Sample records for composite predictive model

  1. Mathematical model predicts the elastic behavior of composite materials

    Directory of Open Access Journals (Sweden)

    Zoroastro de Miranda Boari


    Full Text Available Several studies have found that the non-uniform distribution of reinforcing elements in a composite material can markedly influence its characteristics of elastic and plastic deformation and that a composite's overall response is influenced by the physical and geometrical properties of its reinforcing phases. The finite element method, Eshelby's method and dislocation mechanisms are usually employed in formulating a composite's constitutive response. This paper discusses a composite material containing SiC particles in an aluminum matrix. The purpose of this study was to find the correlation between a composite material's particle distribution and its resistance, and to come up with a mathematical model to predict the material's elastic behavior. The proposed formulation was applied to establish the thermal stress field in the aluminum-SiC composite resulting from its fabrication process, whereby the mixture is prepared at 600 °C and the composite material is used at room temperature. The analytical results, which are presented as stress probabilities, were obtained from the mathematical model proposed herein. These results were compared with the numerical ones obtained by the FEM method. A comparison of the results of the two methods, analytical and numerical, reveals very similar average thermal stress values. It is also shown that Maxwell-Boltzmann's distribution law can be applied to identify the correlation between the material's particle distribution and its resistance, using Eshelby's thermal stresses.

  2. Salt Composition Derived from Veazey Composition by Thermodynamic Modeling and Predicted Composition of Drum Contents

    Energy Technology Data Exchange (ETDEWEB)

    Weisbrod, Kirk Ryan [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Veirs, Douglas Kirk [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Funk, David John [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Clark, David Lewis [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)


    This report describes the derivation of the salt composition from the Veazey salt stream analysis. It also provides an estimate of the proportions of the kitty litter, nitrate salt and neutralizer that was contained in drum 68660. While the actinide content of waste streams was judiciously followed in the 1980s in TA-55, no record of the salt composition could be found. Consequently, a salt waste stream produced from 1992 to 1994 and reported by Gerry Veazey provided the basis for this study. While chemical analysis of the waste stream was highly variable, an average analysis provided input to the Stream Analyzer software to calculate a composition for a concentrated solid nitrate salt and liquid waste stream. The calculation predicted the gas / condensed phase compositions as well as solid salt / saturated liquid compositions. The derived composition provides an estimate of the nitrate feedstream to WIPP for which kinetic measurements can be made. The ratio of salt to Swheat in drum 68660 contents was estimated through an overall mass balance on the parent and sibling drums. The RTR video provided independent confirmation concerning the volume of the mixture. The solid salt layer contains the majority of the salt at a ratio with Swheat that potentially could become exothermic.

  3. Salt Composition Derived from Veazey Composition by Thermodynamic Modeling and Predicted Composition of Drum Contents

    International Nuclear Information System (INIS)

    Weisbrod, Kirk Ryan; Veirs, Douglas Kirk; Funk, David John; Clark, David Lewis


    This report describes the derivation of the salt composition from the Veazey salt stream analysis. It also provides an estimate of the proportions of the kitty litter, nitrate salt and neutralizer that was contained in drum 68660. While the actinide content of waste streams was judiciously followed in the 1980s in TA-55, no record of the salt composition could be found. Consequently, a salt waste stream produced from 1992 to 1994 and reported by Gerry Veazey provided the basis for this study. While chemical analysis of the waste stream was highly variable, an average analysis provided input to the Stream Analyzer software to calculate a composition for a concentrated solid nitrate salt and liquid waste stream. The calculation predicted the gas / condensed phase compositions as well as solid salt / saturated liquid compositions. The derived composition provides an estimate of the nitrate feedstream to WIPP for which kinetic measurements can be made. The ratio of salt to Swheat in drum 68660 contents was estimated through an overall mass balance on the parent and sibling drums. The RTR video provided independent confirmation concerning the volume of the mixture. The solid salt layer contains the majority of the salt at a ratio with Swheat that potentially could become exothermic.

  4. Modelling of nutrient partitioning in growing pigs to predict their anatomical body composition. 2. Model evaluation

    NARCIS (Netherlands)

    Halas, V.; Dijkstra, J.; Babinszky, L.; Verstegen, M.W.A.; Gerrits, W.J.J.


    The objective of the present paper was to evaluate a dynamic mechanistic model for growing and fattening pigs presented in a companion paper. The model predicted the rate of protein and fat deposition (chemical composition), rate of tissue deposition (anatomical composition) and performance of pigs

  5. Modelling of nutrient partitioning in growing pigs to predict their anatomical body composition. 1. Model description

    NARCIS (Netherlands)

    Halas, V.; Dijkstra, J.; Babinszky, L.; Verstegen, M.W.A.; Gerrits, W.J.J.


    A dynamic mechanistic model was developed for growing and fattening pigs. The aim of the model was to predict growth rate and the chemical and anatomical body compositions from the digestible nutrient intake of gilts (20-105 kg live weight). The model represents the partitioning of digestible

  6. A multivariate model for predicting segmental body composition. (United States)

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


    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.

  7. Durability and life prediction modeling in polyimide composites (United States)

    Binienda, Wieslaw K.


    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.

  8. Model of lifetime prediction - Study of the behaviour of polymers and organic matrix composites

    International Nuclear Information System (INIS)

    Colin, X.


    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.)

  9. Predicting Jakarta composite index using hybrid of fuzzy time series and support vector regression models (United States)

    Febrian Umbara, Rian; Tarwidi, Dede; Budi Setiawan, Erwin


    The paper discusses the prediction of Jakarta Composite Index (JCI) in Indonesia Stock Exchange. The study is based on JCI historical data for 1286 days to predict the value of JCI one day ahead. This paper proposes predictions done in two stages., The first stage using Fuzzy Time Series (FTS) to predict values of ten technical indicators, and the second stage using Support Vector Regression (SVR) to predict the value of JCI one day ahead, resulting in a hybrid prediction model FTS-SVR. The performance of this combined prediction model is compared with the performance of the single stage prediction model using SVR only. Ten technical indicators are used as input for each model.

  10. Computerized mathematical model for prediction of resin/fiber composite properties

    International Nuclear Information System (INIS)

    Lowe, K.A.


    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

  11. Miedema model based methodology to predict amorphous-forming-composition range in binary and ternary systems

    Energy Technology Data Exchange (ETDEWEB)

    Das, N., E-mail: [Materials Science Division, Bhabha Atomic Research Centre, Trombay, Mumbai 400 085 (India); Mittra, J. [Materials Science Division, Bhabha Atomic Research Centre, Trombay, Mumbai 400 085 (India); Murty, B.S. [Department of Metallurgical and Materials Engineering, IIT Madras, Chennai 600 036 (India); Pabi, S.K. [Department of Metallurgical and Materials Engineering, IIT Kharagpur, Kharagpur 721 302 (India); Kulkarni, U.D.; Dey, G.K. [Materials Science Division, Bhabha Atomic Research Centre, Trombay, Mumbai 400 085 (India)


    Highlights: Black-Right-Pointing-Pointer A methodology was proposed to predict amorphous forming compositions (AFCs). Black-Right-Pointing-Pointer Chemical contribution to enthalpy of mixing {proportional_to} enthalpy of amorphous for AFCs. Black-Right-Pointing-Pointer Accuracy in the prediction of AFC-range was noticed in Al-Ni-Ti system. Black-Right-Pointing-Pointer Mechanical alloying (MA) results of Al-Ni-Ti followed the predicted AFC-range. Black-Right-Pointing-Pointer Earlier MA results of Al-Ni-Ti also conformed to the predicted AFC-range. - Abstract: From the earlier works on the prediction of amorphous forming composition range (AFCR) using Miedema based model and also, on mechanical alloying experiments it has been observed that all amorphous forming compositions of a given alloy system falls within a linear band when the chemical contribution to enthalpy of the solid solution ({Delta}H{sup ss}) is plotted against the enthalpy of mixing in the amorphous phase ({Delta}H{sup amor}). On the basis of this observation, a methodology has been proposed in this article to identify the AFCR of a ternary system that is likely to be more precise than what can be obtained using {Delta}H{sup amor} - {Delta}H{sup ss} < 0 criterion. MA experiments on various compositions of Al-Ni-Ti system, producing amorphous, crystalline, and mixture of amorphous plus crystalline phases have been carried out and the phases have been characterized using X-ray diffraction and transmission electron microscopy techniques. Data from the present MA experiments and, also, from the literature have been used to validate the proposed approach. Also, the proximity of compositions, producing a mixture of amorphous and crystalline phases to the boundary of AFCR in the Al-Ni-Ti ternary has been found useful to validate the effectiveness of the prediction.

  12. Fatigue life prediction in composites

    CSIR Research Space (South Africa)

    Huston, RJ


    Full Text Available Because of the relatively large number of possible failure mechanisms in fibre reinforced composite materials, the prediction of fatigue life in a component is not a simple process. Several mathematical and statistical models have been proposed...

  13. Micromechanics model for predicting anisotropic electrical conductivity of carbon fiber composite materials (United States)

    Haider, Mohammad Faisal; Haider, Md. Mushfique; Yasmeen, Farzana


    Heterogeneous materials, such as composites consist of clearly distinguishable constituents (or phases) that show different electrical properties. Multifunctional composites have anisotropic electrical properties that can be tailored for a particular application. The effective anisotropic electrical conductivity of composites is strongly affected by many parameters including volume fractions, distributions, and orientations of constituents. Given the electrical properties of the constituents, one important goal of micromechanics of materials consists of predicting electrical response of the heterogeneous material on the basis of the geometries and properties of the individual phases, a task known as homogenization. The benefit of homogenization is that the behavior of a heterogeneous material can be determined without resorting or testing it. Furthermore, continuum micromechanics can predict the full multi-axial properties and responses of inhomogeneous materials, which are anisotropic in nature. Effective electrical conductivity estimation is performed by using classical micromechanics techniques (composite cylinder assemblage method) that investigates the effect of the fiber/matrix electrical properties and their volume fractions on the micro scale composite response. The composite cylinder assemblage method (CCM) is an analytical theory that is based on the assumption that composites are in a state of periodic structure. The CCM was developed to extend capabilities variable fiber shape/array availability with same volume fraction, interphase analysis, etc. The CCM is a continuum-based micromechanics model that provides closed form expressions for upper level length scales such as macro-scale composite responses in terms of the properties, shapes, orientations and constituent distributions at lower length levels such as the micro-scale.

  14. Modified creep and shrinkage prediction model B3 for serviceability limit state analysis of composite slabs (United States)

    Gholamhoseini, Alireza


    Relatively little research has been reported on the time-dependent in-service behavior of composite concrete slabs with profiled steel decking as permanent formwork and little guidance is available for calculating long-term deflections. The drying shrinkage profile through the thickness of a composite slab is greatly affected by the impermeable steel deck at the slab soffit, and this has only recently been quantified. This paper presents the results of long-term laboratory tests on composite slabs subjected to both drying shrinkage and sustained loads. Based on laboratory measurements, a design model for the shrinkage strain profile through the thickness of a slab is proposed. The design model is based on some modifications to an existing creep and shrinkage prediction model B3. In addition, an analytical model is developed to calculate the time-dependent deflection of composite slabs taking into account the time-dependent effects of creep and shrinkage. The calculated deflections are shown to be in good agreement with the experimental measurements.

