

Energy Technology Data Exchange (ETDEWEB)

Maalekian, Mehran, E-mail: mehran.maalekian@ubc.ca [Department of Materials Engineering, University of British Columbia, 309-6350 Stores Road, Vancouver, B.C. V61Z4 (Canada); Kozeschnik, Ernst [Christian Doppler Laboratory for ' Early Stages of Precipitation' , Institute of Materials Science and Technology, Vienna University of Technology (Austria)

2011-01-25

Research highlights: {yields} Compressive elastic stresses up to 250 MPa are applied in continuous cooling. {yields} Using the thermodynamic data and maximum value of the mechanical driving force the predicted increase in M{sub s} ({approx}0.1 K/MPa) is in agreement with experiment {yields} Austenite was deformed plastically at different temperatures (800 deg. C-1100 deg. C). {yields} High deformation temperature (i.e. 1100 deg. C) as well as low plastic strain (i.e. {epsilon}{sub ave} {approx} 30%) do not affect martensite transformation noticeably, whereas lower deformation temperature (e.g. 900 deg. C) and large plastic strain (i.e. {epsilon}{sub ave} {approx} 70%) retards martensite transformation. {yields} The theory of mechanical stabilization predicts the depression of M{sub s}. - Abstract: The influence of compressive stress and prior plastic deformation of austenite on the martensite transformation in a eutectoid steel is studied both experimentally and theoretically. It is demonstrated that martensite formation is assisted by stress but it is retarded when transformation occurs from deformed austenite. With the quantitative modeling of the problem based on the theory of displacive shear transformation, the explanation of the two opposite roles of mechanical treatment prior to or simultaneously to martensite transformation is presented.
Elements of a pragmatic approach for dealing with bias and uncertainty in experiments through predictions : experiment design and data conditioning; %22real space%22 model validation and conditioning; hierarchical modeling and extrapolative prediction.

Energy Technology Data Exchange (ETDEWEB)

Romero, Vicente Jose

2011-11-01

This report explores some important considerations in devising a practical and consistent framework and methodology for utilizing experiments and experimental data to support modeling and prediction. A pragmatic and versatile 'Real Space' approach is outlined for confronting experimental and modeling bias and uncertainty to mitigate risk in modeling and prediction. The elements of experiment design and data analysis, data conditioning, model conditioning, model validation, hierarchical modeling, and extrapolative prediction under uncertainty are examined. An appreciation can be gained for the constraints and difficulties at play in devising a viable end-to-end methodology. Rationale is given for the various choices underlying the Real Space end-to-end approach. The approach adopts and refines some elements and constructs from the literature and adds pivotal new elements and constructs. Crucially, the approach reflects a pragmatism and versatility derived from working many industrial-scale problems involving complex physics and constitutive models, steady-state and time-varying nonlinear behavior and boundary conditions, and various types of uncertainty in experiments and models. The framework benefits from a broad exposure to integrated experimental and modeling activities in the areas of heat transfer, solid and structural mechanics, irradiated electronics, and combustion in fluids and solids.
Predictive Modelling of Heavy Metals in Urban Lakes

OpenAIRE

Lindström, Martin

2000-01-01

Heavy metals are well-known environmental pollutants. In this thesis predictive models for heavy metals in urban lakes are discussed and new models presented. The base of predictive modelling is empirical data from field investigations of many ecosystems covering a wide range of ecosystem characteristics. Predictive models focus on the variabilities among lakes and processes controlling the major metal fluxes. Sediment and water data for this study were collected from ten small lakes in the ...
Stage-specific predictive models for breast cancer survivability.

Science.gov (United States)

Kate, Rohit J; Nadig, Ramya

2017-01-01

Survivability rates vary widely among various stages of breast cancer. Although machine learning models built in past to predict breast cancer survivability were given stage as one of the features, they were not trained or evaluated separately for each stage. To investigate whether there are differences in performance of machine learning models trained and evaluated across different stages for predicting breast cancer survivability. Using three different machine learning methods we built models to predict breast cancer survivability separately for each stage and compared them with the traditional joint models built for all the stages. We also evaluated the models separately for each stage and together for all the stages. Our results show that the most suitable model to predict survivability for a specific stage is the model trained for that particular stage. In our experiments, using additional examples of other stages during training did not help, in fact, it made it worse in some cases. The most important features for predicting survivability were also found to be different for different stages. By evaluating the models separately on different stages we found that the performance widely varied across them. We also demonstrate that evaluating predictive models for survivability on all the stages together, as was done in the past, is misleading because it overestimates performance. Copyright Â© 2016 Elsevier Ireland Ltd. All rights reserved.
Impact of modellers' decisions on hydrological a priori predictions

Science.gov (United States)

Holländer, H. M.; Bormann, H.; Blume, T.; Buytaert, W.; Chirico, G. B.; Exbrayat, J.-F.; Gustafsson, D.; Hölzel, H.; Krauße, T.; Kraft, P.; Stoll, S.; Blöschl, G.; Flühler, H.

2014-06-01

In practice, the catchment hydrologist is often confronted with the task of predicting discharge without having the needed records for calibration. Here, we report the discharge predictions of 10 modellers - using the model of their choice - for the man-made Chicken Creek catchment (6 ha, northeast Germany, Gerwin et al., 2009b) and we analyse how well they improved their prediction in three steps based on adding information prior to each following step. The modellers predicted the catchment's hydrological response in its initial phase without having access to the observed records. They used conceptually different physically based models and their modelling experience differed largely. Hence, they encountered two problems: (i) to simulate discharge for an ungauged catchment and (ii) using models that were developed for catchments, which are not in a state of landscape transformation. The prediction exercise was organized in three steps: (1) for the first prediction the modellers received a basic data set describing the catchment to a degree somewhat more complete than usually available for a priori predictions of ungauged catchments; they did not obtain information on stream flow, soil moisture, nor groundwater response and had therefore to guess the initial conditions; (2) before the second prediction they inspected the catchment on-site and discussed their first prediction attempt; (3) for their third prediction they were offered additional data by charging them pro forma with the costs for obtaining this additional information. Holländer et al. (2009) discussed the range of predictions obtained in step (1). Here, we detail the modeller's assumptions and decisions in accounting for the various processes. We document the prediction progress as well as the learning process resulting from the availability of added information. For the second and third steps, the progress in prediction quality is evaluated in relation to individual modelling experience and costs of
A multivariate model for predicting segmental body composition.

Science.gov (United States)

Tian, Simiao; Mioche, Laurence; Denis, Jean-Baptiste; Morio, Béatrice

2013-12-01

The aims of the present study were to propose a multivariate model for predicting simultaneously body, trunk and appendicular fat and lean masses from easily measured variables and to compare its predictive capacity with that of the available univariate models that predict body fat percentage (BF%). The dual-energy X-ray absorptiometry (DXA) dataset (52% men and 48% women) with White, Black and Hispanic ethnicities (1999-2004, National Health and Nutrition Examination Survey) was randomly divided into three sub-datasets: a training dataset (TRD), a test dataset (TED); a validation dataset (VAD), comprising 3835, 1917 and 1917 subjects. For each sex, several multivariate prediction models were fitted from the TRD using age, weight, height and possibly waist circumference. The most accurate model was selected from the TED and then applied to the VAD and a French DXA dataset (French DB) (526 men and 529 women) to assess the prediction accuracy in comparison with that of five published univariate models, for which adjusted formulas were re-estimated using the TRD. Waist circumference was found to improve the prediction accuracy, especially in men. For BF%, the standard error of prediction (SEP) values were 3.26 (3.75) % for men and 3.47 (3.95)% for women in the VAD (French DB), as good as those of the adjusted univariate models. Moreover, the SEP values for the prediction of body and appendicular lean masses ranged from 1.39 to 2.75 kg for both the sexes. The prediction accuracy was best for age < 65 years, BMI < 30 kg/m2 and the Hispanic ethnicity. The application of our multivariate model to large populations could be useful to address various public health issues.
Two stage neural network modelling for robust model predictive control.

Science.gov (United States)

Patan, Krzysztof

2018-01-01

The paper proposes a novel robust model predictive control scheme realized by means of artificial neural networks. The neural networks are used twofold: to design the so-called fundamental model of a plant and to catch uncertainty associated with the plant model. In order to simplify the optimization process carried out within the framework of predictive control an instantaneous linearization is applied which renders it possible to define the optimization problem in the form of constrained quadratic programming. Stability of the proposed control system is also investigated by showing that a cost function is monotonically decreasing with respect to time. Derived robust model predictive control is tested and validated on the example of a pneumatic servomechanism working at different operating regimes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Hybrid Corporate Performance Prediction Model Considering Technical Capability

Directory of Open Access Journals (Sweden)

Joonhyuck Lee

2016-07-01

Full Text Available Many studies have tried to predict corporate performance and stock prices to enhance investment profitability using qualitative approaches such as the Delphi method. However, developments in data processing technology and machine-learning algorithms have resulted in efforts to develop quantitative prediction models in various managerial subject areas. We propose a quantitative corporate performance prediction model that applies the support vector regression (SVR algorithm to solve the problem of the overfitting of training data and can be applied to regression problems. The proposed model optimizes the SVR training parameters based on the training data, using the genetic algorithm to achieve sustainable predictability in changeable markets and managerial environments. Technology-intensive companies represent an increasing share of the total economy. The performance and stock prices of these companies are affected by their financial standing and their technological capabilities. Therefore, we apply both financial indicators and technical indicators to establish the proposed prediction model. Here, we use time series data, including financial, patent, and corporate performance information of 44 electronic and IT companies. Then, we predict the performance of these companies as an empirical verification of the prediction performance of the proposed model.
Rutile nanopowders for pigment production: Formation mechanism and particle size prediction

Science.gov (United States)

Zhang, Wu; Tang, Hongxin

2018-01-01

Formation mechanism and particle size prediction of rutile nanoparticles for pigment production were investigated. Anatase nanoparticles were observed by oriented attachment with parallel lattice fringe spaces of 0.2419 nm. Upon increasing the calcination temperature, the (1 1 0) plane of rutile was gradually observed, suggesting that the anatase (1 0 3) planes undergo internal structural rearrangement of oxygen and titanium ions into rutile phase due to ionic diffusion. Backpropagation neural network was used to predict particle size of rutile nanopowders, the prediction errors were all smaller than 2%, providing an efficient method to control particle size in pigment production.
Studies of biaxial mechanical properties and nonlinear finite element modeling of skin.

Science.gov (United States)

Shang, Xituan; Yen, Michael R T; Gaber, M Waleed

2010-06-01

The objective of this research is to conduct mechanical property studies of skin from two individual but potentially connected aspects. One is to determine the mechanical properties of the skin experimentally by biaxial tests, and the other is to use the finite element method to model the skin properties. Dynamic biaxial tests were performed on 16 pieces of abdominal skin specimen from rats. Typical biaxial stress-strain responses show that skin possesses anisotropy, nonlinearity and hysteresis. To describe the stress-strain relationship in forms of strain energy function, the material constants of each specimen were obtained and the results show a high correlation between theory and experiments. Based on the experimental results, a finite element model of skin was built to model the skin's special properties including anisotropy and nonlinearity. This model was based on Arruda and Boyce's eight-chain model and Bischoff et al.'s finite element model of skin. The simulation results show that the isotropic, nonlinear eight-chain model could predict the skin's anisotropic and nonlinear responses to biaxial loading by the presence of an anisotropic prestress state.

Life prediction and mechanical reliability of NT551 silicon nitride

Science.gov (United States)

Andrews, Mark Jay

The inert strength and fatigue performance of a diesel engine exhaust valve made from silicon nitride (Si3N4) ceramic were assessed. The Si3N4 characterized in this study was manufactured by Saint Gobain/Norton Industrial Ceramics and was designated as NT551. The evaluation was made utilizing a probabilistic life prediction algorithm that combined censored test specimen strength data with a Weibull distribution function and the stress field of the ceramic valve obtained from finite element analysis. The major assumptions of the life prediction algorithm are that the bulk ceramic material is isotropic and homogeneous and that the strength-limiting flaws are uniformly distributed. The results from mechanical testing indicated that NT551 was not a homogeneous ceramic and that its strength were functions of temperature, loading rate, and machining orientation. Fractographic analysis identified four different failure modes; 2 were identified as inhomogeneities that were located throughout the bulk of NT551 and were due to processing operations. The fractographic analysis concluded that the strength degradation of NT551 observed from the temperature and loading rate test parameters was due to a change of state that occurred in its secondary phase. Pristine and engine-tested valves made from NT551 were loaded to failure and the inert strengths were obtained. Fractographic analysis of the valves identified the same four failure mechanisms as found with the test specimens. The fatigue performance and the inert strength of the Si3N 4 valves were assessed from censored and uncensored test specimen strength data, respectively. The inert strength failure probability predictions were compared to the inert strength of the Si3N4 valves. The inert strength failure probability predictions were more conservative than the strength of the valves. The lack of correlation between predicted and actual valve strength was due to the nonuniform distribution of inhomogeneities present in NT
Methodological advances in predicting flow-induced dynamics of plants using mechanical-engineering theory.