  15. Developing a predictive model for the chemical composition of soot nanoparticles

    Energy Technology Data Exchange (ETDEWEB)

    Violi, Angela [Univ. of Michigan, Ann Arbor, MI (United States); Michelsen, Hope [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Hansen, Nils [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Wilson, Kevin [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)


    In order to provide the scientific foundation to enable technology breakthroughs in transportation fuel, it is important to develop a combustion modeling capability to optimize the operation and design of evolving fuels in advanced engines for transportation applications. The goal of this proposal is to develop a validated predictive model to describe the chemical composition of soot nanoparticles in premixed and diffusion flames. Atomistic studies in conjunction with state-of-the-art experiments are the distinguishing characteristics of this unique interdisciplinary effort. The modeling effort has been conducted at the University of Michigan by Prof. A. Violi. The experimental work has entailed a series of studies using different techniques to analyze gas-phase soot precursor chemistry and soot particle production in premixed and diffusion flames. Measurements have provided spatial distributions of polycyclic aromatic hydrocarbons and other gas-phase species and size and composition of incipient soot nanoparticles for comparison with model results. The experimental team includes Dr. N. Hansen and H. Michelsen at Sandia National Labs' Combustion Research Facility, and Dr. K. Wilson as collaborator at Lawrence Berkeley National Lab's Advanced Light Source. Our results show that the chemical and physical properties of nanoparticles affect the coagulation behavior in soot formation, and our results on an experimentally validated, predictive model for the chemical composition of soot nanoparticles will not only enhance our understanding of soot formation since but will also allow the prediction of particle size distributions under combustion conditions. These results provide a novel description of soot formation based on physical and chemical properties of the particles for use in the next generation of soot models and an enhanced capability for facilitating the design of alternative fuels and the engines they will power.

  16. Empirical Model Development for Predicting Shock Response on Composite Materials Subjected to Pyroshock Loading (United States)

    Gentz, Steven J.; Ordway, David O; Parsons, David S.; Garrison, Craig M.; Rodgers, C. Steven; Collins, Brian W.


    The NASA Engineering and Safety Center (NESC) received a request to develop an analysis model based on both frequency response and wave propagation analyses for predicting shock response spectrum (SRS) on composite materials subjected to pyroshock loading. The model would account for near-field environment (approx. 9 inches from the source) dominated by direct wave propagation, mid-field environment (approx. 2 feet from the source) characterized by wave propagation and structural resonances, and far-field environment dominated by lower frequency bending waves in the structure. This report documents the outcome of the assessment.

  17. Composition-Based Prediction of Temperature-Dependent Thermophysical Food Properties: Reevaluating Component Groups and Prediction Models. (United States)

    Phinney, David Martin; Frelka, John C; Heldman, Dennis Ray


    Prediction of temperature-dependent thermophysical properties (thermal conductivity, density, specific heat, and thermal diffusivity) is an important component of process design for food manufacturing. Current models for prediction of thermophysical properties of foods are based on the composition, specifically, fat, carbohydrate, protein, fiber, water, and ash contents, all of which change with temperature. The objectives of this investigation were to reevaluate and improve the prediction expressions for thermophysical properties. Previously published data were analyzed over the temperature range from 10 to 150 °C. These data were analyzed to create a series of relationships between the thermophysical properties and temperature for each food component, as well as to identify the dependence of the thermophysical properties on more specific structural properties of the fats, carbohydrates, and proteins. Results from this investigation revealed that the relationships between the thermophysical properties of the major constituents of foods and temperature can be statistically described by linear expressions, in contrast to the current polynomial models. Links between variability in thermophysical properties and structural properties were observed. Relationships for several thermophysical properties based on more specific constituents have been identified. Distinctions between simple sugars (fructose, glucose, and lactose) and complex carbohydrates (starch, pectin, and cellulose) have been proposed. The relationships between the thermophysical properties and proteins revealed a potential correlation with the molecular weight of the protein. The significance of relating variability in constituent thermophysical properties with structural properties--such as molecular mass--could significantly improve composition-based prediction models and, consequently, the effectiveness of process design. © 2016 Institute of Food Technologists®.

  18. Kinetic model for predicting the composition of chlorinated water discharged from power plant cooling systems

    International Nuclear Information System (INIS)

    Lietzke, M.H.


    A kinetic model for predicting the composition of chlorinated water discharged from power plant cooling systems has been developed. The model incorporates the most important chemical reactions that are known to occur when chlorine is added to natural fresh waters. The simultaneous differential equations, which describe the rates of these chemical reactions, are solved numerically to give the composition of the water as a function of time. A listing of the computer program is included, along with a description of the input variables. A worked-out example illustrates the application of the program to an actual cooling system. An appendix contains a compilation of the known equilibrium and kinetic data for many of the chemical reactions that might be encountered in chlorinating natural fresh waters

  19. Predictive Model to determine the composition of the gas generated in a downdraft gasifier

    International Nuclear Information System (INIS)

    D'Espaux Shelton, Elbis; Copa Rey, José Ramón; Brito Sauvanel, Angel Luis


    There is currently a trend of using gasification modeling to describe the process without the need to develop experiments, which can be costly. This work presented the necessary tools to analyze the development of a mathematical model with the objective of predicting the chemical composition of the gas generated in a fixed bed downdraft gasifier, with parallel flows and air as a gasification agent as a function of kind of biomass used and the operating parameters of the equipment. This model allows the calculation of thermochemical processes that occur inside a downdraft gasifier and also the determination of temperature profiles. The model developed was based on the energy balance and species equations approach and the control volumes method was used. (author)

  20. Theoretical models to predict the mechanical behavior of thick composite tubes

    Directory of Open Access Journals (Sweden)

    Volnei Tita


    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.

  1. A Progressive Damage Model for Predicting Permanent Indentation and Impact Damage in Composite Laminates (United States)

    Ji, Zhaojie; Guan, Zhidong; Li, Zengshan


    In this paper, a progressive damage model was established on the basis of ABAQUS software for predicting permanent indentation and impact damage in composite laminates. Intralaminar and interlaminar damage was modelled based on the continuum damage mechanics (CDM) in the finite element model. For the verification of the model, low-velocity impact tests of quasi-isotropic laminates with material system of T300/5228A were conducted. Permanent indentation and impact damage of the laminates were simulated and the numerical results agree well with the experiments. It can be concluded that an obvious knee point can be identified on the curve of the indentation depth versus impact energy. Matrix cracking and delamination develops rapidly with the increasing impact energy, while considerable amount of fiber breakage only occurs when the impact energy exceeds the energy corresponding to the knee point. Predicted indentation depth after the knee point is very sensitive to the parameter μ which is proposed in this paper, and the acceptable value of this parameter is in range from 0.9 to 1.0.

  2. Prediction of aged red wine aroma properties from aroma chemical composition. Partial least squares regression models. (United States)

    Aznar, Margarita; López, Ricardo; Cacho, Juan; Ferreira, Vicente


    Partial least squares regression (PLSR) models able to predict some of the wine aroma nuances from its chemical composition have been developed. The aromatic sensory characteristics of 57 Spanish aged red wines were determined by 51 experts from the wine industry. The individual descriptions given by the experts were recorded, and the frequency with which a sensory term was used to define a given wine was taken as a measurement of its intensity. The aromatic chemical composition of the wines was determined by already published gas chromatography (GC)-flame ionization detector and GC-mass spectrometry methods. In the whole, 69 odorants were analyzed. Both matrixes, the sensory and chemical data, were simplified by grouping and rearranging correlated sensory terms or chemical compounds and by the exclusion of secondary aroma terms or of weak aroma chemicals. Finally, models were developed for 18 sensory terms and 27 chemicals or groups of chemicals. Satisfactory models, explaining more than 45% of the original variance, could be found for nine of the most important sensory terms (wood-vanillin-cinnamon, animal-leather-phenolic, toasted-coffee, old wood-reduction, vegetal-pepper, raisin-flowery, sweet-candy-cacao, fruity, and berry fruit). For this set of terms, the correlation coefficients between the measured and predicted Y (determined by cross-validation) ranged from 0.62 to 0.81. Models confirmed the existence of complex multivariate relationships between chemicals and odors. In general, pleasant descriptors were positively correlated to chemicals with pleasant aroma, such as vanillin, beta damascenone, or (E)-beta-methyl-gamma-octalactone, and negatively correlated to compounds showing less favorable odor properties, such as 4-ethyl and vinyl phenols, 3-(methylthio)-1-propanol, or phenylacetaldehyde.

  3. Predictive Local Composition Models for Solid/Liquid Equilibrium in n-Alkane Systems: Wilson Equation for Multicomponent Systems

    DEFF Research Database (Denmark)

    Coutinho, João A.P.; Stenby, Erling Halfdan


    The predictive local composition model is applied to multicomponent hydrocarbon systems with long-chain n-alkanes as solutes. The results show that it can successfully be extended to highorder systems and accurately predict the solid appearance temperature, also known as cloud point, in solutions...

  4. Predictive Models for Different Roughness Parameters During Machining Process of Peek Composites Using Response Surface Methodology

    Directory of Open Access Journals (Sweden)

    Mata-Cabrera Francisco


    Full Text Available Polyetheretherketone (PEEK composite belongs to a group of high performance thermoplastic polymers and is widely used in structural components. To improve the mechanical and tribological properties, short fibers are added as reinforcement to the material. Due to its functional properties and potential applications, it’s impor- tant to investigate the machinability of non-reinforced PEEK (PEEK, PEEK rein- forced with 30% of carbon fibers (PEEK CF30, and reinforced PEEK with 30% glass fibers (PEEK GF30 to determine the optimal conditions for the manufacture of the parts. The present study establishes the relationship between the cutting con- ditions (cutting speed and feed rate and the roughness (Ra , Rt , Rq , Rp , by develop- ing second order mathematical models. The experiments were planned as per full factorial design of experiments and an analysis of variance has been performed to check the adequacy of the models. These state the adequacy of the derived models to obtain predictions for roughness parameters within ranges of parameters that have been investigated during the experiments. The experimental results show that the most influence of the cutting parameters is the feed rate, furthermore, proved that glass fiber reinforcements produce a worse machinability.

  5. LANDIS PRO: a landscape model that predicts forest composition and structure changes at regional scales (United States)

    Wen J. Wang; Hong S. He; Jacob S. Fraser; Frank R. Thompson; Stephen R. Shifley; Martin A. Spetich


    LANDIS PRO predicts forest composition and structure changes incorporating species-, stand-, and landscape-scales processes at regional scales. Species-scale processes include tree growth, establishment, and mortality. Stand-scale processes contain density- and size-related resource competition that regulates self-thinning and seedling establishment. Landscapescale...

  6. A Unit-Cell Model for Predicting the Elastic Constants of 3D Four Directional Cylindrical Braided Composite Shafts (United States)

    Hao, Wenfeng; Liu, Ye; Huang, Xinrong; Liu, Yinghua; Zhu, Jianguo


    In this work, the elastic constants of 3D four directional cylindrical braided composite shafts were predicted using analytical and numerical methods. First, the motion rule of yarn carrier of 3D four directional cylindrical braided composite shafts was analyzed, and the horizontal projection of yarn motion trajectory was obtained. Then, the geometry models of unit-cells with different braiding angles and fiber volume contents were built up, and the meso-scale models of 3D cylindrical braided composite shafts were obtained. Finally, the effects of braiding angles and fiber volume contents on the elastic constants of 3D braided composite shafts were analyzed theoretically and numerically. These results play a crucial role in investigating the mechanical properties of 3D 4-directional braided composites shafts.

  7. Modeling heterogeneous (co)variances from adjacent-SNP groups improves genomic prediction for milk protein composition traits

    DEFF Research Database (Denmark)

    Gebreyesus, Grum; Lund, Mogens Sandø; Buitenhuis, Albert Johannes


    Accurate genomic prediction requires a large reference population, which is problematic for traits that are expensive to measure. Traits related to milk protein composition are not routinely recorded due to costly procedures and are considered to be controlled by a few quantitative trait loci...... of large effect. The amount of variation explained may vary between regions leading to heterogeneous (co)variance patterns across the genome. Genomic prediction models that can efficiently take such heterogeneity of (co)variances into account can result in improved prediction reliability. In this study, we...... developed and implemented novel univariate and bivariate Bayesian prediction models, based on estimates of heterogeneous (co)variances for genome segments (BayesAS). Available data consisted of milk protein composition traits measured on cows and de-regressed proofs of total protein yield derived for bulls...

  8. A Bayesian Performance Prediction Model for Mathematics Education: A Prototypical Approach for Effective Group Composition (United States)

    Bekele, Rahel; McPherson, Maggie


    This research work presents a Bayesian Performance Prediction Model that was created in order to determine the strength of personality traits in predicting the level of mathematics performance of high school students in Addis Ababa. It is an automated tool that can be used to collect information from students for the purpose of effective group…

  9. Artificial neural network model to predict slag viscosity over a broad range of temperatures and slag compositions

    Energy Technology Data Exchange (ETDEWEB)

    Duchesne, Marc A. [Chemical and Biological Engineering Department, University of Ottawa, 161 Louis Pasteur, Ottawa, Ont. (Canada); CanmetENERGY, 1 Haanel Drive, Ottawa, Ontario (Canada); Macchi, Arturo [Chemical and Biological Engineering Department, University of Ottawa, 161 Louis Pasteur, Ottawa, Ont. (Canada); Lu, Dennis Y.; Hughes, Robin W.; McCalden, David; Anthony, Edward J. [CanmetENERGY, 1 Haanel Drive, Ottawa, Ontario (Canada)


    Threshold slag viscosity heuristics are often used for the initial assessment of coal gasification projects. Slag viscosity predictions are also required for advanced combustion and gasification models. Due to unsatisfactory performance of theoretical equations, an artificial neural network model was developed to predict slag viscosity over a broad range of temperatures and slag compositions. This model outperforms other slag viscosity models, resulting in an average error factor of 5.05 which is lower than the best obtained with other available models. Genesee coal ash viscosity predictions were made to investigate the effect of adding Canadian limestone and dolomite. The results indicate that magnesium in the fluxing agent provides a greater viscosity reduction than calcium for the threshold slag tapping temperature range. (author)

  10. Composite control for raymond mill based on model predictive control and disturbance observer

    Directory of Open Access Journals (Sweden)

    Dan Niu


    Full Text Available In the raymond mill grinding process, precise control of operating load is vital for the high product quality. However, strong external disturbances, such as variations of ore size and ore hardness, usually cause great performance degradation. It is not easy to control the current of raymond mill constant. Several control strategies have been proposed. However, most of them (such as proportional–integral–derivative and model predictive control reject disturbances just through feedback regulation, which may lead to poor control performance in the presence of strong disturbances. For improving disturbance rejection, a control method based on model predictive control and disturbance observer is put forward in this article. The scheme employs disturbance observer as feedforward compensation and model predictive control controller as feedback regulation. The test results illustrate that compared with model predictive control method, the proposed disturbance observer–model predictive control method can obtain significant superiority in disturbance rejection, such as shorter settling time and smaller peak overshoot under strong disturbances.

  11. Empirically Based Composite Fracture Prediction Model From the Global Longitudinal Study of Osteoporosis in Postmenopausal Women (GLOW) (United States)

    Compston, Juliet E.; Chapurlat, Roland D.; Pfeilschifter, Johannes; Cooper, Cyrus; Hosmer, David W.; Adachi, Jonathan D.; Anderson, Frederick A.; Díez-Pérez, Adolfo; Greenspan, Susan L.; Netelenbos, J. Coen; Nieves, Jeri W.; Rossini, Maurizio; Watts, Nelson B.; Hooven, Frederick H.; LaCroix, Andrea Z.; March, Lyn; Roux, Christian; Saag, Kenneth G.; Siris, Ethel S.; Silverman, Stuart; Gehlbach, Stephen H.


    Context: Several fracture prediction models that combine fractures at different sites into a composite outcome are in current use. However, to the extent individual fracture sites have differing risk factor profiles, model discrimination is impaired. Objective: The objective of the study was to improve model discrimination by developing a 5-year composite fracture prediction model for fracture sites that display similar risk profiles. Design: This was a prospective, observational cohort study. Setting: The study was conducted at primary care practices in 10 countries. Patients: Women aged 55 years or older participated in the study. Intervention: Self-administered questionnaires collected data on patient characteristics, fracture risk factors, and previous fractures. Main Outcome Measure: The main outcome is time to first clinical fracture of hip, pelvis, upper leg, clavicle, or spine, each of which exhibits a strong association with advanced age. Results: Of four composite fracture models considered, model discrimination (c index) is highest for an age-related fracture model (c index of 0.75, 47 066 women), and lowest for Fracture Risk Assessment Tool (FRAX) major fracture and a 10-site model (c indices of 0.67 and 0.65). The unadjusted increase in fracture risk for an additional 10 years of age ranges from 80% to 180% for the individual bones in the age-associated model. Five other fracture sites not considered for the age-associated model (upper arm/shoulder, rib, wrist, lower leg, and ankle) have age associations for an additional 10 years of age from a 10% decrease to a 60% increase. Conclusions: After examining results for 10 different bone fracture sites, advanced age appeared the single best possibility for uniting several different sites, resulting in an empirically based composite fracture risk model. PMID:24423345

  12. Trait-based representation of biological nitrification: Model development, testing, and predicted community composition

    Directory of Open Access Journals (Sweden)

    Nick eBouskill


    Full Text Available Trait-based microbial models show clear promise as tools to represent the diversity and activity of microorganisms across ecosystem gradients. These models parameterize specific traits that determine the relative fitness of an ‘organism’ in a given environment, and represent the complexity of biological systems across temporal and spatial scales. In this study we introduce a microbial community trait-based modeling framework (MicroTrait focused on nitrification (MicroTrait-N that represents the ammonia-oxidizing bacteria (AOB and ammonia-oxidizing archaea (AOA and nitrite oxidizing bacteria (NOB using traits related to enzyme kinetics and physiological properties. We used this model to predict nitrifier diversity, ammonia (NH3 oxidation rates and nitrous oxide (N2O production across pH, temperature and substrate gradients. Predicted nitrifier diversity was predominantly determined by temperature and substrate availability, the latter was strongly influenced by pH. The model predicted that transient N2O production rates are maximized by a decoupling of the AOB and NOB communities, resulting in an accumulation and detoxification of nitrite to N2O by AOB. However, cumulative N2O production (over six month simulations is maximized in a system where the relationship between AOB and NOB is maintained. When the reactions uncouple, the AOB become unstable and biomass declines rapidly, resulting in decreased NH3 oxidation and N2O production. We evaluated this model against site level chemical datasets from the interior of Alaska and accurately simulated NH3 oxidation rates and the relative ratio of AOA:AOB biomass. The predicted community structure and activity indicate (a parameterization of a small number of traits may be sufficient to broadly characterize nitrifying community structure and (b changing decadal trends in climate and edaphic conditions could impact nitrification rates in ways that are not captured by extant biogeochemical models.

  13. Models for the prediction of the cetane index of biofuels obtained from different vegetable oils using their fatty acid composition

    International Nuclear Information System (INIS)

    Sanchez Borroto, Yisel; Piloto Rodriguez, Ramon; Goyos Perez, Leonardo


    The objective of the present work is to obtain a physical-mathematical model that establishes a relationship between the cetane index of biofuels obtained from different vegetable oils and its composition of essential fatty acid. This model is based on experimental data obtained by the authors of the present work and an experimental data reported by different extracted authors of indexed databases. The adjustment of the coefficients of the model is based on the obtaining of residual minima in the capacity of prediction of the model. Starting from these results it is established a very useful tool for the determination of such an important parameter for the fuel diesel as it is the cetane index obtained from an analysis of chemical composition and not obtained from tests in engines banks, to save time and economic resources. (author)

  14. Prediction models of mechanical properties for pet-mortar composite in sodium sulphateaggressive mediums

    Directory of Open Access Journals (Sweden)

    Kazi Tani Nabil


    Full Text Available In this research, an investigation was carried out on the effect of sodium sulphate attack on the durability of composites produced with waste polyethylene terephthalate (PET. Experiments were accomplished on limestone sand and cement mortars where the blended Portland cement was partially replaced by various volume fractions of waste PET particles (6%, 12% and 17%. The test solutions used to supply the sulphate ions and cations were 5%sodium sulphate solution. Compressive strengths measured on specimens were used to assess the changes in the mechanical properties of PET-mortars exposed to sulphate attack at different ages, mainly the Young modulus of elasticity. Based on experimental compressive tests on PETMortar composite specimens and there densities, the evolution of Young modulus of elasticity has been analyzed in accordance with normative models given by (ACI-318 and (BS-8110 codes of practice. In addition, a comparative study has been carried out for corrosion resistance coefficients K of unmodified mortar to those modified with waste PET particles. It can be noticed that, for the composite immersed in a corrosive Na2SO4 solution, the corrosion resistance coefficients decrease with the increase of the immersion period. The corrosion sulphate resistance K based on Young modulus before and after immersion of PET-mortar composites is better than that of the control mortar. Therefore, for safety considerations of PET-mortar composites use, ACI 318 is recommended code for design and investigation works. Also, it can be concluded that adding waste PET by volume fractions (6%, 12% and 17% to blend Portland cement renders this cement more resistant to the sodium sulphate aggressive medium. Therefore, composites materials based waste PET aare often presented as the materials of the future because of their potential for innovation and the advantages they offer. In fact, using waste PET as cement substitutes reduces the energy consumption. These

  15. Anchorage strength models for end-debonding predictions in RC beams strengthened with FRP composites (United States)

    Nardini, V.; Guadagnini, M.; Valluzzi, M. R.


    The increase in the flexural capacity of RC beams obtained by externally bonding FRP composites to their tension side is often limited by the premature and brittle debonding of the external reinforcement. An in-depth understanding of this complex failure mechanism, however, has not yet been achieved. With specific regard to end-debonding failure modes, extensive experimental observations reported in the literature highlight the important distinction, often neglected in strength models proposed by researchers, between the peel-off and rip-off end-debonding types of failure. The peel-off failure is generally characterized by a failure plane located within the first few millimetres of the concrete cover, whilst the rip-off failure penetrates deeper into the concrete cover and propagates along the tensile steel reinforcement. A new rip-off strength model is described in this paper. The model proposed is based on the Chen and Teng peel-off model and relies upon additional theoretical considerations. The influence of the amount of the internal tensile steel reinforcement and the effective anchorage length of FRP are considered and discussed. The validity of the new model is analyzed further through comparisons with test results, findings of a numerical investigation, and a parametric study. The new rip-off strength model is assessed against a database comprising results from 62 beams tested by various researchers and is shown to yield less conservative results.