Science.gov (United States)

de Langre, Emmanuel

2012-03-15

The modeling of fluid-structure interactions, such as flow-induced vibrations, is a well-developed field of mechanical engineering. Many methods exist, and it seems natural to apply them to model the behavior of plants, and potentially other cantilever-like biological structures, under flow. Overcoming this disciplinary divide, and the application of such models to biological systems, will significantly advance our understanding of ecological patterns and processes and improve our predictive capabilities. Nonetheless, several methodological issues must first be addressed, which I describe here using two practical examples that have strong similarities: one from agricultural sciences and the other from nuclear engineering. Very similar issues arise in both: individual and collective behavior, small and large space and time scales, porous modeling, standard and extreme events, trade-off between the surface of exchange and individual or collective risk of damage, variability, hostile environments and, in some aspects, evolution. The conclusion is that, although similar issues do exist, which need to be exploited in some detail, there is a significant gap that requires new developments. It is obvious that living plants grow in and adapt to their environment, which certainly makes plant biomechanics fundamentally distinct from classical mechanical engineering. Moreover, the selection processes in biology and in human engineering are truly different, making the issue of safety different as well. A thorough understanding of these similarities and differences is needed to work efficiently in the application of a mechanistic approach to ecology.
Active tension network model suggests an exotic mechanical state realized in epithelial tissues

Science.gov (United States)

Noll, Nicholas; Mani, Madhav; Heemskerk, Idse; Streichan, Sebastian J.; Shraiman, Boris I.

2017-12-01

Mechanical interactions play a crucial role in epithelial morphogenesis, yet understanding the complex mechanisms through which stress and deformation affect cell behaviour remains an open problem. Here we formulate and analyse the active tension network (ATN) model, which assumes that the mechanical balance of cells within a tissue is dominated by cortical tension and introduces tension-dependent active remodelling of the cortex. We find that ATNs exhibit unusual mechanical properties. Specifically, an ATN behaves as a fluid at short times, but at long times supports external tension like a solid. Furthermore, an ATN has an extensively degenerate equilibrium mechanical state associated with a discrete conformal--`isogonal'--deformation of cells. The ATN model predicts a constraint on equilibrium cell geometries, which we demonstrate to approximately hold in certain epithelial tissues. We further show that isogonal modes are observed in the fruit fly embryo, accounting for the striking variability of apical areas of ventral cells and helping understand the early phase of gastrulation. Living matter realizes new and exotic mechanical states, the study of which helps to understand biological phenomena.
A tool model for predicting atmospheric kinetics with sensitivity analysis

Institute of Scientific and Technical Information of China (English)

无

2001-01-01

A package( a tool model) for program of predicting atmospheric chemical kinetics with sensitivity analysis is presented. The new direct method of calculating the first order sensitivity coefficients using sparse matrix technology to chemical kinetics is included in the tool model, it is only necessary to triangularize the matrix related to the Jacobian matrix of the model equation. The Gear type procedure is used to integrate amodel equation and its coupled auxiliary sensitivity coefficient equations. The FORTRAN subroutines of the model equation, the sensitivity coefficient equations, and their Jacobian analytical expressions are generated automatically from a chemical mechanism. The kinetic representation for the model equation and its sensitivity coefficient equations, and their Jacobian matrix is presented. Various FORTRAN subroutines in packages, such as SLODE, modified MA28, Gear package, with which the program runs in conjunction are recommended.The photo-oxidation of dimethyl disulfide is used for illustration.
Dynamic Simulation of Human Gait Model With Predictive Capability.

Science.gov (United States)

Sun, Jinming; Wu, Shaoli; Voglewede, Philip A

2018-03-01

In this paper, it is proposed that the central nervous system (CNS) controls human gait using a predictive control approach in conjunction with classical feedback control instead of exclusive classical feedback control theory that controls based on past error. To validate this proposition, a dynamic model of human gait is developed using a novel predictive approach to investigate the principles of the CNS. The model developed includes two parts: a plant model that represents the dynamics of human gait and a controller that represents the CNS. The plant model is a seven-segment, six-joint model that has nine degrees-of-freedom (DOF). The plant model is validated using data collected from able-bodied human subjects. The proposed controller utilizes model predictive control (MPC). MPC uses an internal model to predict the output in advance, compare the predicted output to the reference, and optimize the control input so that the predicted error is minimal. To decrease the complexity of the model, two joints are controlled using a proportional-derivative (PD) controller. The developed predictive human gait model is validated by simulating able-bodied human gait. The simulation results show that the developed model is able to simulate the kinematic output close to experimental data.
Massive Predictive Modeling using Oracle R Enterprise

CERN Multimedia

CERN. Geneva

2014-01-01

R is fast becoming the lingua franca for analyzing data via statistics, visualization, and predictive analytics. For enterprise-scale data, R users have three main concerns: scalability, performance, and production deployment. Oracle's R-based technologies - Oracle R Distribution, Oracle R Enterprise, Oracle R Connector for Hadoop, and the R package ROracle - address these concerns. In this talk, we introduce Oracle's R technologies, highlighting how each enables R users to achieve scalability and performance while making production deployment of R results a natural outcome of the data analyst/scientist efforts. The focus then turns to Oracle R Enterprise with code examples using the transparency layer and embedded R execution, targeting massive predictive modeling. One goal behind massive predictive modeling is to build models per entity, such as customers, zip codes, simulations, in an effort to understand behavior and tailor predictions at the entity level. Predictions...
Predicting lymphatic filariasis transmission and elimination dynamics using a multi-model ensemble framework

Directory of Open Access Journals (Sweden)

Morgan E. Smith

2017-03-01

Full Text Available Mathematical models of parasite transmission provide powerful tools for assessing the impacts of interventions. Owing to complexity and uncertainty, no single model may capture all features of transmission and elimination dynamics. Multi-model ensemble modelling offers a framework to help overcome biases of single models. We report on the development of a first multi-model ensemble of three lymphatic filariasis (LF models (EPIFIL, LYMFASIM, and TRANSFIL, and evaluate its predictive performance in comparison with that of the constituents using calibration and validation data from three case study sites, one each from the three major LF endemic regions: Africa, Southeast Asia and Papua New Guinea (PNG. We assessed the performance of the respective models for predicting the outcomes of annual MDA strategies for various baseline scenarios thought to exemplify the current endemic conditions in the three regions. The results show that the constructed multi-model ensemble outperformed the single models when evaluated across all sites. Single models that best fitted calibration data tended to do less well in simulating the out-of-sample, or validation, intervention data. Scenario modelling results demonstrate that the multi-model ensemble is able to compensate for variance between single models in order to produce more plausible predictions of intervention impacts. Our results highlight the value of an ensemble approach to modelling parasite control dynamics. However, its optimal use will require further methodological improvements as well as consideration of the organizational mechanisms required to ensure that modelling results and data are shared effectively between all stakeholders.
The importance of the strain rate and creep on the stress corrosion cracking mechanisms and models

International Nuclear Information System (INIS)

Aly, Omar F.; Mattar Neto, Miguel; Schvartzman, Monica M.A.M.

2011-01-01

Stress corrosion cracking is a nuclear, power, petrochemical, and other industries equipment and components (like pressure vessels, nozzles, tubes, accessories) life degradation mode, involving fragile fracture. The stress corrosion cracking failures can produce serious accidents, and incidents which can put on risk the safety, reliability, and efficiency of many plants. These failures are of very complex prediction. The stress corrosion cracking mechanisms are based on three kinds of factors: microstructural, mechanical and environmental. Concerning the mechanical factors, various authors prefer to consider the crack tip strain rate rather than stress, as a decisive factor which contributes to the process: this parameter is directly influenced by the creep strain rate of the material. Based on two KAPL-Knolls Atomic Power Laboratory experimental studies in SSRT (slow strain rate test) and CL (constant load) test, for prediction of primary water stress corrosion cracking in nickel based alloys, it has done a data compilation of the film rupture mechanism parameters, for modeling PWSCC of Alloy 600 and discussed the importance of the strain rate and the creep on the stress corrosion cracking mechanisms and models. As derived from this study, a simple theoretical model is proposed, and it is showed that the crack growth rate estimated with Brazilian tests results with Alloy 600 in SSRT, are according with the KAPL ones and other published literature. (author)
Prediction of residential radon exposure of the whole Swiss population: comparison of model-based predictions with measurement-based predictions.

Science.gov (United States)

Hauri, D D; Huss, A; Zimmermann, F; Kuehni, C E; Röösli, M

2013-10-01

Radon plays an important role for human exposure to natural sources of ionizing radiation. The aim of this article is to compare two approaches to estimate mean radon exposure in the Swiss population: model-based predictions at individual level and measurement-based predictions based on measurements aggregated at municipality level. A nationwide model was used to predict radon levels in each household and for each individual based on the corresponding tectonic unit, building age, building type, soil texture, degree of urbanization, and floor. Measurement-based predictions were carried out within a health impact assessment on residential radon and lung cancer. Mean measured radon levels were corrected for the average floor distribution and weighted with population size of each municipality. Model-based predictions yielded a mean radon exposure of the Swiss population of 84.1 Bq/m(3) . Measurement-based predictions yielded an average exposure of 78 Bq/m(3) . This study demonstrates that the model- and the measurement-based predictions provided similar results. The advantage of the measurement-based approach is its simplicity, which is sufficient for assessing exposure distribution in a population. The model-based approach allows predicting radon levels at specific sites, which is needed in an epidemiological study, and the results do not depend on how the measurement sites have been selected. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
A burnout prediction model based around char morphology

Energy Technology Data Exchange (ETDEWEB)

T. Wu; E. Lester; M. Cloke [University of Nottingham, Nottingham (United Kingdom). Nottingham Energy and Fuel Centre

2005-07-01

Poor burnout in a coal-fired power plant has marked penalties in the form of reduced energy efficiency and elevated waste material that can not be utilized. The prediction of coal combustion behaviour in a furnace is of great significance in providing valuable information not only for process optimization but also for coal buyers in the international market. Coal combustion models have been developed that can make predictions about burnout behaviour and burnout potential. Most of these kinetic models require standard parameters such as volatile content, particle size and assumed char porosity in order to make a burnout prediction. This paper presents a new model called the Char Burnout Model (ChB) that also uses detailed information about char morphology in its prediction. The model can use data input from one of two sources. Both sources are derived from image analysis techniques. The first from individual analysis and characterization of real char types using an automated program. The second from predicted char types based on data collected during the automated image analysis of coal particles. Modelling results were compared with a different carbon burnout kinetic model and burnout data from re-firing the chars in a drop tube furnace operating at 1300{sup o}C, 5% oxygen across several residence times. An improved agreement between ChB model and DTF experimental data proved that the inclusion of char morphology in combustion models can improve model predictions. 27 refs., 4 figs., 4 tabs.
Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database.

Science.gov (United States)

Wessler, Benjamin S; Lai Yh, Lana; Kramer, Whitney; Cangelosi, Michael; Raman, Gowri; Lutz, Jennifer S; Kent, David M

2015-07-01

Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described. We conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. There is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood. © 2015 American Heart Association, Inc.
Prediction of resource volumes at untested locations using simple local prediction models

Science.gov (United States)

Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.