  16. Empirical Model Development for Predicting Shock Response on Composite Materials Subjected to Pyroshock Loading. Volume 2, Part 1; Appendices (United States)

    Gentz, Steven J.; Ordway, David O.; Parsons, David S.; Garrison, Craig M.; Rodgers, C. Steven; Collins, Brian W.


    The NASA Engineering and Safety Center (NESC) received a request to develop an analysis model based on both frequency response and wave propagation analyses for predicting shock response spectrum (SRS) on composite materials subjected to pyroshock loading. The model would account for near-field environment (approximately 9 inches from the source) dominated by direct wave propagation, mid-field environment (approximately 2 feet from the source) characterized by wave propagation and structural resonances, and far-field environment dominated by lower frequency bending waves in the structure. This document contains appendices to the Volume I report.

  17. Validation of the kinetic model for predicting the composition of chlorinated water discharged from power plant cooling systems

    International Nuclear Information System (INIS)

    Lietzke, M.H.


    The purpose of this report is to present a validation of a previously described kinetic model which was developed to predict the composition of chlorinated fresh water discharged from power plant cooling systems. The model was programmed in two versions: as a stand-alone program and as a part of a unified transport model developed from consistent mathematical models to simulate the dispersion of heated water and radioisotopic and chemical effluents from power plant discharges. The results of testing the model using analytical data taken during operation of the once-through cooling system of the Quad Cities Nuclear Station are described. Calculations are also presented on the Three Mile Island Nuclear Station which uses cooling towers

  18. Modeling and Predicting the Electrical Conductivity of Composite Cathode for Solid Oxide Fuel Cell by Using Support Vector Regression (United States)

    Tang, J. L.; Cai, C. Z.; Xiao, T. T.; Huang, S. J.


    The electrical conductivity of solid oxide fuel cell (SOFC) cathode is one of the most important indices affecting the efficiency of SOFC. In order to improve the performance of fuel cell system, it is advantageous to have accurate model with which one can predict the electrical conductivity. In this paper, a model utilizing support vector regression (SVR) approach combined with particle swarm optimization (PSO) algorithm for its parameter optimization was established to modeling and predicting the electrical conductivity of Ba0.5Sr0.5Co0.8Fe0.2 O3-δ-xSm0.5Sr0.5CoO3-δ (BSCF-xSSC) composite cathode under two influence factors, including operating temperature (T) and SSC content (x) in BSCF-xSSC composite cathode. The leave-one-out cross validation (LOOCV) test result by SVR strongly supports that the generalization ability of SVR model is high enough. The absolute percentage error (APE) of 27 samples does not exceed 0.05%. The mean absolute percentage error (MAPE) of all 30 samples is only 0.09% and the correlation coefficient (R2) as high as 0.999. This investigation suggests that the hybrid PSO-SVR approach may be not only a promising and practical methodology to simulate the properties of fuel cell system, but also a powerful tool to be used for optimal designing or controlling the operating process of a SOFC system.

  19. Body composition in dialysis patients: a functional assessment of bioimpedance using different prediction models. (United States)

    Broers, Natascha J H; Martens, Remy J H; Cornelis, Tom; Diederen, Nanda M P; Wabel, Peter; van der Sande, Frank M; Leunissen, Karel M L; Kooman, Jeroen P


    The assessment of body composition (BC) in dialysis patients is of clinical importance given its role in the diagnosis of malnutrition and sarcopenia. Bioimpedance techniques routinely express BC as a 2-compartment (2-C) model distinguishing fat mass (FM) and fat-free mass (FFM), which may be influenced by the hydration of adipose tissue and fluid overload (OH). Recently, the BC monitor was introduced which applies a 3-compartment (3-C) model, distinguishing OH, adipose tissue mass, and lean tissue mass. The aim of this study was to compare BC between the 2-C and 3-C models and assess their relation with markers of functional performance (handgrip strength [HGS] and 4-m walking test), as well as with biochemical markers of nutrition. Forty-seven dialysis patients (30 males and 17 females) (35 hemodialysis, 12 peritoneal dialysis) with a mean age of 64.8 ± 16.5 years were studied. 3-C BC was assessed by BC monitor, whereas the obtained resistivity values were used to calculate FM and FFM according to the Xitron Hydra 4200 formulas, which are based on a 2-C model. FFM (3-C) was 0.99 kg (95% confidence interval [CI], 0.27 to 1.71, P = .008) higher than FFM (2-C). FM (3-C) was 2.43 kg (95% CI, 1.70-3.15, P FFM 3-C - FFM 2-C) (r = 0.361; P FFM (2-C) (r = 0.713; P FFM (3-C) (r = 0.711; P FFM (3-C) and FFM (2-C) were significantly related to HGS. Bioimpedance, HGS, and the 4-m walking test may all be valuable tools in the multidimensional nutritional assessment of both hemodialysis and peritoneal dialysis patients. Copyright © 2015 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

  20. Biochemical methane potential prediction of plant biomasses: Comparing chemical composition versus near infrared methods and linear versus non-linear models. (United States)

    Godin, Bruno; Mayer, Frédéric; Agneessens, Richard; Gerin, Patrick; Dardenne, Pierre; Delfosse, Philippe; Delcarte, Jérôme


    The reliability of different models to predict the biochemical methane potential (BMP) of various plant biomasses using a multispecies dataset was compared. The most reliable prediction models of the BMP were those based on the near infrared (NIR) spectrum compared to those based on the chemical composition. The NIR predictions of local (specific regression and non-linear) models were able to estimate quantitatively, rapidly, cheaply and easily the BMP. Such a model could be further used for biomethanation plant management and optimization. The predictions of non-linear models were more reliable compared to those of linear models. The presentation form (green-dried, silage-dried and silage-wet form) of biomasses to the NIR spectrometer did not influence the performances of the NIR prediction models. The accuracy of the BMP method should be improved to enhance further the BMP prediction models. Copyright © 2014 Elsevier Ltd. All rights reserved.

  1. A CFD-Model for prediction of unintended porosities in metal matrix composites

    DEFF Research Database (Denmark)

    Li, Shizhao; Spangenberg, Jon; Hattel, Jesper Henri


    This paper presents a numerical method that simulates the flow through the porous corridors of the preform, which in theory enables the prediction of unintended porosities at the end of the process....

  2. Predicting Alkylate Yield and its Hydrocarbon Composition for Sulfuric Acid Catalyzed Isobutane Alkylation with Olefins Using the Method of Mathematical Modeling


    Nurmakanova, А. Е.; Ivashkina, Elena Nikolaevna; Ivanchina, Emilia Dmitrievna; Dolganov, I. A.; Boychenko, S. S.


    The article provides the results of applied mathematical model of isobutane alkylation with olefins catalyzed by sulfuric acid to predict yield and hydrocarbon composition of alkylate caused by the changes in the feedstock composition and process parameters. It is shown that the alkylate produced from feedstock with less mass fraction of isobutane has lower octane value. Wherein the difference in composition of the feedstock contributes to antiknock index by the amount of 1.0-2.0 points.

  3. Prediction models of mechanical properties for pet-mortar composite in sodium sulphateaggressive mediums


    Kazi Tani Nabil; Benosman A.S.; Senhadji Y.; Taïbi H.; Mouli M.; Belbachir M.


    In this research, an investigation was carried out on the effect of sodium sulphate attack on the durability of composites produced with waste polyethylene terephthalate (PET). Experiments were accomplished on limestone sand and cement mortars where the blended Portland cement was partially replaced by various volume fractions of waste PET particles (6%, 12% and 17%). The test solutions used to supply the sulphate ions and cations were 5%sodium sulphate solution. Compressive strengths measure...

  4. Fatigue life prediction in composites using progressive damage modelling under block and spectrum loading

    DEFF Research Database (Denmark)

    Passipoularidis, Vaggelis; Philippidis, T.P.; Brøndsted, Povl


    series can be simulated. The predictions are validated against fatigue life data both from repeated block tests at a single stress ratio as well as against spectral fatigue using the WISPER, WISPERX and NEW WISPER load sequences on a Glass/Epoxy multidirectional laminate typical of a Wind Turbine Rotor...

  5. Kinetic model for predicting the composition of chlorinated water discharged from power plant cooling systems

    International Nuclear Information System (INIS)

    Lietzke, M.H.


    The results of applying a kinetic model to the chlorination data supplied by Commonwealth Edison on the once-through cooling system at the Quad Cities Nuclear Station provide a validation of the model. The two examples given demonstrate that the model may be applied to either once-through cooling systems or to cooling systems involving cooling towers

  6. Predicting community composition from pairwise interactions (United States)

    Friedman, Jonathan; Higgins, Logan; Gore, Jeff

    The ability to predict the structure of complex, multispecies communities is crucial for understanding the impact of species extinction and invasion on natural communities, as well as for engineering novel, synthetic communities. Communities are often modeled using phenomenological models, such as the classical generalized Lotka-Volterra (gLV) model. While a lot of our intuition comes from such models, their predictive power has rarely been tested experimentally. To directly assess the predictive power of this approach, we constructed synthetic communities comprised of up to 8 soil bacteria. We measured the outcome of competition between all species pairs, and used these measurements to predict the composition of communities composed of more than 2 species. The pairwise competitions resulted in a diverse set of outcomes, including coexistence, exclusion, and bistability, and displayed evidence for both interference and facilitation. Most pair outcomes could be captured by the gLV framework, and the composition of multispecies communities could be predicted for communities composed solely of such pairs. Our results demonstrate the predictive ability and utility of simple phenomenology, which enables accurate predictions in the absence of mechanistic details.

  7. Evaluation of models for prediction of the energy value of diets for growing cattle from the chemical composition of feeds

    Directory of Open Access Journals (Sweden)

    Cláudia Batista Sampaio


    Full Text Available The objective of this study was to estimate and evaluate the contents of apparently digestible fractions of crude protein, ether extract and non-fibrous carbohydrates, the digestible fraction of the neutral detergent fiber and the content of total digestible nutrients (TDN from the chemical composition of feeds in growing cattle fed different diets. Fourteen F1 Red Angus × Nellore young bulls with average age and weight of 12 months and 287±36 kg were used. Animals were fed elephant grass silage, corn silage or signal grass hay, with or without supplementation of 200 g concentrate per kg of the total diet. The experiment consisted of two 13-days periods, in which the concentrate supplementation was crossed over animals. The values of digestible fractions and the TDN content observed were obtained based on total collection of feces. Several sub-models applied to the different digestible fractions were assessed and discussed. Estimates of the TDN content in the diet were produced from the combination of sub-models applied to the individual digestible fractions. The TDN content was more efficiently predicted from the sub-models proposed by Detmann et al. (2010 when biological procedures for the estimation of the undegradable fraction of the protein and the potentially degradable fraction of the neutral detergent fiber were considered.

  8. A numerical approach to model and predict the energy absorption and crush mechanics within a long-fiber composite crush tube (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.

  9. Predicting relative species composition within mixed conifer forest pixels using zero‐inflated models and Landsat imagery (United States)

    Shannon L. Savage; Rick L. Lawrence; John R. Squires


    Ecological and land management applications would often benefit from maps of relative canopy cover of each species present within a pixel, instead of traditional remote-sensing based maps of either dominant species or percent canopy cover without regard to species composition. Widely used statistical models for remote sensing, such as randomForest (RF),...

  10. 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


    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.

  11. Statistical model to predict dry sliding wear behaviour of Aluminium-Jute bast ash particulate composite produced by stir-casting

    Directory of Open Access Journals (Sweden)

    Gambo Anthony VICTOR


    Full Text Available A model to predict the dry sliding wear behaviour of Aluminium-Jute bast ash particulate composites produced by double stir-casting method was developed in terms of weight fraction of jute bast ash (JBA. Experiments were designed on the basis of the Design of Experiments (DOE technique. A 2k factorial, where k is the number of variables, with central composite second-order rotatable design was used to improve the reliability of results and to reduce the size of experimentation without loss of accuracy. The factors considered in this study were sliding velocity, sliding distance, normal load and mass fraction of JBA reinforcement in the matrix. The developed regression model was validated by statistical software MINITAB-R14 and statistical tool such as analysis of variance (ANOVA. It was found that the developed regression model could be effectively used to predict the wear rate at 95% confidence level. The wear rate of cast Al-JBAp composite decreased with an increase in the mass fraction of JBA and increased with an increase of the sliding velocity, sliding distance and normal load acting on the composite specimen.

  12. Surface quality prediction model of nano-composite ceramics in ultrasonic vibration-assisted ELID mirror grinding

    Energy Technology Data Exchange (ETDEWEB)

    Zhao, Bo; Chen, Fan; Jia, Xiao-feng; Zhao, Chong-yang; Wang, Xiao-bo [Henan Polytechnic University, Jiaozuo (China)


    Ultrasonic vibration-assisted Electrolytic in-process dressing (ELID) grinding is a highly efficient and highly precise machining method. The surface quality prediction model in ultrasonic vibration-assisted ELID mirror grinding was studied. First, the interaction between grits and workpiece surface was analyzed according to kinematic mechanics, and the surface roughness model was developed. The variations in surface roughness under different parameters was subsequently calculated and analyzed by MATLAB. Results indicate that compared with the ordinary ELID grinding, ultrasonic vibration-assisted ELID grinding is superior, because it has more stable and better surface quality and has an improved range of ductile machining.

  13. Development of a molecular dynamic based cohesive zone model for prediction of an equivalent material behavior for Al/Al2O3 composite

    Energy Technology Data Exchange (ETDEWEB)

    Sazgar, A. [Center of Excellence in Design, Robotics and Automation, Department of Mechanical Engineering, Sharif University of Technology, Tehran (Iran, Islamic Republic of); Movahhedy, M.R., E-mail: [Center of Excellence in Design, Robotics and Automation, Department of Mechanical Engineering, Sharif University of Technology, Tehran (Iran, Islamic Republic of); Mahnama, M. [School of Mechanical Engineering, University of Tehran, Tehran (Iran, Islamic Republic of); Sohrabpour, S. [Center of Excellence in Design, Robotics and Automation, Department of Mechanical Engineering, Sharif University of Technology, Tehran (Iran, Islamic Republic of)


    The interfacial behavior of composites is often simulated using a cohesive zone model (CZM). In this approach, a traction-separation (T-S) relation between the matrix and reinforcement particles, which is often obtained from experimental results, is employed. However, since the determination of this relation from experimental results is difficult, the molecular dynamics (MD) simulation may be used as a virtual environment to obtain this relation. In this study, MD simulations under the normal and shear loadings are used to obtain the interface behavior of Al/Al2O3 composite material and to derive the T-S relation. For better agreement with Al/Al2O3 interfacial behavior, the exponential form of the T-S relation suggested by Needleman [1] is modified to account for thermal effects. The MD results are employed to develop a parameterized cohesive zone model which is implemented in a finite element model of the matrix-particle interactions. Stress-strain curves obtained from simulations under different loading conditions and volume fractions show a close correlation with experimental results. Finally, by studying the effects of strain rate and volume fraction of particles in Al(6061-T6)/Al2O3 composite, an equivalent homogeneous model is introduced which can predict the overall behavior of the composite.

  14. Predicting the Mineral Composition of Dust Aerosols. Part 2; Model Evaluation and Identification of Key Processes with Observations (United States)

    Perlwitz, J. P.; Garcia-Pando, C. Perez; Miller, R. L.


    A global compilation of nearly sixty measurement studies is used to evaluate two methods of simulating the mineral composition of dust aerosols in an Earth system model. Both methods are based upon a Mean Mineralogical Table (MMT) that relates the soil mineral fractions to a global atlas of arid soil type. The Soil Mineral Fraction (SMF) method assumes that the aerosol mineral fractions match the fractions of the soil. The MMT is based upon soil measurements after wet sieving, a process that destroys aggregates of soil particles that would have been emitted from the original, undisturbed soil. The second method approximately reconstructs the emitted aggregates. This model is referred to as the Aerosol Mineral Fraction (AMF) method because the mineral fractions of the aerosols differ from those of the wet-sieved parent soil, partly due to reaggregation. The AMF method remedies some of the deficiencies of the SMF method in comparison to observations. Only the AMF method exhibits phyllosilicate mass at silt sizes, where they are abundant according to observations. In addition, the AMF quartz fraction of silt particles is in better agreement with measured values, in contrast to the overestimated SMF fraction. Measurements at distinct clay and silt particle sizes are shown to be more useful for evaluation of the models, in contrast to the sum over all particles sizes that is susceptible to compensating errors, as illustrated by the SMF experiment. Model errors suggest that allocation of the emitted silt fraction of each mineral into the corresponding transported size categories is an important remaining source of uncertainty. Evaluation of both models and the MMT is hindered by the limited number of size-resolved measurements of mineral content that sparsely sample aerosols from the major dust sources. The importance of climate processes dependent upon aerosol mineral composition shows the need for global and routine mineral measurements.

  15. Model, prediction, and experimental verification of composition and thickness in continuous spread thin film combinatorial libraries grown by pulsed laser deposition

    International Nuclear Information System (INIS)

    Bassim, N. D.; Schenck, P. K.; Otani, M.; Oguchi, H.


    Pulsed laser deposition was used to grow continuous spread thin film libraries of continuously varying composition as a function of position on a substrate. The thickness of each component that contributes to a library can be empirically modeled to a bimodal cosine power distribution. We deposited ternary continuous spread thin film libraries from Al 2 O 3 , HfO 2 , and Y 2 O 3 targets, at two different background pressures of O 2 : 1.3 and 13.3 Pa. Prior to library deposition, we deposited single component calibration films at both pressures in order to measure and fit the thickness distribution. Following the deposition and fitting of the single component films, we predict both the compositional coverage and the thickness of the libraries. Then, we map the thickness of the continuous spread libraries using spectroscopic reflectometry and measure the composition of the libraries as a function of position using mapping wavelength-dispersive spectrometry (WDS). We then compare the compositional coverage of the libraries and observe that compositional coverage is enhanced in the case of 13.3 Pa library. Our models demonstrate linear correlation coefficients of 0.98 for 1.3 Pa and 0.98 for 13.3 Pa with the WDS

  16. Partially composite Higgs models

    DEFF Research Database (Denmark)

    Alanne, Tommi; Buarque Franzosi, Diogo; Frandsen, Mads T.


    We study the phenomenology of partially composite-Higgs models where electroweak symmetry breaking is dynamically induced, and the Higgs is a mixture of a composite and an elementary state. The models considered have explicit realizations in terms of gauge-Yukawa theories with new strongly...... interacting fermions coupled to elementary scalars and allow for a very SM-like Higgs state. We study constraints on their parameter spaces from vacuum stability and perturbativity as well as from LHC results and find that requiring vacuum stability up to the compositeness scale already imposes relevant...... constraints. A small part of parameter space around the classically conformal limit is stable up to the Planck scale. This is however already strongly disfavored by LHC results. in different limits, the models realize both (partially) composite-Higgs and (bosonic) technicolor models and a dynamical extension...

  17. Service life prediction and cementitious composites

    DEFF Research Database (Denmark)

    Stoklund Larsen, E.

    The present Ph.D.thesis describes and discusses the applicability of a systematic methodology recommended by CIB W80/RILEM-PSL for sevice life prediction. The report describes the most important inherent and environmental factors affecting the service life of structures of cementitious composites....... On the basis of this discription of factors and experience from a test programme described in SBI Report 222, Service life prediction and fibre reinforced cementitious composites, the applicabillity of the CIB/RILEM methodology is discussed....

  18. Predictive modeling of complications. (United States)

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


    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.

  19. Review on failure prediction techniques of composite single lap joint

    International Nuclear Information System (INIS)

    Ab Ghani, A.F.; Rivai, Ahmad


    Adhesive bonding is the most appropriate joining method in construction of composite structures. The use of reliable design and prediction technique will produce better performance of bonded joints. Several papers from recent papers and journals have been reviewed and synthesized to understand the current state of the art in this area. It is done by studying the most relevant analytical solutions for composite adherends with start of reviewing the most fundamental ones involving beam/plate theory. It is then extended to review single lap joint non linearity and failure prediction and finally on the failure prediction on composite single lap joint. The review also encompasses the finite element modelling part as tool to predict the elastic response of composite single lap joint and failure prediction numerically.

  20. Review on failure prediction techniques of composite single lap joint

    Energy Technology Data Exchange (ETDEWEB)

    Ab Ghani, A.F., E-mail:; Rivai, Ahmad, E-mail: [Faculty of Mechanical Engineering, Locked Bag 1200, Hang Tuah Jaya, 75450 Ayer Keroh, Melaka (Malaysia)


    Adhesive bonding is the most appropriate joining method in construction of composite structures. The use of reliable design and prediction technique will produce better performance of bonded joints. Several papers from recent papers and journals have been reviewed and synthesized to understand the current state of the art in this area. It is done by studying the most relevant analytical solutions for composite adherends with start of reviewing the most fundamental ones involving beam/plate theory. It is then extended to review single lap joint non linearity and failure prediction and finally on the failure prediction on composite single lap joint. The review also encompasses the finite element modelling part as tool to predict the elastic response of composite single lap joint and failure prediction numerically.

  1. Gas and grain chemical composition in cold cores as predicted by the Nautilus three-phase model (United States)

    Ruaud, Maxime; Wakelam, Valentine; Hersant, Franck


    We present an extended version of the two-phase gas-grain code NAUTILUS to the three-phase modelling of gas and grain chemistry of cold cores. In this model, both the mantle and the surface are considered as chemically active. We also take into account the competition among reaction, diffusion and evaporation. The model predictions are confronted to ice observations in the envelope of low-mass and massive young stellar objects as well as towards background stars. Modelled gas-phase abundances are compared to species observed towards TMC-1 (CP) and L134N dark clouds. We find that our model successfully reproduces the observed ice species. It is found that the reaction-diffusion competition strongly enhances reactions with barriers and more specifically reactions with H2, which is abundant on grains. This finding highlights the importance having a good approach to determine the abundance of H2 on grains. Consequently, it is found that the major N-bearing species on grains go from NH3 to N2 and HCN when the reaction-diffusion competition is taken into account. In the gas phase and before a few 105 yr, we find that the three-phase model does not have a strong impact on the observed species compared to the two-phase model. After this time, the computed abundances dramatically decrease due to the strong accretion on dust, which is not counterbalanced by the desorption less efficient than in the two-phase model. This strongly constrains the chemical age of cold cores to be of the order of few 105 yr.