2006-01-01

This paper shows how local spatial nonparametric prediction models can be applied to estimate volumes of recoverable gas resources at individual undrilled sites, at multiple sites on a regional scale, and to compute confidence bounds for regional volumes based on the distribution of those estimates. An approach that combines cross-validation, the jackknife, and bootstrap procedures is used to accomplish this task. Simulation experiments show that cross-validation can be applied beneficially to select an appropriate prediction model. The cross-validation procedure worked well for a wide range of different states of nature and levels of information. Jackknife procedures are used to compute individual prediction estimation errors at undrilled locations. The jackknife replicates also are used with a bootstrap resampling procedure to compute confidence bounds for the total volume. The method was applied to data (partitioned into a training set and target set) from the Devonian Antrim Shale continuous-type gas play in the Michigan Basin in Otsego County, Michigan. The analysis showed that the model estimate of total recoverable volumes at prediction sites is within 4 percent of the total observed volume. The model predictions also provide frequency distributions of the cell volumes at the production unit scale. Such distributions are the basis for subsequent economic analyses. ?? Springer Science+Business Media, LLC 2007.
Mechanical properties of open-cell metallic biomaterials manufactured using additive manufacturing

International Nuclear Information System (INIS)

Campoli, G.; Borleffs, M.S.; Amin Yavari, S.; Wauthle, R.; Weinans, H.; Zadpoor, A.A.

2013-01-01

Highlights: ► Finite element (FE) models were used to predict the mechanical properties of porous biomaterials. ► Porous materials were produced using additive manufacturing techniques. ► Manufacturing irregularities need to be implemented in FE models. ► FE models are more accurate than analytical models in predicting mechanical properties. - Abstract: An important practical problem in application of open-cell porous biomaterials is the prediction of the mechanical properties of the material given its micro-architecture and the properties of its matrix material. Although analytical methods can be used for this purpose, these models are often based on several simplifying assumptions with respect to the complex architecture and cannot provide accurate prediction results. The aim of the current study is to present finite element (FE) models that can predict the mechanical properties of porous titanium produced using selective laser melting or selective electron beam melting. The irregularities caused by the manufacturing process including structural variations of the architecture are implemented in the FE models using statistical models. The predictions of FE models are compared with those of analytical models and are tested against experimental data. It is shown that, as opposed to analytical models, the predictions of FE models are in agreement with experimental observations. It is concluded that manufacturing irregularities significantly affect the mechanical properties of porous biomaterials
A burnout prediction model based around char morphology

Energy Technology Data Exchange (ETDEWEB)

Tao Wu; Edward Lester; Michael Cloke [University of Nottingham, Nottingham (United Kingdom). School of Chemical, Environmental and Mining Engineering

2006-05-15

Several combustion models have been developed that can make predictions about coal burnout and burnout potential. Most of these kinetic models require standard parameters such as volatile content and particle size to make a burnout prediction. This article presents a new model called the char burnout (ChB) model, which also uses detailed information about char morphology in its prediction. The input data to the model is based on information derived from two different image analysis techniques. One technique generates characterization data from real char samples, and the other predicts char types based on characterization data from image analysis of coal particles. The pyrolyzed chars in this study were created in a drop tube furnace operating at 1300{sup o}C, 200 ms, and 1% oxygen. Modeling results were compared with a different carbon burnout kinetic model as well as the actual burnout data from refiring the same chars in a drop tube furnace operating at 1300{sup o}C, 5% oxygen, and residence times of 200, 400, and 600 ms. A good agreement between ChB model and experimental data indicates that the inclusion of char morphology in combustion models could well improve model predictions. 38 refs., 5 figs., 6 tabs.
Comparative Study of Bancruptcy Prediction Models

Directory of Open Access Journals (Sweden)

Isye Arieshanti

2013-09-01

Full Text Available Early indication of bancruptcy is important for a company. If companies aware of potency of their bancruptcy, they can take a preventive action to anticipate the bancruptcy. In order to detect the potency of a bancruptcy, a company can utilize a a model of bancruptcy prediction. The prediction model can be built using a machine learning methods. However, the choice of machine learning methods should be performed carefully. Because the suitability of a model depends on the problem specifically. Therefore, in this paper we perform a comparative study of several machine leaning methods for bancruptcy prediction. According to the comparative study, the performance of several models that based on machine learning methods (k-NN, fuzzy k-NN, SVM, Bagging Nearest Neighbour SVM, Multilayer Perceptron(MLP, Hybrid of MLP + Multiple Linear Regression, it can be showed that fuzzy k-NN method achieve the best performance with accuracy 77.5%
A grey NGM(1,1, k) self-memory coupling prediction model for energy consumption prediction.

Science.gov (United States)

Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling

2014-01-01

Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1, k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1, k) model. The traditional grey model's weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1, k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span.
Risk predictive modelling for diabetes and cardiovascular disease.

Science.gov (United States)

Kengne, Andre Pascal; Masconi, Katya; Mbanya, Vivian Nchanchou; Lekoubou, Alain; Echouffo-Tcheugui, Justin Basile; Matsha, Tandi E

2014-02-01

Absolute risk models or clinical prediction models have been incorporated in guidelines, and are increasingly advocated as tools to assist risk stratification and guide prevention and treatments decisions relating to common health conditions such as cardiovascular disease (CVD) and diabetes mellitus. We have reviewed the historical development and principles of prediction research, including their statistical underpinning, as well as implications for routine practice, with a focus on predictive modelling for CVD and diabetes. Predictive modelling for CVD risk, which has developed over the last five decades, has been largely influenced by the Framingham Heart Study investigators, while it is only ∼20 years ago that similar efforts were started in the field of diabetes. Identification of predictive factors is an important preliminary step which provides the knowledge base on potential predictors to be tested for inclusion during the statistical derivation of the final model. The derived models must then be tested both on the development sample (internal validation) and on other populations in different settings (external validation). Updating procedures (e.g. recalibration) should be used to improve the performance of models that fail the tests of external validation. Ultimately, the effect of introducing validated models in routine practice on the process and outcomes of care as well as its cost-effectiveness should be tested in impact studies before wide dissemination of models beyond the research context. Several predictions models have been developed for CVD or diabetes, but very few have been externally validated or tested in impact studies, and their comparative performance has yet to be fully assessed. A shift of focus from developing new CVD or diabetes prediction models to validating the existing ones will improve their adoption in routine practice.
Statistical Model Predictions for p+p and Pb+Pb Collisions at LHC

CERN Document Server

Kraus, I; Oeschler, H; Redlich, K; Wheaton, S

2009-01-01

Particle production in p+p and central Pb+Pb collisions at LHC is discussed in the context of the statistical thermal model. For heavy-ion collisions, predictions of various particle ratios are presented. The sensitivity of several ratios on the temperature and the baryon chemical potential is studied in detail, and some of them, which are particularly appropriate to determine the chemical freeze-out point experimentally, are indicated. Considering elementary interactions on the other hand, we focus on strangeness production and its possible suppression. Extrapolating the thermal parameters to LHC energy, we present predictions of the statistical model for particle yields in p+p collisions. We quantify the strangeness suppression by the correlation volume parameter and discuss its influence on particle production. We propose observables that can provide deeper insight into the mechanism of strangeness production and suppression at LHC.
Model-based uncertainty in species range prediction

DEFF Research Database (Denmark)

Pearson, R. G.; Thuiller, Wilfried; Bastos Araujo, Miguel

2006-01-01

Aim Many attempts to predict the potential range of species rely on environmental niche (or 'bioclimate envelope') modelling, yet the effects of using different niche-based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions...
Reliability analysis and prediction of mixed mode load using Markov Chain Model

International Nuclear Information System (INIS)

Nikabdullah, N.; Singh, S. S. K.; Alebrahim, R.; Azizi, M. A.; K, Elwaleed A.; Noorani, M. S. M.

2014-01-01

The aim of this paper is to present the reliability analysis and prediction of mixed mode loading by using a simple two state Markov Chain Model for an automotive crankshaft. The reliability analysis and prediction for any automotive component or structure is important for analyzing and measuring the failure to increase the design life, eliminate or reduce the likelihood of failures and safety risk. The mechanical failures of the crankshaft are due of high bending and torsion stress concentration from high cycle and low rotating bending and torsional stress. The Markov Chain was used to model the two states based on the probability of failure due to bending and torsion stress. In most investigations it revealed that bending stress is much serve than torsional stress, therefore the probability criteria for the bending state would be higher compared to the torsion state. A statistical comparison between the developed Markov Chain Model and field data was done to observe the percentage of error. The reliability analysis and prediction was derived and illustrated from the Markov Chain Model were shown in the Weibull probability and cumulative distribution function, hazard rate and reliability curve and the bathtub curve. It can be concluded that Markov Chain Model has the ability to generate near similar data with minimal percentage of error and for a practical application; the proposed model provides a good accuracy in determining the reliability for the crankshaft under mixed mode loading

Modeling Mechanical Properties of Aluminum Composite Produced Using Stir Casting Method

Directory of Open Access Journals (Sweden)

Muhammad Hayat Jokhio

2011-01-01

Full Text Available ANN (Artificial Neural Networks modeling methodology was adopted for predicting mechanical properties of aluminum cast composite materials. For this purpose aluminum alloy were developed using conventional foundry method. The composite materials have complex nature which posses the nonlinear relationship among heat treatment, processing parameters, and composition and affects their mechanical properties. These nonlinear relation ships with properties can more efficiently be modeled by ANNs. Neural networks modeling needs sufficient data base consisting of mechanical properties, chemical composition and processing parameters. Such data base is not available for modeling. Therefore, a large range of experimental work was carried out for the development of aluminum composite materials. Alloys containing Cu, Mg and Zn as matrix were reinforced with 1- 15% Al2O3 particles using stir casting method. Alloys composites were cast in a metal mold. More than eighty standard samples were prepared for tensile tests. Sixty samples were given solution treatments at 580oC for half an hour and tempered at 120oC for 24 hours. The samples were characterized to investigate mechanical properties using Scanning Electron Microscope, X-Ray Spectrometer, Optical Metallurgical Microscope, Vickers Hardness, Universal Testing Machine and Abrasive Wear Testing Machine. A MLP (Multilayer Perceptron feedforward was developed and used for modeling purpose. Training, testing and validation of the model were carried out using back propagation learning algorithm. The modeling results show that an architecture of 14 inputs with 9 hidden neurons and 4 outputs which includes the tensile strength, elongation, hardness and abrasive wear resistance gives reasonably accurate results with an error within the range of 2-7 % in training, testing and validation.
Modeling mechanical properties of aluminum composite produced using stir casting method

International Nuclear Information System (INIS)

Jokhio, M.H.; Panhwar, M.I.; Unar, M.A.

2011-01-01

ANN (Artificial Neural Networks) modeling methodology was adopted for predicting mechanical properties of aluminum cast composite materials. For this purpose aluminum alloy were developed using conventional foundry method. The composite materials have complex nature which posses the nonlinear relationship among heat treatment, processing parameters, and composition and affects their mechanical properties. These nonlinear relation ships with properties can more efficiently be modeled by ANNs. Neural networks modeling needs sufficient data base consisting of mechanical properties, chemical composition and processing parameters. Such data base is not available for modeling. Therefore, a large range of experimental work was carried out for the development of aluminum composite materials. Alloys containing Cu, Mg and Zn as matrix were reinforced with 1- 15% AI/sub 2/O/sub 3/ particles using stir casting method. Alloys composites were cast in a metal mold. More than eighty standard samples were prepared for tensile tests. Sixty samples were given solution treatments at 580 deg. C for half an hour and tempered at 120 deg. C for 24 hours. The samples were characterized to investigate mechanical properties using Scanning Electron Microscope, X-Ray Spectrometer, Optical Metallurgical Microscope, Vickers Hardness, Universal Testing Machine and Abrasive Wear Testing Machine. A MLP (Multilayer Perceptron) feed forward was developed and used for modeling purpose. Training, testing and validation of the model were carried out using back propagation learning algorithm. The modeling results show that an architecture of 14 inputs with 9 hidden neurons and 4 outputs which includes the tensile strength, elongation, hardness and abrasive wear resistance gives reasonably accurate results with an error within the range of 2-7 % in training, testing and validation. (author)
Survival prediction model for postoperative hepatocellular carcinoma patients.