  2. Composite hadron models

    International Nuclear Information System (INIS)

    Ogava, S.; Savada, S.; Nakagava, M.


    Composite models of hadrons are considered. The main attention is paid to the Sakata, S model. In the framework of the model it is presupposed that proton, neutron and Λ particle are the fundamental particles. Theoretical studies of unknown fundamental constituents of a substance have led to the creation of the quark model. In the framework of the quark model using the theory of SU(6)-symmetry the classification of mesons and baryons is considered. Using the quark model relations between hadron masses, their spins and electromagnetic properties are explained. The problem of three-colour model with many flavours is briefly presented

  3. Archaeological predictive model set. (United States)


    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...

  4. Wind power prediction models (United States)

    Levy, R.; Mcginness, H.


    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.

  5. Composite quantum collision models (United States)

    Lorenzo, Salvatore; Ciccarello, Francesco; Palma, G. Massimo


    A collision model (CM) is a framework to describe open quantum dynamics. In its memoryless version, it models the reservoir R as consisting of a large collection of elementary ancillas: the dynamics of the open system S results from successive collisions of S with the ancillas of R . Here, we present a general formulation of memoryless composite CMs, where S is partitioned into the very open system under study S coupled to one or more auxiliary systems {Si} . Their composite dynamics occurs through internal S -{Si} collisions interspersed with external ones involving {Si} and the reservoir R . We show that important known instances of quantum non-Markovian dynamics of S —such as the emission of an atom into a reservoir featuring a Lorentzian, or multi-Lorentzian, spectral density or a qubit subject to random telegraph noise—can be mapped on to such memoryless composite CMs.

  6. Inverse and Predictive Modeling

    Energy Technology Data Exchange (ETDEWEB)

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


    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.

  7. Modeling precipitation thermodynamics and kinetics in type 316 austenitic stainless steels with varying composition as an initial step toward predicting phase stability during irradiation

    Energy Technology Data Exchange (ETDEWEB)

    Shim, Jae-Hyeok, E-mail: [Department of Nuclear Engineering, University of Tennessee, Knoxville, TN 37996 (United States); High Temperature Energy Materials Research Center, Korea Institute of Science and Technology, Seoul 136-791 (Korea, Republic of); Povoden-Karadeniz, Erwin [Christian Doppler Laboratory for Early Stages of Precipitation, Vienna University of Technology, A-1040 Vienna (Austria); Kozeschnik, Ernst [Institute of Materials Science and Technology, Vienna University of Technology, A-1040 Vienna (Austria); Wirth, Brian D. [Department of Nuclear Engineering, University of Tennessee, Knoxville, TN 37996 (United States)


    Highlights: • We model the precipitation kinetics in irradiated 316 austenitic stainless steels. • Radiation-induced phases are predicted to form at over 10 dpa segregation conditions. • The Si content is the most critical for the formation of radiation-induced phases. - Abstract: The long-term evolution of precipitates in type 316 austenitic stainless steels at 400 °C has been simulated using a numerical model based on classical nucleation theory and the thermodynamic extremum principle. Particular attention has been paid to the precipitation of radiation-induced phases such as γ′ and G phases. In addition to the original compositions, the compositions for radiation-induced segregation at a dose level of 5, 10 or 20 dpa have been used in the simulation. In a 316 austenitic stainless steel, γ′ appears as the main precipitate with a small amount of G phase forming at 10 and 20 dpa. On the other hand, G phase becomes relatively dominant over γ′ at the same dose levels in a Ti-stabilized 316 austenitic stainless steel, which tends to suppress the formation of γ′. Among the segregated alloying elements, the concentration of Si seems to be the most critical for the formation of radiation-induced phases. An increase in dislocation density as well as increased diffusivity of Mn and Si significantly enhances the precipitation kinetics of the radiation-induced phases within this model.

  8. Predicting oil and gas compositional yields via chemical structure-chemical yield modeling (CS-CYM): Part 1 - Concepts and implementation

    Energy Technology Data Exchange (ETDEWEB)

    Freund, H.; Walters, C.C.; Kelemen, S.R.; Siskin, M.; Gorbaty, M.L.; Curry, D.J.; Bence, A.E. [ExxonMobil Research & Engineering Co., Annandale, NJ (United States)


    We have developed a method to calculate the amounts and composition of products resulting from the thermal decomposition of a solid complex carbonaceous material. This procedure provides a means of using laboratory measurements of complex carbonaceous solids to construct a representative model of its chemical structure (CS) that is then coupled with elementary reaction pathways to predict the chemical yield (CY) upon thermal decomposition. Data from elemental analysis, H, N, O, S, solid state {sup 13}C NMR, X-ray photoelectron spectroscopy (XPS), sulfur X-ray absorption structure spectroscopy (XANES), and pyrolysis-gas chromatography (GC) are used to constrain the construction of core molecular structures representative of the complex carbonaceous material. These core structures are expanded stochastically to describe large macromolecules ({gt} 10{sup 6} cores with similar to 10{sup 6} atoms) with bulk properties that match the experimental results. Gas, liquid and solid product yields, resulting from thermal decomposition, are calculated by identifying reactive functional groups within the CS stochastic ensemble and imposing a reaction network constrained by fundamental thermodynamics and kinetics. An expulsion model is added to the decomposition model to calculate the chemical products in open and closed systems. Product yields may then be predicted under a wide range of time-temperature conditions used in rapid laboratory pyrolysis experiments, refinery processes, or geologic maturation.

  9. Composite Dos Attack Model

    Directory of Open Access Journals (Sweden)

    Simona Ramanauskaitė


    Full Text Available Preparation for potential threats is one of the most important phases ensuring system security. It allows evaluating possible losses, changes in the attack process, the effectiveness of used countermeasures, optimal system settings, etc. In cyber-attack cases, executing real experiments can be difficult for many reasons. However, mathematical or programming models can be used instead of conducting experiments in a real environment. This work proposes a composite denial of service attack model that combines bandwidth exhaustion, filtering and memory depletion models for a more real representation of similar cyber-attacks. On the basis of the introduced model, different experiments were done. They showed the main dependencies of the influence of attacker and victim’s properties on the success probability of denial of service attack. In the future, this model can be used for the denial of service attack or countermeasure optimization.

  10. Intake, evaluation of small ruminant nutrition system model and prediction of body composition of Santa Ines lambs fed diets with different levels of energy

    Directory of Open Access Journals (Sweden)

    Iana Sérvulo Gomes Maia


    Full Text Available The objective of this study was to evaluate the nutrient intake and suitability test of the SRNS nutritional model for dry matter intake (DMI and average daily gain (ADG, and Hankins and Howe equations to estimate the carcass and empty body chemical composition of 35 Santa Ines lambs, non-castrated, with initial body weight of 14.77 ± 1.26 kg and two months old. After 10 days of adaptation, five animals were slaughtered serving as reference group for estimates of empty body weight (EBW and initial body composition. The remaining animals were distributed in randomized block design with five treatments with different levels of metabolizable energy (1.13, 1.40, 1.73, 2.22 and 2.60 Mcal/kg DM. Quadratic effect was observed for DMI, expressed in g/d, % BW and g/BW0.75, with maximum DM intake of 867.25 g/d. Non fiber carbohydrates (NFC and total digestible nutrients (TDN intakes, expressed in g/d, increased with increases in ME levels and the intakes of neutral detergent fiber (NDF, acid detergent fiber (ADF and fibrous carbohydrates (FC, expressed in g/d, presented decreasing linear effect. The DMI and ADG observed and predicted by SRNS model showed a Pearson correlation coefficient of 0.68 and 0.98, respectively. Comparing the chemical composition of the carcass and HH section, observed that HH section estimated satisfactorily the protein and ether extract of carcass of animals, with Pearson correlation coefficient of 0.77 and 0.92, respectively, while the water content was underestimated with Pearson correlation coefficient of 0.42. The rib section also satisfactorily estimated to ether extract and protein in the empty body (r = 0.96 and 0.86, respectively.

  11. Material Modelling - Composite Approach

    DEFF Research Database (Denmark)

    Nielsen, Lauge Fuglsang


    is successfully justified comparing predicted results with experimental data obtained in the HETEK-project on creep, relaxation, and shrinkage of very young concretes cured at a temperature of T = 20^o C and a relative humidity of RH = 100%. The model is also justified comparing predicted creep, shrinkage......, and internal stresses caused by drying shrinkage with experimental results reported in the literature on the mechanical behavior of mature concretes. It is then concluded that the model presented applied in general with respect to age at loading.From a stress analysis point of view the most important finding...... in this report is that cement paste and concrete behave practically as linear-viscoelastic materials from an age of approximately 10 hours. This is a significant age extension relative to earlier studies in the literature where linear-viscoelastic behavior is only demonstrated from ages of a few days. Thus...

  12. A stochastic model for predicting dextrose equivalent and saccharide composition during hydrolysis of starch by alpha-amylase

    NARCIS (Netherlands)

    Besselink, T.; Baks, T.; Janssen, A.E.M.; Boom, R.M.


    A stochastic model was developed that was used to describe the formation and breakdown of all saccharides involved during -amylolytic starch hydrolysis in time. This model is based on the subsite maps found in literature for Bacillus amyloliquefaciens -amylase (BAA) and Bacillus licheniformis

  13. Prediction of frozen food properties during freezing using product composition. (United States)

    Boonsupthip, W; Heldman, D R


    Frozen water fraction (FWF), as a function of temperature, is an important parameter for use in the design of food freezing processes. An FWF-prediction model, based on concentrations and molecular weights of specific product components, has been developed. Published food composition data were used to determine the identity and composition of key components. The model proposed in this investigation had been verified using published experimental FWF data and initial freezing temperature data, and by comparison to outputs from previously published models. It was found that specific food components with significant influence on freezing temperature depression of food products included low molecular weight water-soluble compounds with molality of 50 micromol per 100 g food or higher. Based on an analysis of 200 high-moisture food products, nearly 45% of the experimental initial freezing temperature data were within an absolute difference (AD) of +/- 0.15 degrees C and standard error (SE) of +/- 0.65 degrees C when compared to values predicted by the proposed model. The predicted relationship between temperature and FWF for all analyzed food products provided close agreements with experimental data (+/- 0.06 SE). The proposed model provided similar prediction capability for high- and intermediate-moisture food products. In addition, the proposed model provided statistically better prediction of initial freezing temperature and FWF than previous published models.

  14. Cultural Resource Predictive Modeling (United States)


    CR cultural resource CRM cultural resource management CRPM Cultural Resource Predictive Modeling DoD Department of Defense ESTCP Environmental...resource management ( CRM ) legal obligations under NEPA and the NHPA, military installations need to demonstrate that CRM decisions are based on objective...maxim “one size does not fit all,” and demonstrate that DoD installations have many different CRM needs that can and should be met through a variety

  15. Candidate Prediction Models and Methods

    DEFF Research Database (Denmark)

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


    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....

  16. Calibration of a PHREEQC Based Geochemical Model to Predict Surface Water Discharge Compositions from an Operating Uranium Mill in the Athabasca Basin

    International Nuclear Information System (INIS)

    Mahoney, John J.; Frey, Ryan A.


    Objectives: • Develop predictive model to estimate concentrations in the Sink Vulture Treated Effluent Management System (SVTEMS) for AREVA Resources Canada McClean Lake Mill: • Sink Reservoir, Vulture and McClean Lakes; • PHREEQC based calculations for geochemistry; • Employ PHREEPLOT for data fittings. • Model designed to predict concentrations in response to changing conditions, including: • Different ores; • Different processes; • Different waters sources; • Changing treatment conditions; • This is a batch mixing model: • Think well mixed beakers; • Each model represents one year; • No year-to-year carry over in models

  17. New Application of Bioelectrical Impedance Analysis by the Back Propagation Artificial Neural Network Mathematically Predictive Model of Tissue Composition in the Lower Limbs of Elderly People

    Directory of Open Access Journals (Sweden)

    Tsang-Pai Liu


    Conclusion: In summary, the greater predictive accuracy and precision made the application of BIA with the BP–ANN mathematical model more feasible for the clinical measurement of FM and FFM in the lower limbs of elderly people.

  18. Survey of composite particle models of electroweak interaction

    International Nuclear Information System (INIS)

    Suzuki, Mahiko.


    Models of composite weak bosons, the top-condensate model of electroweak interaction and related models we surveyed. Composite weak bosons must be tightly bound with a high compositeness scale in order to generate approximate puge symmetry dynamically. However, naturalness argument suggests that the compositeness scale is low at least in toy models. In the top-condensate model, where a composite Higgs doublet is formed with a very high scale, the prediction of the model is insensitive to details of the model and almost model-independent Actually, the numerical prediction of the t-quark and Higgs boson masses does not test compositeness of the Higgs boson nor condensation of the t-quark field. To illustrate the point, a composite t R -quark model is discussed which leads to the same numerical prediction as the top-condensate model. However, different constraints an imposed on the structure of the Higgs sector, depending on which particles are composite. The attempt to account the large t-b mass splitting by the high compositeness scale of the top-condensate model is reinterpreted in terms of fine tuning of more than one vacuum expectation value. It is difficult to lower, without a fourth generation, the t-quark mass in the composite particle models in general because the Yukawa coupling of the i-quark to the Higgs boson, t2 /4π = 0.1 for m t = 200 GeV, is too small for a coupling of a composite particle

  19. Micromechanical modelling of shape memory alloy composites

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Y.F.; Wang, X.M.; Yue, Z.F. [School of Mechanic, Civil Engineering and Architecture, Northwestern Polytechnical University, Xian, 710072 (China)


    An isothermal finite element method (FEM) model has been applied to study the behavior of two kinds of shape memory alloy (SMA) composites. For SMA-fiber reinforced normal metal composites, the FEM analysis shows that the mechanical behavior of the composites depends on the SMA volume fraction. For normal metal-fiber reinforced SMA matrix composites, the SMA phase transformation is affected by the increasing Young's modulus of the metal fiber. The phase transformation was also treated using a simple numerical analysis, which assumes that there are uniform stresses and strains distributions in the fiber and the matrix respectively. It is found that there is an obvious difference between the FEM analysis and the simple numerical assessment. Only FEM can provide reasonable predictions of phase transformations in SMA/normal metal composites. (Abstract Copyright [2004], Wiley Periodicals, Inc.)

  20. Composite Goldstone Dark Matter: Experimental Predictions from the Lattice

    DEFF Research Database (Denmark)

    Hietanen, Ari; Lewis, Randy; Pica, Claudio


    We study, via first principles lattice simulations, the nonperturbative dynamics of SU(2) gauge theory with two fundamental Dirac flavors. The model can be used simultaneously as a template for composite Goldstone boson dark matter and for breaking the electroweak symmetry dynamically. We compute...... the form factor, allowing us to estimate the associated electromagnetic charge radius. Surprisingly we observe that the form factor obeys vector meson dominance even for the two color theory. We finally compare the model predictions with dark matter direct detection experiments. Our results...

  1. Life Prediction for FRP composites with Data Fusion & Machine Learning (United States)

    National Aeronautics and Space Administration — High-fidelity, probabilistic predictions of damage evolution in fiber-reinforced polymer (FRP) composite structures could accelerate development and certification of...

  2. Confidence scores for prediction models

    DEFF Research Database (Denmark)

    Gerds, Thomas Alexander; van de Wiel, MA


    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,...

  3. Exploring holographic Composite Higgs models

    Energy Technology Data Exchange (ETDEWEB)

    Croon, Djuna [Department of Physics and Astronomy, University of Sussex,BN1 9QH Brighton (United Kingdom); Perimeter Institute for Theoretical Physics,Waterloo, ON (Canada); Dillon, Barry M.; Huber, Stephan J.; Sanz, Veronica [Department of Physics and Astronomy, University of Sussex,BN1 9QH Brighton (United Kingdom)


    Simple Composite Higgs models predict new vector-like fermions not too far from the electroweak scale, yet LHC limits are now sensitive to the TeV scale. Motivated by this tension, we explore the holographic dual of the minimal model, MCHM{sub 5}, to try and alleviate this tension without increasing the fine-tuning in the Higgs potential. Interestingly, we find that lowering the UV cutoff in the 5D picture allows for heavier top partners and less fine-tuning. In the 4D dual this corresponds to increasing the number of “colours” N, thus increasing the decay constant of the Goldstone Higgs. This is essentially a ‘Little Randall-Sundrum Model’, which are known to reduce some flavour and electroweak constraints. Furthermore, in anticipation of the ongoing efforts at the LHC to put bounds on the top Yukawa, we demonstrate that deviations from the SM can be suppressed or enhanced with respect to what is expected from mere symmetry arguments in 4D. We conclude that the 5D holographic realisation of the MCHM{sub 5} with a small UV cutoff is not in tension with the current experimental data.

  4. Predicting Magnetoelectric Coupling in Layered and Graded Composites

    Directory of Open Access Journals (Sweden)

    Mirza Bichurin


    Full Text Available Magnetoelectric (ME interaction in magnetostrictive-piezoelectric multiferroic structures consists in inducing the electric field across the structure in an applied magnetic field and is a product property of magnetostriction and piezoelectricity in components. ME voltage coefficient that is the ratio of induced electric field to applied magnetic field is the key parameter of ME coupling strength. It has been known that the ME coupling strength is dictated by the product of the piezoelectric and piezomagnetic coefficients of initial phases. As a result, using the laminates with graded piezoelectric and piezomagnetic parameters are a new pathway to the increase in the ME coupling strength. Recently developed models predict stronger ME interactions in composites based on graded components compared to homogeneous ones. We discuss predicting the ME coupling strength for layered structures of homogeneous and compositionally graded magnetostrictive and piezoelectric components based on the graphs of ME voltage coefficients against composite parameters. For obtaining the graphs, we developed equations for ME output in applied magnetic field for possible modes of operation and layered structure configurations. In particular, our studies have been performed on low-frequency ME coupling, enhanced ME effect in electromechanical resonance (EMR region for longitudinal and bending modes. Additionally, ME coupling at magnetic resonance in magnetostrictive component and at overlapping the EMR and magnetic resonance is investigated. We considered symmetric trilayers and asymmetric bilayers of magnetostrictive and piezoelectric components and multilayered structures based on compositionally stepped initial components.

  5. Sludge pipe flow pressure drop prediction using composite power ...

    African Journals Online (AJOL)

    Sludge pipe flow pressure drop prediction using composite power-law friction ... Water SA. Journal Home · ABOUT THIS JOURNAL · Advanced Search · Current Issue ... When predicting pressure gradients for the flow of sludges in pipes, the ...

  6. NOAA's National Air Quality Prediction and Development of Aerosol and Atmospheric Composition Prediction Components for NGGPS (United States)

    Stajner, I.; McQueen, J.; Lee, P.; Stein, A. F.; Wilczak, J. M.; Upadhayay, S.; daSilva, A.; Lu, C. H.; Grell, G. A.; Pierce, R. B.


    NOAA's operational air quality predictions of ozone, fine particulate matter (PM2.5) and wildfire smoke over the United States and airborne dust over the contiguous 48 states are distributed at The National Air Quality Forecast Capability (NAQFC) providing these predictions was updated in June 2017. Ozone and PM2.5 predictions are now produced using the system linking the Community Multiscale Air Quality model (CMAQ) version 5.0.2 with meteorological inputs from the North American Mesoscale Forecast System (NAM) version 4. Predictions of PM2.5 include intermittent dust emissions and wildfire emissions from an updated version of BlueSky system. For the latter, the CMAQ system is initialized by rerunning it over the previous 24 hours to include wildfire emissions at the time when they were observed from the satellites. Post processing to reduce the bias in PM2.5 prediction was updated using the Kalman filter analog (KFAN) technique. Dust related aerosol species at the CMAQ domain lateral boundaries now come from the NEMS Global Aerosol Component (NGAC) v2 predictions. Further development of NAQFC includes testing of CMAQ predictions to 72 hours, Canadian fire emissions data from Environment and Climate Change Canada (ECCC) and the KFAN technique to reduce bias in ozone predictions. NOAA is developing the Next Generation Global Predictions System (NGGPS) with an aerosol and gaseous atmospheric composition component to improve and integrate aerosol and ozone predictions and evaluate their impacts on physics, data assimilation and weather prediction. Efforts are underway to improve cloud microphysics, investigate aerosol effects and include representations of atmospheric composition of varying complexity into NGGPS: from the operational ozone parameterization, GOCART aerosols, with simplified ozone chemistry, to CMAQ chemistry with aerosol modules. We will present progress on community building, planning and development of NGGPS.

  7. Maximally Symmetric Composite Higgs Models. (United States)

    Csáki, Csaba; Ma, Teng; Shu, Jing


    Maximal symmetry is a novel tool for composite pseudo Goldstone boson Higgs models: it is a remnant of an enhanced global symmetry of the composite fermion sector involving a twisting with the Higgs field. Maximal symmetry has far-reaching consequences: it ensures that the Higgs potential is finite and fully calculable, and also minimizes the tuning. We present a detailed analysis of the maximally symmetric SO(5)/SO(4) model and comment on its observational consequences.

  8. Thermoviscoelastic characterization and prediction of Kevlar/epoxy composite laminates (United States)

    Gramoll, K. C.; Dillard, D. A.; Brinson, H. F.


    The thermoviscoelastic characterization of Kevlar 49/Fiberite 7714A epoxy composite lamina and the development of a numerical procedure to predict the viscoelastic response of any general laminate constructed from the same material were studied. The four orthotropic material properties, S sub 11, S sub 12, S sub 22, and S sub 66, were characterized by 20 minute static creep tests on unidirectional (0) sub 8, (10) sub 8, and (90) sub 16 lamina specimens. The Time-Temperature Superposition-Principle (TTSP) was used successfully to accelerate the characterization process. A nonlinear constitutive model was developed to describe the stress dependent viscoelastic response for each of the material properties. A numerical procedure to predict long term laminate properties from lamina properties (obtained experimentally) was developed. Numerical instabilities and time constraints associated with viscoelastic numerical techniques were discussed and solved. The numerical procedure was incorporated into a user friendly microcomputer program called Viscoelastic Composite Analysis Program (VCAP), which is available for IBM PC type computers. The program was designed for ease of use. The final phase involved testing actual laminates constructed from the characterized material, Kevlar/epoxy, at various temperatures and load level for 4 to 5 weeks. These results were compared with the VCAP program predictions to verify the testing procedure and to check the numerical procedure used in the program. The actual tests and predictions agreed for all test cases which included 1, 2, 3, and 4 fiber direction laminates.