Science.gov (United States)

Ren, Zhihui; He, Shasha; Fan, Xiaotang; He, Fangping; Sang, Wei; Bao, Yongxing; Ren, Weixin; Zhao, Jinming; Ji, Xuewen; Wen, Hao

2017-09-01

This study is to establish a predictive index (PI) model of 5-year survival rate for patients with hepatocellular carcinoma (HCC) after radical resection and to evaluate its prediction sensitivity, specificity, and accuracy.Patients underwent HCC surgical resection were enrolled and randomly divided into prediction model group (101 patients) and model evaluation group (100 patients). Cox regression model was used for univariate and multivariate survival analysis. A PI model was established based on multivariate analysis and receiver operating characteristic (ROC) curve was drawn accordingly. The area under ROC (AUROC) and PI cutoff value was identified.Multiple Cox regression analysis of prediction model group showed that neutrophil to lymphocyte ratio, histological grade, microvascular invasion, positive resection margin, number of tumor, and postoperative transcatheter arterial chemoembolization treatment were the independent predictors for the 5-year survival rate for HCC patients. The model was PI = 0.377 × NLR + 0.554 × HG + 0.927 × PRM + 0.778 × MVI + 0.740 × NT - 0.831 × transcatheter arterial chemoembolization (TACE). In the prediction model group, AUROC was 0.832 and the PI cutoff value was 3.38. The sensitivity, specificity, and accuracy were 78.0%, 80%, and 79.2%, respectively. In model evaluation group, AUROC was 0.822, and the PI cutoff value was well corresponded to the prediction model group with sensitivity, specificity, and accuracy of 85.0%, 83.3%, and 84.0%, respectively.The PI model can quantify the mortality risk of hepatitis B related HCC with high sensitivity, specificity, and accuracy.
Methodology for Designing Models Predicting Success of Infertility Treatment

OpenAIRE

Alireza Zarinara; Mohammad Mahdi Akhondi; Hojjat Zeraati; Koorsh Kamali; Kazem Mohammad

2016-01-01

Abstract Background: The prediction models for infertility treatment success have presented since 25 years ago. There are scientific principles for designing and applying the prediction models that is also used to predict the success rate of infertility treatment. The purpose of this study is to provide basic principles for designing the model to predic infertility treatment success. Materials and Methods: In this paper, the principles for developing predictive models are explained and...
A study on the development of advanced models to predict the critical heat flux for water and liquid metals

International Nuclear Information System (INIS)

Lee, Yong Bum

1994-02-01

The critical heat flux (CHF) phenomenon in the two-phase convective flows has been an important issue in the fields of design and safety analysis of light water reactor (LWR) as well as sodium cooled liquid metal fast breeder reactor (LMFBR). Especially in the LWR application many physical aspects of the CHF phenomenon are understood and reliable correlations and mechanistic models to predict the CHF condition have been proposed. However, there are few correlations and models which are applicable to liquid metals. Compared with water, liquid metals show a divergent picture for boiling pattern. Therefore, the CHF conditions obtained from investigations with water cannot be applied to liquid metals. In this work a mechanistic model to predict the CHF of water and a correlation for liquid metals are developed. First, a mechanistic model to predict the CHF in flow boiling at low quality was developed based on the liquid sublayer dryout mechanism. In this approach the CHF is assumed to occur when a vapor blanket isolates the liquid sublayer from bulk liquid and then the liquid entering the sublayer falls short of balancing the rate of sublayer dryout by vaporization. Therefore, the vapor blanket velocity is the key parameter. In this work the vapor blanket velocity is theoretically determined based on mass, energy, and momentum balance and finally the mechanistic model to predict the CHF in flow boiling at low quality is developed. The accuracy of the present model is evaluated by comparing model predictions with the experimental data and tabular data of look-up tables. The predictions of the present model agree well with extensive CHF data. In the latter part a correlation to predict the CHF for liquid metals is developed based on the flow excursion mechanism. By using Baroczy two-phase frictional pressure drop correlation and Ledinegg instability criterion, the relationship between the CHF of liquid metals and the principal parameters is derived and finally the
Mechanisms and predictions for subcooled flow boiling CHF

International Nuclear Information System (INIS)

Liu, Wei; Nariai, Hideki; Inasaka, Fujio

2000-01-01

Corresponding to the two kinds of flow pattern reported in literature for subcooled flow boiling, two kinds of CHF triggering mechanism are considered existing with working in different working scope. On the base of a criterion proposed recently by the present authors, subcooled flow boiling data firstly are categorized into two groups by judging whether the first kind or the second kind of flow pattern is established. Possible CHF triggering mechanisms and prediction methods for the two kinds of flow pattern condition are discussed. By considering both the flow pattern development and CHF triggering mechanism, a detailed data categorization is carried out. The corresponding CHF occurrence properties in different data groups are summarized. Parametric trends are reviewed for the first and second kind of data group working condition respectively. Mass flux, pressure, inlet subcooling and inner diameter show almost same effects in the two different working conditions, while the ratio of heated length to diameter's effects on CHF show to be different. Research for the L/D effect on the CHF transverse the interface of the different data groups is carried out. (author)
Prediction of the time course of callus stiffness as a function of mechanical parameters in experimental rat fracture healing studies--a numerical study.

Directory of Open Access Journals (Sweden)

Tim Wehner

Full Text Available Numerous experimental fracture healing studies are performed on rats, in which different experimental, mechanical parameters are applied, thereby prohibiting direct comparison between each other. Numerical fracture healing simulation models are able to predict courses of fracture healing and offer support for pre-planning animal experiments and for post-hoc comparison between outcomes of different in vivo studies. The aims of this study are to adapt a pre-existing fracture healing simulation algorithm for sheep and humans to the rat, to corroborate it using the data of numerous different rat experiments, and to provide healing predictions for future rat experiments. First, material properties of different tissue types involved were adjusted by comparing experimentally measured callus stiffness to respective simulated values obtained in three finite element (FE models. This yielded values for Young's moduli of cortical bone, woven bone, cartilage, and connective tissue of 15,750 MPa, 1,000 MPa, 5 MPa, and 1 MPa, respectively. Next, thresholds in the underlying mechanoregulatory tissue differentiation rules were calibrated by modifying model parameters so that predicted fracture callus stiffness matched experimental data from a study that used rigid and flexible fixators. This resulted in strain thresholds at higher magnitudes than in models for sheep and humans. The resulting numerical model was then used to simulate numerous fracture healing scenarios from literature, showing a considerable mismatch in only 6 of 21 cases. Based on this corroborated model, a fit curve function was derived which predicts the increase of callus stiffness dependent on bodyweight, fixation stiffness, and fracture gap size. By mathematically predicting the time course of the healing process prior to the animal studies, the data presented in this work provides support for planning new fracture healing experiments in rats. Furthermore, it allows one to transfer and
Hidden Semi-Markov Models for Predictive Maintenance

Directory of Open Access Journals (Sweden)

Francesco Cartella

2015-01-01

Full Text Available Realistic predictive maintenance approaches are essential for condition monitoring and predictive maintenance of industrial machines. In this work, we propose Hidden Semi-Markov Models (HSMMs with (i no constraints on the state duration density function and (ii being applied to continuous or discrete observation. To deal with such a type of HSMM, we also propose modifications to the learning, inference, and prediction algorithms. Finally, automatic model selection has been made possible using the Akaike Information Criterion. This paper describes the theoretical formalization of the model as well as several experiments performed on simulated and real data with the aim of methodology validation. In all performed experiments, the model is able to correctly estimate the current state and to effectively predict the time to a predefined event with a low overall average absolute error. As a consequence, its applicability to real world settings can be beneficial, especially where in real time the Remaining Useful Lifetime (RUL of the machine is calculated.
An articulated predictive model for fluid-free artificial basilar membrane as broadband frequency sensor

Science.gov (United States)

Ahmed, Riaz; Banerjee, Sourav

2018-02-01

In this article, an extremely versatile predictive model for a newly developed Basilar meta-Membrane (BM2) sensors is reported with variable engineering parameters that contribute to it's frequency selection capabilities. The predictive model reported herein is for advancement over existing method by incorporating versatile and nonhomogeneous (e.g. functionally graded) model parameters that could not only exploit the possibilities of creating complex combinations of broadband frequency sensors but also explain the unique unexplained physical phenomenon that prevails in BM2, e.g. tailgating waves. In recent years, few notable attempts were made to fabricate the artificial basilar membrane, mimicking the mechanics of the human cochlea within a very short range of frequencies. To explain the operation of these sensors a few models were proposed. But, we fundamentally argue the "fabrication to explanation" approach and proposed the model driven predictive design process for the design any (BM2) as broadband sensors. Inspired by the physics of basilar membrane, frequency domain predictive model is proposed where both the material and geometrical parameters can be arbitrarily varied. Broadband frequency is applicable in many fields of science, engineering and technology, such as, sensors for chemical, biological and acoustic applications. With the proposed model, which is three times faster than its FEM counterpart, it is possible to alter the attributes of the selected length of the designed sensor using complex combinations of model parameters, based on target frequency applications. Finally, the tailgating wave peaks in the artificial basilar membranes that prevails in the previously reported experimental studies are also explained using the proposed model.
Collapse mechanisms and strength prediction of reinforced concrete pile caps

DEFF Research Database (Denmark)

Jensen, Uffe G.; Hoang, Linh Cao

2012-01-01

. Calculations have been compared with nearly 200 test results found in the literature. Satisfactory agreement has been found. The analyses are conducted on concentrically loaded caps supported by four piles. The paper briefly outlines how the approach may be extended to more complicated loadings and geometries......This paper describes an upper bound plasticity approach for strength prediction of reinforced concrete pile caps. A number of collapse mechanisms are identified and analysed. The procedure leads to an estimate of the load-carrying capacity and an identification of the critical collapse mechanism...
Modeling and Control of CSTR using Model based Neural Network Predictive Control

OpenAIRE

Shrivastava, Piyush

2012-01-01

This paper presents a predictive control strategy based on neural network model of the plant is applied to Continuous Stirred Tank Reactor (CSTR). This system is a highly nonlinear process; therefore, a nonlinear predictive method, e.g., neural network predictive control, can be a better match to govern the system dynamics. In the paper, the NN model and the way in which it can be used to predict the behavior of the CSTR process over a certain prediction horizon are described, and some commen...
Consensus models to predict endocrine disruption for all ...

Science.gov (United States)

Humans are potentially exposed to tens of thousands of man-made chemicals in the environment. It is well known that some environmental chemicals mimic natural hormones and thus have the potential to be endocrine disruptors. Most of these environmental chemicals have never been tested for their ability to disrupt the endocrine system, in particular, their ability to interact with the estrogen receptor. EPA needs tools to prioritize thousands of chemicals, for instance in the Endocrine Disruptor Screening Program (EDSP). Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) was intended to be a demonstration of the use of predictive computational models on HTS data including ToxCast and Tox21 assays to prioritize a large chemical universe of 32464 unique structures for one specific molecular target – the estrogen receptor. CERAPP combined multiple computational models for prediction of estrogen receptor activity, and used the predicted results to build a unique consensus model. Models were developed in collaboration between 17 groups in the U.S. and Europe and applied to predict the common set of chemicals. Structure-based techniques such as docking and several QSAR modeling approaches were employed, mostly using a common training set of 1677 compounds provided by U.S. EPA, to build a total of 42 classification models and 8 regression models for binding, agonist and antagonist activity. All predictions were evaluated on ToxCast data and on an exte
Energy based prediction models for building acoustics

DEFF Research Database (Denmark)

Brunskog, Jonas

2012-01-01

In order to reach robust and simplified yet accurate prediction models, energy based principle are commonly used in many fields of acoustics, especially in building acoustics. This includes simple energy flow models, the framework of statistical energy analysis (SEA) as well as more elaborated...... principles as, e.g., wave intensity analysis (WIA). The European standards for building acoustic predictions, the EN 12354 series, are based on energy flow and SEA principles. In the present paper, different energy based prediction models are discussed and critically reviewed. Special attention is placed...... on underlying basic assumptions, such as diffuse fields, high modal overlap, resonant field being dominant, etc., and the consequences of these in terms of limitations in the theory and in the practical use of the models....
Response to a pure tone in a nonlinear mechanical-electrical-acoustical model of the cochlea.

Science.gov (United States)

Meaud, Julien; Grosh, Karl

2012-03-21

In this article, a nonlinear mathematical model is developed based on the physiology of the cochlea of the guinea pig. The three-dimensional intracochlear fluid dynamics are coupled to a micromechanical model of the organ of Corti and to electrical potentials in the cochlear ducts and outer hair cells (OHC). OHC somatic electromotility is modeled by linearized piezoelectric relations whereas the OHC hair-bundle mechanoelectrical transduction current is modeled as a nonlinear function of the hair-bundle deflection. The steady-state response of the cochlea to a single tone is simulated in the frequency domain using an alternating frequency time scheme. Compressive nonlinearity, harmonic distortion, and DC shift on the basilar membrane (BM), tectorial membrane (TM), and OHC potentials are predicted using a single set of parameters. The predictions of the model are verified by comparing simulations to available in vivo experimental data for basal cochlear mechanics. In particular, the model predicts more amplification on the reticular lamina (RL) side of the cochlear partition than on the BM, which replicates recent measurements. Moreover, small harmonic distortion and DC shifts are predicted on the BM, whereas more significant harmonic distortion and DC shifts are predicted in the RL and TM displacements and in the OHC potentials. Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.
Comparison of Simple Versus Performance-Based Fall Prediction Models

Directory of Open Access Journals (Sweden)

Shekhar K. Gadkaree BS

2015-05-01

Full Text Available Objective: To compare the predictive ability of standard falls prediction models based on physical performance assessments with more parsimonious prediction models based on self-reported data. Design: We developed a series of fall prediction models progressing in complexity and compared area under the receiver operating characteristic curve (AUC across models. Setting: National Health and Aging Trends Study (NHATS, which surveyed a nationally representative sample of Medicare enrollees (age ≥65 at baseline (Round 1: 2011-2012 and 1-year follow-up (Round 2: 2012-2013. Participants: In all, 6,056 community-dwelling individuals participated in Rounds 1 and 2 of NHATS. Measurements: Primary outcomes were 1-year incidence of “ any fall ” and “ recurrent falls .” Prediction models were compared and validated in development and validation sets, respectively. Results: A prediction model that included demographic information, self-reported problems with balance and coordination, and previous fall history was the most parsimonious model that optimized AUC for both any fall (AUC = 0.69, 95% confidence interval [CI] = [0.67, 0.71] and recurrent falls (AUC = 0.77, 95% CI = [0.74, 0.79] in the development set. Physical performance testing provided a marginal additional predictive value. Conclusion: A simple clinical prediction model that does not include physical performance testing could facilitate routine, widespread falls risk screening in the ambulatory care setting.
A Grey NGM(1,1, k) Self-Memory Coupling Prediction Model for Energy Consumption Prediction

Science.gov (United States)

Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling

2014-01-01

Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1, k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1, k) model. The traditional grey model's weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1, k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span. PMID:25054174
Prediction of PWSCC in nickel base alloys using crack growth rate models

International Nuclear Information System (INIS)

Thompson, C.D.

1995-01-01

The Ford/Andresen slip dissolution SCC model, originally developed for stainless steel components in BWR environments, has been applied to Alloy 600 and Alloy X-750 tested in deaerated pure water chemistry. A method is described whereby the crack growth rates measured in compact tension specimens can be used to estimate crack growth in a component. Good agreement was found between model prediction and measured SCC in X-750 threaded fasteners over a wide range of temperatures, stresses, and material condition. Most data support the basic assumption of this model that cracks initiate early in life. The evidence supporting a particular SCC mechanism is mixed. Electrochemical repassivation data and estimates of oxide fracture strain indicate that the slip dissolution model can account for the observed crack growth rates, provided primary rather than secondary creep rates are used. However, approximately 100 cross-sectional TEM foils of SCC cracks including crack tips reveal no evidence of enhanced plasticity or unique dislocation patterns at the crack tip or along the crack to support a classic slip dissolution mechanism. No voids, hydrides,, or microcracks are found in the vicinity of the crack tips creating doubt about classic hydrogen related mechanisms. The bulk oxide films exhibit a surface oxide which is often different than the oxide found within a crack. Although bulk chromium concentration affects the rate of SCC, analytical data indicates the mechanism does not result from chromium depletion at the grain boundaries. The overall findings support a corrosion/dissolution mechanism but not one necessarily related to slip at the crack tip. (author). 12 refs, 27 figs
Bayesian Predictive Models for Rayleigh Wind Speed

DEFF Research Database (Denmark)

Shahirinia, Amir; Hajizadeh, Amin; Yu, David C

2017-01-01

predictive model of the wind speed aggregates the non-homogeneous distributions into a single continuous distribution. Therefore, the result is able to capture the variation among the probability distributions of the wind speeds at the turbines’ locations in a wind farm. More specifically, instead of using...... a wind speed distribution whose parameters are known or estimated, the parameters are considered as random whose variations are according to probability distributions. The Bayesian predictive model for a Rayleigh which only has a single model scale parameter has been proposed. Also closed-form posterior...... and predictive inferences under different reasonable choices of prior distribution in sensitivity analysis have been presented....
Modeling and Prediction Using Stochastic Differential Equations

DEFF Research Database (Denmark)

Juhl, Rune; Møller, Jan Kloppenborg; Jørgensen, John Bagterp

2016-01-01

Pharmacokinetic/pharmakodynamic (PK/PD) modeling for a single subject is most often performed using nonlinear models based on deterministic ordinary differential equations (ODEs), and the variation between subjects in a population of subjects is described using a population (mixed effects) setup...... deterministic and can predict the future perfectly. A more realistic approach would be to allow for randomness in the model due to e.g., the model be too simple or errors in input. We describe a modeling and prediction setup which better reflects reality and suggests stochastic differential equations (SDEs...
Multi-layer composite mechanical modeling for the inhomogeneous biofilm mechanical behavior.

Science.gov (United States)

Wang, Xiaoling; Han, Jingshi; Li, Kui; Wang, Guoqing; Hao, Mudong

2016-08-01

Experiments showed that bacterial biofilms are heterogeneous, for example, the density, the diffusion coefficient, and mechanical properties of the biofilm are different along the biofilm thickness. In this paper, we establish a multi-layer composite model to describe the biofilm mechanical inhomogeneity based on unified multiple-component cellular automaton (UMCCA) model. By using our model, we develop finite element simulation procedure for biofilm tension experiment. The failure limit and biofilm extension displacement obtained from our model agree well with experimental measurements. This method provides an alternative theory to study the mechanical inhomogeneity in biological materials.

Prediction of hourly solar radiation with multi-model framework

International Nuclear Information System (INIS)

Wu, Ji; Chan, Chee Keong

2013-01-01

Highlights: • A novel approach to predict solar radiation through the use of clustering paradigms. • Development of prediction models based on the intrinsic pattern observed in each cluster. • Prediction based on proper clustering and selection of model on current time provides better results than other methods. • Experiments were conducted on actual solar radiation data obtained from a weather station in Singapore. - Abstract: In this paper, a novel multi-model prediction framework for prediction of solar radiation is proposed. The framework started with the assumption that there are several patterns embedded in the solar radiation series. To extract the underlying pattern, the solar radiation series is first segmented into smaller subsequences, and the subsequences are further grouped into different clusters. For each cluster, an appropriate prediction model is trained. Hence a procedure for pattern identification is developed to identify the proper pattern that fits the current period. Based on this pattern, the corresponding prediction model is applied to obtain the prediction value. The prediction result of the proposed framework is then compared to other techniques. It is shown that the proposed framework provides superior performance as compared to others
Understanding comorbidity among internalizing problems: Integrating latent structural models of psychopathology and risk mechanisms

Science.gov (United States)

Hankin, Benjamin L.; Snyder, Hannah R.; Gulley, Lauren D.; Schweizer, Tina H.; Bijttebier, Patricia; Nelis, Sabine; Toh, Gim; Vasey, Michael W.

2016-01-01

It is well known that comorbidity is the rule, not the exception, for categorically defined psychiatric disorders, and this is also the case for internalizing disorders of depression and anxiety. This theoretical review paper addresses the ubiquity of comorbidity among internalizing disorders. Our central thesis is that progress in understanding this co-occurrence can be made by employing latent dimensional structural models that organize both psychopathology as well as vulnerabilities and risk mechanisms and by connecting the multiple levels of risk and psychopathology outcomes together. Different vulnerabilities and risk mechanisms are hypothesized to predict different levels of the structural model of psychopathology. We review the present state of knowledge based on concurrent and developmental sequential comorbidity patterns among common discrete psychiatric disorders in youth, and then we advocate for the use of more recent bifactor dimensional models of psychopathology (e.g., p factor, Caspi et al., 2014) that can help to explain the co-occurrence among internalizing symptoms. In support of this relatively novel conceptual perspective, we review six exemplar vulnerabilities and risk mechanisms, including executive function, information processing biases, cognitive vulnerabilities, positive and negative affectivity aspects of temperament, and autonomic dysregulation, along with the developmental occurrence of stressors in different domains, to show how these vulnerabilities can predict the general latent psychopathology factor, a unique latent internalizing dimension, as well as specific symptom syndrome manifestations. PMID:27739389
Revised predictive equations for salt intrusion modelling in estuaries

NARCIS (Netherlands)

Gisen, J.I.A.; Savenije, H.H.G.; Nijzink, R.C.

2015-01-01

For one-dimensional salt intrusion models to be predictive, we need predictive equations to link model parameters to observable hydraulic and geometric variables. The one-dimensional model of Savenije (1993b) made use of predictive equations for the Van der Burgh coefficient $K$ and the dispersion
Preprocedural Prediction Model for Contrast-Induced Nephropathy Patients.

Science.gov (United States)

Yin, Wen-Jun; Yi, Yi-Hu; Guan, Xiao-Feng; Zhou, Ling-Yun; Wang, Jiang-Lin; Li, Dai-Yang; Zuo, Xiao-Cong

2017-02-03

Several models have been developed for prediction of contrast-induced nephropathy (CIN); however, they only contain patients receiving intra-arterial contrast media for coronary angiographic procedures, which represent a small proportion of all contrast procedures. In addition, most of them evaluate radiological interventional procedure-related variables. So it is necessary for us to develop a model for prediction of CIN before radiological procedures among patients administered contrast media. A total of 8800 patients undergoing contrast administration were randomly assigned in a 4:1 ratio to development and validation data sets. CIN was defined as an increase of 25% and/or 0.5 mg/dL in serum creatinine within 72 hours above the baseline value. Preprocedural clinical variables were used to develop the prediction model from the training data set by the machine learning method of random forest, and 5-fold cross-validation was used to evaluate the prediction accuracies of the model. Finally we tested this model in the validation data set. The incidence of CIN was 13.38%. We built a prediction model with 13 preprocedural variables selected from 83 variables. The model obtained an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.907 and gave prediction accuracy of 80.8%, sensitivity of 82.7%, specificity of 78.8%, and Matthews correlation coefficient of 61.5%. For the first time, 3 new factors are included in the model: the decreased sodium concentration, the INR value, and the preprocedural glucose level. The newly established model shows excellent predictive ability of CIN development and thereby provides preventative measures for CIN. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.
Time dependent patient no-show predictive modelling development.

Science.gov (United States)

Huang, Yu-Li; Hanauer, David A

2016-05-09

Purpose - The purpose of this paper is to develop evident-based predictive no-show models considering patients' each past appointment status, a time-dependent component, as an independent predictor to improve predictability. Design/methodology/approach - A ten-year retrospective data set was extracted from a pediatric clinic. It consisted of 7,291 distinct patients who had at least two visits along with their appointment characteristics, patient demographics, and insurance information. Logistic regression was adopted to develop no-show models using two-thirds of the data for training and the remaining data for validation. The no-show threshold was then determined based on minimizing the misclassification of show/no-show assignments. There were a total of 26 predictive model developed based on the number of available past appointments. Simulation was employed to test the effective of each model on costs of patient wait time, physician idle time, and overtime. Findings - The results demonstrated the misclassification rate and the area under the curve of the receiver operating characteristic gradually improved as more appointment history was included until around the 20th predictive model. The overbooking method with no-show predictive models suggested incorporating up to the 16th model and outperformed other overbooking methods by as much as 9.4 per cent in the cost per patient while allowing two additional patients in a clinic day. Research limitations/implications - The challenge now is to actually implement the no-show predictive model systematically to further demonstrate its robustness and simplicity in various scheduling systems. Originality/value - This paper provides examples of how to build the no-show predictive models with time-dependent components to improve the overbooking policy. Accurately identifying scheduled patients' show/no-show status allows clinics to proactively schedule patients to reduce the negative impact of patient no-shows.
Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System: Preprint

Energy Technology Data Exchange (ETDEWEB)

Smith, Kandler A [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Saxon, Aron R [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Keyser, Matthew A [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Lundstrom, Blake R [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Cao, Ziwei [SunPower Corporation; Roc, Albert [SunPower Corp.

2017-08-25

Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System: Preprint Lithium-ion (Li-ion) batteries are being deployed on the electrical grid for a variety of purposes, such as to smooth fluctuations in solar renewable power generation. The lifetime of these batteries will vary depending on their thermal environment and how they are charged and discharged. To optimal utilization of a battery over its lifetime requires characterization of its performance degradation under different storage and cycling conditions. Aging tests were conducted on commercial graphite/nickel-manganese-cobalt (NMC) Li-ion cells. A general lifetime prognostic model framework is applied to model changes in capacity and resistance as the battery degrades. Across 9 aging test conditions from 0oC to 55oC, the model predicts capacity fade with 1.4 percent RMS error and resistance growth with 15 percent RMS error. The model, recast in state variable form with 8 states representing separate fade mechanisms, is used to extrapolate lifetime for example applications of the energy storage system integrated with renewable photovoltaic (PV) power generation.
Neural mechanisms of transient neocortical beta rhythms: Converging evidence from humans, computational modeling, monkeys, and mice

Science.gov (United States)

Sherman, Maxwell A.; Lee, Shane; Law, Robert; Haegens, Saskia; Thorn, Catherine A.; Hämäläinen, Matti S.; Moore, Christopher I.; Jones, Stephanie R.

2016-01-01

Human neocortical 15–29-Hz beta oscillations are strong predictors of perceptual and motor performance. However, the mechanistic origin of beta in vivo is unknown, hindering understanding of its functional role. Combining human magnetoencephalography (MEG), computational modeling, and laminar recordings in animals, we present a new theory that accounts for the origin of spontaneous neocortical beta. In our MEG data, spontaneous beta activity from somatosensory and frontal cortex emerged as noncontinuous beta events typically lasting drive targeting proximal and distal dendrites of pyramidal neurons, where the defining feature of a beta event was a strong distal drive that lasted one beta period (∼50 ms). This beta mechanism rigorously accounted for the beta event profiles; several other mechanisms did not. The spatial location of synaptic drive in the model to supragranular and infragranular layers was critical to the emergence of beta events and led to the prediction that beta events should be associated with a specific laminar current profile. Laminar recordings in somatosensory neocortex from anesthetized mice and awake monkeys supported these predictions, suggesting this beta mechanism is conserved across species and recording modalities. These findings make several predictions about optimal states for perceptual and motor performance and guide causal interventions to modulate beta for optimal function. PMID:27469163
Physics-based process modeling, reliability prediction, and design guidelines for flip-chip devices

Science.gov (United States)

Michaelides, Stylianos

Flip Chip on Board (FCOB) and Chip-Scale Packages (CSPs) are relatively new technologies that are being increasingly used in the electronic packaging industry. Compared to the more widely used face-up wirebonding and TAB technologies, flip-chips and most CSPs provide the shortest possible leads, lower inductance, higher frequency, better noise control, higher density, greater input/output (I/O), smaller device footprint and lower profile. However, due to the short history and due to the introduction of several new electronic materials, designs, and processing conditions, very limited work has been done to understand the role of material, geometry, and processing parameters on the reliability of flip-chip devices. Also, with the ever-increasing complexity of semiconductor packages and with the continued reduction in time to market, it is too costly to wait until the later stages of design and testing to discover that the reliability is not satisfactory. The objective of the research is to develop integrated process-reliability models that will take into consideration the mechanics of assembly processes to be able to determine the reliability of face-down devices under thermal cycling and long-term temperature dwelling. The models incorporate the time and temperature-dependent constitutive behavior of various materials in the assembly to be able to predict failure modes such as die cracking and solder cracking. In addition, the models account for process-induced defects and macro-micro features of the assembly. Creep-fatigue and continuum-damage mechanics models for the solder interconnects and fracture-mechanics models for the die have been used to determine the reliability of the devices. The results predicted by the models have been successfully validated against experimental data. The validated models have been used to develop qualification and test procedures for implantable medical devices. In addition, the research has helped develop innovative face
Assessment and propagation of mechanical property uncertainties in fatigue life prediction of composite laminates

DEFF Research Database (Denmark)

Castro, Oscar; Branner, Kim; Dimitrov, Nikolay Krasimirov

2018-01-01

amplitude loading cycles. Fatigue life predictions of unidirectional and multi-directional glass/epoxy laminates are carried out to validate the proposed model against experimental data. The probabilistic fatigue behavior of laminates is analyzed under constant amplitude loading conditions as well as under......A probabilistic model for estimating the fatigue life of laminated composite materials considering the uncertainty in their mechanical properties is developed. The uncertainty in the material properties is determined from fatigue coupon tests. Based on this uncertainty, probabilistic constant life...... diagrams are developed which can efficiently estimate probabilistic É›-N curves at any load level and stress ratio. The probabilistic É›-N curve information is used in a reliability analysis for fatigue limit state proposed for estimating the probability of failure of composite laminates under variable...
Predicting the mechanical properties of brittle porous materials with various porosity and pore sizes.

Science.gov (United States)

Cui, Zhiwei; Huang, Yongmin; Liu, Honglai

2017-07-01

In this work, a micromechanical study using the lattice spring model (LSM) was performed to predict the mechanical properties of BPMs by simulation of the Brazilian test. Stress-strain curve and Weibull plot were analyzed for the determination of fracture strength and Weibull modulus. The presented model composed of linear elastic elements is capable of reproducing the non-linear behavior of BPMs resulting from the damage accumulation and provides consistent results which are in agreement with experimental measurements. Besides, it is also found that porosity shows significant impact on fracture strength while pore size dominates the Weibull modulus, which enables us to establish how choices made in the microstructure to meet the demand of brittle porous materials functioning in various operating conditions. Copyright © 2017 Elsevier Ltd. All rights reserved.
Prediction-Based Energy Saving Mechanism in 3GPP NB-IoT Networks.

Science.gov (United States)

Lee, Jinseong; Lee, Jaiyong

2017-09-01

The current expansion of the Internet of things (IoT) demands improved communication platforms that support a wide area with low energy consumption. The 3rd Generation Partnership Project introduced narrowband IoT (NB-IoT) as IoT communication solutions. NB-IoT devices should be available for over 10 years without requiring a battery replacement. Thus, a low energy consumption is essential for the successful deployment of this technology. Given that a high amount of energy is consumed for radio transmission by the power amplifier, reducing the uplink transmission time is key to ensure a long lifespan of an IoT device. In this paper, we propose a prediction-based energy saving mechanism (PBESM) that is focused on enhanced uplink transmission. The mechanism consists of two parts: first, the network architecture that predicts the uplink packet occurrence through a deep packet inspection; second, an algorithm that predicts the processing delay and pre-assigns radio resources to enhance the scheduling request procedure. In this way, our mechanism reduces the number of random accesses and the energy consumed by radio transmission. Simulation results showed that the energy consumption using the proposed PBESM is reduced by up to 34% in comparison with that in the conventional NB-IoT method.
Model predictive control using fuzzy decision functions

NARCIS (Netherlands)

Kaymak, U.; Costa Sousa, da J.M.

2001-01-01

Fuzzy predictive control integrates conventional model predictive control with techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the
Thermo-mechanical Modelling of Pebble Beds in Fusion Blankets and its Implementation by a Return-Mapping Algorithm

International Nuclear Information System (INIS)

Gan, Yixiang; Kamlah, Marc

2008-01-01

In this investigation, a thermo-mechanical model of pebble beds is adopted and developed based on experiments by Dr. Reimann at Forschungszentrum Karlsruhe (FZK). The framework of the present material model is composed of a non-linear elastic law, the Drucker-Prager-Cap theory, and a modified creep law. Furthermore, the volumetric inelastic strain dependent thermal conductivity of beryllium pebble beds is taken into account and full thermo-mechanical coupling is considered. Investigation showed that the Drucker-Prager-Cap model implemented in ABAQUS can not fulfill the requirements of both the prediction of large creep strains and the hardening behaviour caused by creep, which are of importance with respect to the application of pebble beds in fusion blankets. Therefore, UMAT (user defined material's mechanical behaviour) and UMATHT (user defined material's thermal behaviour) routines are used to re-implement the present thermo-mechanical model in ABAQUS. An elastic predictor radial return mapping algorithm is used to solve the non-associated plasticity iteratively, and a proper tangent stiffness matrix is obtained for cost-efficiency in the calculation. An explicit creep mechanism is adopted for the prediction of time-dependent behaviour in order to represent large creep strains in high temperature. Finally, the thermo-mechanical interactions are implemented in a UMATHT routine for the coupled analysis. The oedometric compression tests and creep tests of pebble beds at different temperatures are simulated with the help of the present UMAT and UMATHT routines, and the comparison between the simulation and the experiments is made. (authors)
Mechanical Properties of Nanoporous Au: From Empirical Evidence to Phenomenological Modeling

Directory of Open Access Journals (Sweden)

Giorgio Pia

2015-09-01

Full Text Available The present work focuses on the development of a theoretical model aimed at relating the mechanical properties of nanoporous metals to the bending response of thick ligaments. The model describes the structure of nanoporous metal foams in terms of an idealized regular lattice of massive cubic nodes and thick ligaments with square cross-sections. Following a general introduction to the subject, model predictions are compared with Young’s modulus and the yield strength of nanoporous Au foams determined experimentally and available in literature. It is shown that the model provides a quantitative description of the elastic and plastic deformation behavior of nanoporous metals, reproducing to a satisfactory extent the experimental Young’s modulus and yield strength values of nanoporous Au.
Predicting and Modelling of Survival Data when Cox's Regression Model does not hold

DEFF Research Database (Denmark)

Scheike, Thomas H.; Zhang, Mei-Jie

2002-01-01

Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects...
Evaluating the Predictive Value of Growth Prediction Models

Science.gov (United States)

Murphy, Daniel L.; Gaertner, Matthew N.

2014-01-01

This study evaluates four growth prediction models--projection, student growth percentile, trajectory, and transition table--commonly used to forecast (and give schools credit for) middle school students' future proficiency. Analyses focused on vertically scaled summative mathematics assessments, and two performance standards conditions (high…
Developing descriptors to predict mechanical properties of nanotubes.

Science.gov (United States)

Borders, Tammie L; Fonseca, Alexandre F; Zhang, Hengji; Cho, Kyeongjae; Rusinko, Andrew

2013-04-22

Descriptors and quantitative structure property relationships (QSPR) were investigated for mechanical property prediction of carbon nanotubes (CNTs). 78 molecular dynamics (MD) simulations were carried out, and 20 descriptors were calculated to build quantitative structure property relationships (QSPRs) for Young's modulus and Poisson's ratio in two separate analyses: vacancy only and vacancy plus methyl functionalization. In the first analysis, C(N2)/C(T) (number of non-sp2 hybridized carbons per the total carbons) and chiral angle were identified as critical descriptors for both Young's modulus and Poisson's ratio. Further analysis and literature findings indicate the effect of chiral angle is negligible at larger CNT radii for both properties. Raman spectroscopy can be used to measure C(N2)/C(T), providing a direct link between experimental and computational results. Poisson's ratio approaches two different limiting values as CNT radii increases: 0.23-0.25 for chiral and armchair CNTs and 0.10 for zigzag CNTs (surface defects <3%). In the second analysis, the critical descriptors were C(N2)/C(T), chiral angle, and M(N)/C(T) (number of methyl groups per total carbons). These results imply new types of defects can be represented as a new descriptor in QSPR models. Finally, results are qualified and quantified against experimental data.
Investigation of membrane mechanics using spring networks: application to red-blood-cell modelling.

Science.gov (United States)

Chen, Mingzhu; Boyle, Fergal J

2014-10-01

In recent years a number of red-blood-cell (RBC) models have been proposed using spring networks to represent the RBC membrane. Some results predicted by these models agree well with experimental measurements. However, the suitability of these membrane models has been questioned. The RBC membrane, like a continuum membrane, is mechanically isotropic throughout its surface, but the mechanical properties of a spring network vary on the network surface and change with deformation. In this work spring-network mechanics are investigated in large deformation for the first time via an assessment of the effect of network parameters, i.e. network mesh, spring type and surface constraint. It is found that a spring network is conditionally equivalent to a continuum membrane. In addition, spring networks are employed for RBC modelling to replicate the optical tweezers test. It is found that a spring network is sufficient for modelling the RBC membrane but strain-hardening springs are required. Moreover, the deformation profile of a spring network is presented for the first time via the degree of shear. It is found that spring-network deformation approaches continuous as the mesh density increases. Copyright © 2014 Elsevier B.V. All rights reserved.
Search for morphological parameters influential for prediction of the mechanical characteristics of an austeno-ferritic duplex stainless steel

International Nuclear Information System (INIS)

Messiaen, L.

1997-01-01

Duplex stainless steels are commonly used (among others in nuclear industry) for their good properties. However these steels may 'age' in service condition at high temperatures. As their mechanical properties (Charpy impact toughness, resistance to ductile tearing) are often very scattered and tend to decrease after ageing, it has become essential to predict them with high precision. For this, we propose to explain a part of the scattering of the mechanical properties with measurable parameters in relation with the particularly complicated two-phase morphology. The two-phase and bi-percolated morphology of the ferrite and austenite phases is first characterised from the observation of 2D images and from the reconstitution of a 3D image. At the same time we precise the genesis of the formation's mechanisms of the structure (germination and growth of the austenitic phase in the solidified ferri tic one) in relation with the literature. The morphological characteristics so observed corresponding with classical notions of mathematical morphology, - size, covariance, connexity -, we use morphological operators to measure morphological variables by image analysis. We establish then a link between toughness and a parameter measuring fineness of the morphology. The lack of data for very aged steels prevent us from proposing a model of toughness which could take this parameter into consideration at these ageing states, for which it is properly the more crucial to obtain specially precise predictions. A mathematical mo del of the 3D structure of the steel is finally proposed. We choose an homogeneous Markov chain of 3D spatial processes, whose evolution in time mimes the solidification. The morphology of the microstructure is so summarised with 8 parameters. This model is liable to be coupled with a model of toughness, for which it would so enlarge the possibilities of prediction. It could also be used to simulate subsequently the damage and the rupture of two
Uncertainties in model-based outcome predictions for treatment planning

International Nuclear Information System (INIS)

Deasy, Joseph O.; Chao, K.S. Clifford; Markman, Jerry

2001-01-01

Purpose: Model-based treatment-plan-specific outcome predictions (such as normal tissue complication probability [NTCP] or the relative reduction in salivary function) are typically presented without reference to underlying uncertainties. We provide a method to assess the reliability of treatment-plan-specific dose-volume outcome model predictions. Methods and Materials: A practical method is proposed for evaluating model prediction based on the original input data together with bootstrap-based estimates of parameter uncertainties. The general framework is applicable to continuous variable predictions (e.g., prediction of long-term salivary function) and dichotomous variable predictions (e.g., tumor control probability [TCP] or NTCP). Using bootstrap resampling, a histogram of the likelihood of alternative parameter values is generated. For a given patient and treatment plan we generate a histogram of alternative model results by computing the model predicted outcome for each parameter set in the bootstrap list. Residual uncertainty ('noise') is accounted for by adding a random component to the computed outcome values. The residual noise distribution is estimated from the original fit between model predictions and patient data. Results: The method is demonstrated using a continuous-endpoint model to predict long-term salivary function for head-and-neck cancer patients. Histograms represent the probabilities for the level of posttreatment salivary function based on the input clinical data, the salivary function model, and the three-dimensional dose distribution. For some patients there is significant uncertainty in the prediction of xerostomia, whereas for other patients the predictions are expected to be more reliable. In contrast, TCP and NTCP endpoints are dichotomous, and parameter uncertainties should be folded directly into the estimated probabilities, thereby improving the accuracy of the estimates. Using bootstrap parameter estimates, competing treatment

Sequence-Specific Model for Peptide Retention Time Prediction in Strong Cation Exchange Chromatography.

Science.gov (United States)

Gussakovsky, Daniel; Neustaeter, Haley; Spicer, Victor; Krokhin, Oleg V

2017-11-07

The development of a peptide retention prediction model for strong cation exchange (SCX) separation on a Polysulfoethyl A column is reported. Off-line 2D LC-MS/MS analysis (SCX-RPLC) of S. cerevisiae whole cell lysate was used to generate a retention dataset of ∼30 000 peptides, sufficient for identifying the major sequence-specific features of peptide retention mechanisms in SCX. In contrast to RPLC/hydrophilic interaction liquid chromatography (HILIC) separation modes, where retention is driven by hydrophobic/hydrophilic contributions of all individual residues, SCX interactions depend mainly on peptide charge (number of basic residues at acidic pH) and size. An additive model (incorporating the contributions of all 20 residues into the peptide retention) combined with a peptide length correction produces a 0.976 R 2 value prediction accuracy, significantly higher than the additive models for either HILIC or RPLC. Position-dependent effects on peptide retention for different residues were driven by the spatial orientation of tryptic peptides upon interaction with the negatively charged surface functional groups. The positively charged N-termini serve as a primary point of interaction. For example, basic residues (Arg, His, Lys) increase peptide retention when located closer to the N-terminus. We also found that hydrophobic interactions, which could lead to a mixed-mode separation mechanism, are largely suppressed at 20-30% of acetonitrile in the eluent. The accuracy of the final Sequence-Specific Retention Calculator (SSRCalc) SCX model (∼0.99 R 2 value) exceeds all previously reported predictors for peptide LC separations. This also provides a solid platform for method development in 2D LC-MS protocols in proteomics and peptide retention prediction filtering of false positive identifications.
A computational model that predicts behavioral sensitivity to intracortical microstimulation

Science.gov (United States)

Kim, Sungshin; Callier, Thierri; Bensmaia, Sliman J.

2017-02-01

Objective. Intracortical microstimulation (ICMS) is a powerful tool to investigate the neural mechanisms of perception and can be used to restore sensation for patients who have lost it. While sensitivity to ICMS has previously been characterized, no systematic framework has been developed to summarize the detectability of individual ICMS pulse trains or the discriminability of pairs of pulse trains. Approach. We develop a simple simulation that describes the responses of a population of neurons to a train of electrical pulses delivered through a microelectrode. We then perform an ideal observer analysis on the simulated population responses to predict the behavioral performance of non-human primates in ICMS detection and discrimination tasks. Main results. Our computational model can predict behavioral performance across a wide range of stimulation conditions with high accuracy (R 2 = 0.97) and generalizes to novel ICMS pulse trains that were not used to fit its parameters. Furthermore, the model provides a theoretical basis for the finding that amplitude discrimination based on ICMS violates Weber’s law. Significance. The model can be used to characterize the sensitivity to ICMS across the range of perceptible and safe stimulation regimes. As such, it will be a useful tool for both neuroscience and neuroprosthetics.
Diaphragmatic excursion: does it predict successful weaning from mechanical ventilation?

International Nuclear Information System (INIS)

Hayat, A.; Khalil, A.

2017-01-01

To measure the diaphragmatic excursion and its outcome on weani ng from mechanical ventilation. Study Design: Cross-sectional comparative study. Place and Duration of Study: Medical Intensive Care Unit (ICU), Military Hospital (MH), Rawalpindi, Pakistan, from January to December 2014. Methodology: Diaphragmatic excursion (DE) in cm was measured through ultrasound by marking liver and spleen displacement in patients who fulfilled the criteria of removal from ventilatory support. The patients were followed up for 48 hours and classified according to the outcome as successful weaning and weaning failure. Results: Out of 100 cases, 76 patients had a successful weaning while 24 had a failed weaning outcome. At a diaphragmatic excursion of 1.2 cm and more, out of 67 cases, 60 had a successful weaning (89.55%) while 7 cases (10.45%) had a weaning failure. At an excursion of less than 1. 2 cm, 17 out of 33 cases (51.5%) had successful weaning while 16 (48.48%) had weaning failure. At this cut off point (1.2 cm), the sensitivity and specificity for successful weaning were 78.95% and 70.83%, respectively. The positive and negative likelihood ratio (LR) for these values being 2.70 and 0.29, respectively. The positive predictive value was 82.35% and negative predictive value 60.00%. Conclusion: Ultrasonographic measurement of diaphragmatic excursion is a good method for predicting weaning outcome from mechanical ventilation. (author)
Prediction error, ketamine and psychosis: An updated model.

Science.gov (United States)

Corlett, Philip R; Honey, Garry D; Fletcher, Paul C

2016-11-01

In 2007, we proposed an explanation of delusion formation as aberrant prediction error-driven associative learning. Further, we argued that the NMDA receptor antagonist ketamine provided a good model for this process. Subsequently, we validated the model in patients with psychosis, relating aberrant prediction error signals to delusion severity. During the ensuing period, we have developed these ideas, drawing on the simple principle that brains build a model of the world and refine it by minimising prediction errors, as well as using it to guide perceptual inferences. While previously we focused on the prediction error signal per se, an updated view takes into account its precision, as well as the precision of prior expectations. With this expanded perspective, we see several possible routes to psychotic symptoms - which may explain the heterogeneity of psychotic illness, as well as the fact that other drugs, with different pharmacological actions, can produce psychotomimetic effects. In this article, we review the basic principles of this model and highlight specific ways in which prediction errors can be perturbed, in particular considering the reliability and uncertainty of predictions. The expanded model explains hallucinations as perturbations of the uncertainty mediated balance between expectation and prediction error. Here, expectations dominate and create perceptions by suppressing or ignoring actual inputs. Negative symptoms may arise due to poor reliability of predictions in service of action. By mapping from biology to belief and perception, the account proffers new explanations of psychosis. However, challenges remain. We attempt to address some of these concerns and suggest future directions, incorporating other symptoms into the model, building towards better understanding of psychosis. © The Author(s) 2016.
High impact polystyrene (HIPS). Predicting its molecular, morphological and mechanical properties

International Nuclear Information System (INIS)

Luciani, C; Estenoz, D; Morales, G; Meira, G

2004-01-01

A mathematical model that is able to predict the molecular and morphological structure of high impact polystyrene (HIPS) and the Fluidity Index (MFI) is presented. The model is divided into two parts: a) the estimation of the material's molecular and morphological properties, simulating the polymerization process in discontinuous mass based on the recipe and the conditions of synthesis; and b) the prediction of rheological variables (viscosity at low deformation speeds and relaxation time), and of the MFI, based on average molecular and morphological variables. The model also combines with empirical correlations proposed in the literature [1] in order to estimate impact strength (IS). The predictions for a), b) and impact strength were co-validated by independent data and resulted in a good fit (CW)
Reducing usage of the computational resources by event driven approach to model predictive control

Science.gov (United States)

Misik, Stefan; Bradac, Zdenek; Cela, Arben

2017-08-01

This paper deals with a real-time and optimal control of dynamic systems while also considers the constraints which these systems might be subject to. Main objective of this work is to propose a simple modification of the existing Model Predictive Control approach to better suit needs of computational resource-constrained real-time systems. An example using model of a mechanical system is presented and the performance of the proposed method is evaluated in a simulated environment.
Predictive Capability Maturity Model for computational modeling and simulation.

Energy Technology Data Exchange (ETDEWEB)

Oberkampf, William Louis; Trucano, Timothy Guy; Pilch, Martin M.

2007-10-01

The Predictive Capability Maturity Model (PCMM) is a new model that can be used to assess the level of maturity of computational modeling and simulation (M&S) efforts. The development of the model is based on both the authors experience and their analysis of similar investigations in the past. The perspective taken in this report is one of judging the usefulness of a predictive capability that relies on the numerical solution to partial differential equations to better inform and improve decision making. The review of past investigations, such as the Software Engineering Institute's Capability Maturity Model Integration and the National Aeronautics and Space Administration and Department of Defense Technology Readiness Levels, indicates that a more restricted, more interpretable method is needed to assess the maturity of an M&S effort. The PCMM addresses six contributing elements to M&S: (1) representation and geometric fidelity, (2) physics and material model fidelity, (3) code verification, (4) solution verification, (5) model validation, and (6) uncertainty quantification and sensitivity analysis. For each of these elements, attributes are identified that characterize four increasing levels of maturity. Importantly, the PCMM is a structured method for assessing the maturity of an M&S effort that is directed toward an engineering application of interest. The PCMM does not assess whether the M&S effort, the accuracy of the predictions, or the performance of the engineering system satisfies or does not satisfy specified application requirements.
An Extended Assessment of Fluid Flow Models for the Prediction of Two-Dimensional Steady-State Airfoil Aerodynamics

Directory of Open Access Journals (Sweden)

José F. Herbert-Acero

2015-01-01

Full Text Available This work presents the analysis, application, and comparison of thirteen fluid flow models in the prediction of two-dimensional airfoil aerodynamics, considering laminar and turbulent subsonic inflow conditions. Diverse sensitivity analyses of different free parameters (e.g., the domain topology and its discretization, the flow model, and the solution method together with its convergence mechanisms revealed important effects on the simulations’ outcomes. The NACA 4412 airfoil was considered throughout the work and the computational predictions were compared with experiments conducted under a wide range of Reynolds numbers (7e5≤Re≤9e6 and angles-of-attack (-10°≤α≤20°. Improvements both in modeling accuracy and processing time were achieved by considering the RS LP-S and the Transition SST turbulence models, and by considering finite volume-based solution methods with preconditioned systems, respectively. The RS LP-S model provided the best lift force predictions due to the adequate modeling of the micro and macro anisotropic turbulence at the airfoil’s surface and at the nearby flow field, which in turn allowed the adequate prediction of stall conditions. The Transition-SST model provided the best drag force predictions due to adequate modeling of the laminar-to-turbulent flow transition and the surface shear stresses. Conclusions, recommendations, and a comprehensive research agenda are presented based on validated computational results.
Model complexity control for hydrologic prediction

NARCIS (Netherlands)

Schoups, G.; Van de Giesen, N.C.; Savenije, H.H.G.

2008-01-01

A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore
Solid mechanics theory, modeling, and problems

CERN Document Server

Bertram, Albrecht

2015-01-01

This textbook offers an introduction to modeling the mechanical behavior of solids within continuum mechanics and thermodynamics. To illustrate the fundamental principles, the book starts with an overview of the most important models in one dimension. Tensor calculus, which is called for in three-dimensional modeling, is concisely presented in the second part of the book. Once the reader is equipped with these essential mathematical tools, the third part of the book develops the foundations of continuum mechanics right from the beginning. Lastly, the book’s fourth part focuses on modeling the mechanics of materials and in particular elasticity, viscoelasticity and plasticity. Intended as an introductory textbook for students and for professionals interested in self-study, it also features numerous worked-out examples to aid in understanding.
Mechanical signaling coordinates the embryonic heartbeat

Science.gov (United States)

Chiou, Kevin K.; Rocks, Jason W.; Chen, Christina Yingxian; Cho, Sangkyun; Merkus, Koen E.; Rajaratnam, Anjali; Robison, Patrick; Tewari, Manorama; Vogel, Kenneth; Majkut, Stephanie F.; Prosser, Benjamin L.; Discher, Dennis E.; Liu, Andrea J.

2016-01-01

In the beating heart, cardiac myocytes (CMs) contract in a coordinated fashion, generating contractile wave fronts that propagate through the heart with each beat. Coordinating this wave front requires fast and robust signaling mechanisms between CMs. The primary signaling mechanism has long been identified as electrical: gap junctions conduct ions between CMs, triggering membrane depolarization, intracellular calcium release, and actomyosin contraction. In contrast, we propose here that, in the early embryonic heart tube, the signaling mechanism coordinating beats is mechanical rather than electrical. We present a simple biophysical model in which CMs are mechanically excitable inclusions embedded within the extracellular matrix (ECM), modeled as an elastic-fluid biphasic material. Our model predicts strong stiffness dependence in both the heartbeat velocity and strain in isolated hearts, as well as the strain for a hydrogel-cultured CM, in quantitative agreement with recent experiments. We challenge our model with experiments disrupting electrical conduction by perfusing intact adult and embryonic hearts with a gap junction blocker, β-glycyrrhetinic acid (BGA). We find this treatment causes rapid failure in adult hearts but not embryonic hearts—consistent with our hypothesis. Last, our model predicts a minimum matrix stiffness necessary to propagate a mechanically coordinated wave front. The predicted value is in accord with our stiffness measurements at the onset of beating, suggesting that mechanical signaling may initiate the very first heartbeats. PMID:27457951
Predictive Model of Systemic Toxicity (SOT)

Science.gov (United States)

In an effort to ensure chemical safety in light of regulatory advances away from reliance on animal testing, USEPA and L’Oréal have collaborated to develop a quantitative systemic toxicity prediction model. Prediction of human systemic toxicity has proved difficult and remains a ...
Numerical prediction of mechanical properties of Pb-Sn solder alloys containing antimony, bismuth and or silver ternary trace elements

Science.gov (United States)

Gadag, Shiva P.; Patra, Susant

2000-12-01

Solder joint interconnects are mechanical means of structural support for bridging the various electronic components and providing electrical contacts and a thermal path for heat dissipation. The functionality of the electronic device often relies on the structural integrity of the solder. The dimensional stability of solder joints is numerically predicted based on their mechanical properties. Algorithms to model the kinetics of dissolution and subsequent growth of intermetallic from the complete knowledge of a single history of time-temperature-reflow profile, by considering equivalent isothermal time intervals, have been developed. The information for dissolution is derived during the heating cycle of reflow and for the growth process from cooling curve of reflow profile. A simple and quick analysis tool to derive tensile stress-strain maps as a function of the reflow temperature of solder and strain rate has been developed by numerical program. The tensile properties are used in modeling thermal strain, thermal fatigue and to predict the overall fatigue life of solder joints. The numerical analysis of the tensile properties as affected by their composition and rate of testing, has been compiled in this paper. A numerical model using constitutive equation has been developed to evaluate the interfacial fatigue crack growth rate. The model can assess the effect of cooling rate, which depends on the level of strain energy release rate. Increasing cooling rate from normalizing to water-quenching, enhanced the fatigue resistance to interfacial crack growth by up to 50% at low strain energy release rate. The increased cooling rates enhanced the fatigue crack growth resistance by surface roughening at the interface of solder joint. This paper highlights salient features of process modeling. Interfacial intermetallic microstructure is affected by cooling rate and thereby affects the mechanical properties.
Determination of a suitable set of loss models for centrifugal compressor performance prediction

Directory of Open Access Journals (Sweden)

Elkin I. GUTIÉRREZ VELÁSQUEZ

2017-10-01

Full Text Available Performance prediction in preliminary design stages of several turbomachinery components is a critical task in order to bring the design processes of these devices to a successful conclusion. In this paper, a review and analysis of the major loss mechanisms and loss models, used to determine the efficiency of a single stage centrifugal compressor, and a subsequent examination to determine an appropriate loss correlation set for estimating the isentropic efficiency in preliminary design stages of centrifugal compressors, were developed. Several semi-empirical correlations, commonly used to predict the efficiency of centrifugal compressors, were implemented in FORTRAN code and then were compared with experimental results in order to establish a loss correlation set to determine, with good approximation, the isentropic efficiency of single stage compressor. The aim of this study is to provide a suitable loss correlation set for determining the isentropic efficiency of a single stage centrifugal compressor, because, with a large amount of loss mechanisms and correlations available in the literature, it is difficult to ascertain how many and which correlations to employ for the correct prediction of the efficiency in the preliminary stage design of a centrifugal compressor. As a result of this study, a set of correlations composed by nine loss mechanisms for single stage centrifugal compressors, conformed by a rotor and a diffuser, are specified.
Using Pareto points for model identification in predictive toxicology

Science.gov (United States)

2013-01-01

Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology. PMID:23517649
Physical and JIT Model Based Hybrid Modeling Approach for Building Thermal Load Prediction

Science.gov (United States)

Iino, Yutaka; Murai, Masahiko; Murayama, Dai; Motoyama, Ichiro

Energy conservation in building fields is one of the key issues in environmental point of view as well as that of industrial, transportation and residential fields. The half of the total energy consumption in a building is occupied by HVAC (Heating, Ventilating and Air Conditioning) systems. In order to realize energy conservation of HVAC system, a thermal load prediction model for building is required. This paper propose a hybrid modeling approach with physical and Just-in-Time (JIT) model for building thermal load prediction. The proposed method has features and benefits such as, (1) it is applicable to the case in which past operation data for load prediction model learning is poor, (2) it has a self checking function, which always supervises if the data driven load prediction and the physical based one are consistent or not, so it can find if something is wrong in load prediction procedure, (3) it has ability to adjust load prediction in real-time against sudden change of model parameters and environmental conditions. The proposed method is evaluated with real operation data of an existing building, and the improvement of load prediction performance is illustrated.
Coupling of Mechanical Behavior of Lithium Ion Cells to Electrochemical-Thermal (ECT) Models for Battery Crush

Energy Technology Data Exchange (ETDEWEB)

Zhang, Chao; Santhanagopalan, Shriram; Pesaran, Ahmad; Sahraei, Elham; Wierzbicki, Tom

2016-06-14

Vehicle crashes can lead to crushing of the battery, damaging lithium ion battery cells and causing local shorts, heat generation, and thermal runaway. Simulating all the physics and geometries at the same time is challenging and takes a lot of effort; thus, simplifications are needed. We developed a material model for simultaneously modeling the mechanical-electrochemical-thermal behavior, which predicted the electrical short, voltage drop, and thermal runaway behaviors followed by a mechanical abuse-induced short. The effect of short resistance on the battery cell performance was studied.
Neural Network Models of Simple Mechanical Systems Illustrating the Feasibility of Accelerated Life Testing

Science.gov (United States)

Fusaro, Robert L.; Jones, Steven P.; Jansen, Ralph

1996-01-01

A complete evaluation of the tribological characteristics of a given material/mechanical system is a time-consuming operation since the friction and wear process is extremely systems sensitive. As a result, experimental designs (i.e., Latin Square, Taguchi) have been implemented in an attempt to not only reduce the total number of experimental combinations needed to fully characterize a material/mechanical system, but also to acquire life data for a system without having to perform an actual life test. Unfortunately, these experimental designs still require a great deal of experimental testing and the output does not always produce meaningful information. In order to further reduce the amount of experimental testing required, this study employs a computer neural network model to investigate different material/mechanical systems. The work focuses on the modeling of the wear behavior, while showing the feasibility of using neural networks to predict life data. The model is capable of defining which input variables will influence the tribological behavior of the particular material/mechanical system being studied based on the specifications of the overall system.
The influence of ligament modelling strategies on the predictive capability of finite element models of the human knee joint.

Science.gov (United States)

Naghibi Beidokhti, Hamid; Janssen, Dennis; van de Groes, Sebastiaan; Hazrati, Javad; Van den Boogaard, Ton; Verdonschot, Nico

2017-12-08

In finite element (FE) models knee ligaments can represented either by a group of one-dimensional springs, or by three-dimensional continuum elements based on segmentations. Continuum models closer approximate the anatomy, and facilitate ligament wrapping, while spring models are computationally less expensive. The mechanical properties of ligaments can be based on literature, or adjusted specifically for the subject. In the current study we investigated the effect of ligament modelling strategy on the predictive capability of FE models of the human knee joint. The effect of literature-based versus specimen-specific optimized material parameters was evaluated. Experiments were performed on three human cadaver knees, which were modelled in FE models with ligaments represented either using springs, or using continuum representations. In spring representation collateral ligaments were each modelled with three and cruciate ligaments with two single-element bundles. Stiffness parameters and pre-strains were optimized based on laxity tests for both approaches. Validation experiments were conducted to evaluate the outcomes of the FE models. Models (both spring and continuum) with subject-specific properties improved the predicted kinematics and contact outcome parameters. Models incorporating literature-based parameters, and particularly the spring models (with the representations implemented in this study), led to relatively high errors in kinematics and contact pressures. Using a continuum modelling approach resulted in more accurate contact outcome variables than the spring representation with two (cruciate ligaments) and three (collateral ligaments) single-element-bundle representations. However, when the prediction of joint kinematics is of main interest, spring ligament models provide a faster option with acceptable outcome. Copyright © 2017 Elsevier Ltd. All rights reserved.
Logistic regression modelling: procedures and pitfalls in developing and interpreting prediction models

Directory of Open Access Journals (Sweden)

Nataša Šarlija

2017-01-01

Full Text Available This study sheds light on the most common issues related to applying logistic regression in prediction models for company growth. The purpose of the paper is 1 to provide a detailed demonstration of the steps in developing a growth prediction model based on logistic regression analysis, 2 to discuss common pitfalls and methodological errors in developing a model, and 3 to provide solutions and possible ways of overcoming these issues. Special attention is devoted to the question of satisfying logistic regression assumptions, selecting and defining dependent and independent variables, using classification tables and ROC curves, for reporting model strength, interpreting odds ratios as effect measures and evaluating performance of the prediction model. Development of a logistic regression model in this paper focuses on a prediction model of company growth. The analysis is based on predominantly financial data from a sample of 1471 small and medium-sized Croatian companies active between 2009 and 2014. The financial data is presented in the form of financial ratios divided into nine main groups depicting following areas of business: liquidity, leverage, activity, profitability, research and development, investing and export. The growth prediction model indicates aspects of a business critical for achieving high growth. In that respect, the contribution of this paper is twofold. First, methodological, in terms of pointing out pitfalls and potential solutions in logistic regression modelling, and secondly, theoretical, in terms of identifying factors responsible for high growth of small and medium-sized companies.

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