  9. Structural modeling for multicell composite rotor blades (United States)

    Rehfield, Lawrence W.; Atilgan, Ali R.


    Composite material systems are currently good candidates for aerospace structures, primarily for the design flexibility they offer, i.e., it is possible to tailor the material and manufacturing approach to the application. A working definition of elastic or structural tailoring is the use of structural concept, fiber orientation, ply stacking sequence, and a blend of materials to achieve specific performance goals. In the design process, choices of materials and dimensions are made which produce specific response characteristics, and which permit the selected goals to be achieved. Common choices for tailoring goals are preventing instabilities or vibration resonances or enhancing damage tolerance. An essential, enabling factor in the design of tailored composite structures is structural modeling that accurately, but simply, characterizes response. The objective of this paper is to present a new multicell beam model for composite rotor blades and to validate predictions based on the new model by comparison with a finite element simulation in three benchmark static load cases.


    African Journals Online (AJOL)


    their low power requirements, are relatively cheap and are environment friendly. ... PREDICTED PERCENTAGE DISSATISFIED MODEL EVALUATION OF EVAPORATIVE COOLING ... The performance of direct evaporative coolers is a.

  11. Multifunctional multiscale composites: Processing, modeling and characterization (United States)

    Qiu, Jingjing

    Carbon nanotubes (CNTs) demonstrate extraordinary properties and show great promise in enhancing out-of-plane properties of traditional polymer/fiber composites and enabling functionality. However, current manufacturing challenges hinder the realization of their potential. In the dissertation research, both experimental and computational efforts have been conducted to investigate effective manufacturing techniques of CNT integrated multiscale composites. The fabricated composites demonstrated significant improvements in physical properties, such as tensile strength, tensile modulus, inter-laminar shear strength, thermal dimension stability and electrical conductivity. Such multiscale composites were truly multifunctional with the addition of CNTs. Furthermore, a novel hierarchical multiscale modeling method was developed in this research. Molecular dynamic (MD) simulation offered reasonable explanation of CNTs dispersion and their motion in polymer solution. Bi-mode finite-extensible-nonlinear-elastic (FENE) dumbbell simulation was used to analyze the influence of CNT length distribution on the stress tensor and shear-rate-dependent viscosity. Based on the simulated viscosity profile and empirical equations from experiments, a macroscale flow simulation model on the finite element method (FEM) method was developed and validated to predict resin flow behavior in the processing of CNT-enhanced multiscale composites. The proposed multiscale modeling method provided a comprehensive understanding of micro/nano flow in both atomistic details and mesoscale. The simulation model can be used to optimize process design and control of the mold-filling process in multiscale composite manufacturing. This research provided systematic investigations into the CNT-based multiscale composites. The results from this study may be used to leverage the benefits of CNTs and open up new application opportunities for high-performance multifunctional multiscale composites. Keywords. Carbon

  12. Compositional and Quantitative Model Checking

    DEFF Research Database (Denmark)

    Larsen, Kim Guldstrand


    This paper gives a survey of a composition model checking methodology and its succesfull instantiation to the model checking of networks of finite-state, timed, hybrid and probabilistic systems with respect; to suitable quantitative versions of the modal mu-calculus [Koz82]. The method is based...

  13. Composites

    International Nuclear Information System (INIS)

    Kasen, M.B.


    This chapter discusses the roles of composite laminates and aggregates in cryogenic technology. Filamentary-reinforced composites are emphasized because they are the most widely used composite materials. Topics considered include composite systems and terminology, design and fabrication, composite failure, high-pressure reinforced plastic laminates, low-pressure reinforced plastics, reinforced metals, selectively reinforced structures, the effect of cryogenic temperatures, woven-fabric and random-mat composites, uniaxial fiber-reinforced composites, composite joints in cryogenic structures, joining techniques at room temperature, radiation effects, testing laminates at cryogenic temperatures, static and cyclic tensile testing, static and cyclic compression testing, interlaminar shear testing, secondary property tests, and concrete aggregates. It is suggested that cryogenic composite technology would benefit from the development of a fracture mechanics model for predicting the fitness-for-purpose of polymer-matrix composite structures

  14. NOAA ESRI Shapefile - sediment composition class predictions in New York offshore planning area from Biogeography Branch (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset represents sediment composition class predictions from a sediment spatial model developed for the New York offshore spatial planning area. The...

  15. Modeling comprehensive chemical composition of weathered oil following a marine spill to predict ozone and potential secondary aerosol formation and constrain transport pathways (United States)

    Drozd, Greg T.; Worton, David R.; Aeppli, Christoph; Reddy, Christopher M.; Zhang, Haofei; Variano, Evan; Goldstein, Allen H.


    Releases of hydrocarbons from oil spills have large environmental impacts in both the ocean and atmosphere. Oil evaporation is not simply a mechanism of mass loss from the ocean, as it also causes production of atmospheric pollutants. Monitoring atmospheric emissions from oil spills must include a broad range of volatile organic compounds (VOC), including intermediate-volatile and semivolatile compounds (IVOC, SVOC), which cause secondary organic aerosol (SOA) and ozone production. The Deepwater Horizon (DWH) disaster in the northern Gulf of Mexico during Spring/Summer of 2010 presented a unique opportunity to observe SOA production due to an oil spill. To better understand these observations, we conducted measurements and modeled oil evaporation utilizing unprecedented comprehensive composition measurements, achieved by gas chromatography with vacuum ultraviolet time of flight mass spectrometry (GC-VUV-HR-ToFMS). All hydrocarbons with 10-30 carbons were classified by degree of branching, number of cyclic rings, aromaticity, and molecular weight; these hydrocarbons comprise ˜70% of total oil mass. Such detailed and comprehensive characterization of DWH oil allowed bottom-up estimates of oil evaporation kinetics. We developed an evaporative model, using solely our composition measurements and thermodynamic data, that is in excellent agreement with published mass evaporation rates and our wind-tunnel measurements. Using this model, we determine surface slick samples are composed of oil with a distribution of evaporative ages and identify and characterize probable subsurface transport of oil.

  16. Bootstrap prediction and Bayesian prediction under misspecified models


    Fushiki, Tadayoshi


    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...

  17. Foundations of compositional model theory

    Czech Academy of Sciences Publication Activity Database

    Jiroušek, Radim


    Roč. 40, č. 6 (2011), s. 623-678 ISSN 0308-1079 R&D Projects: GA MŠk 1M0572; GA ČR GA201/09/1891; GA ČR GEICC/08/E010 Institutional research plan: CEZ:AV0Z10750506 Keywords : multidimensional probability distribution * conditional independence * graphical Markov model * composition of distributions Subject RIV: IN - Informatics, Computer Science Impact factor: 0.667, year: 2011 of compositional model theory.pdf

  18. Prediction of enthalpy and thermal conductivity of frozen meat and fish products from composition data

    NARCIS (Netherlands)

    Sman, van der R.G.M.


    In this paper we present models predicting thermophysical properties of frozen meat products purely using their composition data. Based on our previous model, predicting the water activity of (frozen) meat and fish products, while taking into account the non-ideality of the unfrozen solution, we can


    African Journals Online (AJOL)


    Jul 2, 2012 ... signal based on a process model, coping with constraints on inputs and ... paper, we will present an introduction to the theory and application of MPC with Matlab codes ... section 5 presents the simulation results and section 6.

  20. Models for Textile Composites Forming

    NARCIS (Netherlands)

    Akkerman, Remko; ten Thije, R.H.W.


    Drape of dry fabrics or prepregs was recognised as an important feature in composites forming operations from the start. In the 1950s the first mathematical models were developed based on purely kinematic arguments. The assumption of zero fibre strains and trellis deformations led to the socalled

  1. Micromechanical models for graded composite materials

    DEFF Research Database (Denmark)

    Reiter, T; Dvorak, G.J.; Tvergaard, Viggo


    of piecewise homogeneous layers with equivalent elastic properties estimated by Mori-Tanaka and self-consistent methods are also analysed under similar boundary conditions. Comparisons of the overall and local fields predicted by the discrete and homogenized models are made using a C/SiC composite system...... fields are predicted by Mori-Tanaka estimates. On the other hand, the response of graded materials with a skeletal microstructure in a wide transition zone between clearly defined matrix phases is better approximated by the self-consistent estimates. Certain exceptions are noted for loading by overall...... transverse shear stress; The results suggest that the averaging methods originally developed for statistically homogeneous aggregates may be selectively applied, with a reasonable degree of confidence, to aggregates dth composition gradients, subjected to both uniform and nonuniform overall loads. (C) 1997...

  2. Melanoma Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  3. Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou's General Pseudo Amino Acid Composition. (United States)

    Ahmad, Khurshid; Waris, Muhammad; Hayat, Maqsood


    Mitochondrion is the key organelle of eukaryotic cell, which provides energy for cellular activities. Submitochondrial locations of proteins play crucial role in understanding different biological processes such as energy metabolism, program cell death, and ionic homeostasis. Prediction of submitochondrial locations through conventional methods are expensive and time consuming because of the large number of protein sequences generated in the last few decades. Therefore, it is intensively desired to establish an automated model for identification of submitochondrial locations of proteins. In this regard, the current study is initiated to develop a fast, reliable, and accurate computational model. Various feature extraction methods such as dipeptide composition (DPC), Split Amino Acid Composition, and Composition and Translation were utilized. In order to overcome the issue of biasness, oversampling technique SMOTE was applied to balance the datasets. Several classification learners including K-Nearest Neighbor, Probabilistic Neural Network, and support vector machine (SVM) are used. Jackknife test is applied to assess the performance of classification algorithms using two benchmark datasets. Among various classification algorithms, SVM achieved the highest success rates in conjunction with the condensed feature space of DPC, which are 95.20 % accuracy on dataset SML3-317 and 95.11 % on dataset SML3-983. The empirical results revealed that our proposed model obtained the highest results so far in the literatures. It is anticipated that our proposed model might be useful for future studies.

  4. Modelling bankruptcy prediction models in Slovak companies

    Directory of Open Access Journals (Sweden)

    Kovacova Maria


    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.

  5. Predictive models of moth development (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...

  6. Advanced Manufacturing Technologies (AMT): Composites Integrated Modeling (United States)

    National Aeronautics and Space Administration — The Composites Integrated Modeling (CIM) Element developed low cost, lightweight, and efficient composite structures, materials and manufacturing technologies with...

  7. Predictive Models and Computational Embryology (United States)

    EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

  8. Predictive Modeling in Race Walking

    Directory of Open Access Journals (Sweden)

    Krzysztof Wiktorowicz


    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.

  9. Prediction of Composite Pressure Vessel Failure Location using Fiber Bragg Grating Sensors (United States)

    Kreger, Steven T.; Taylor, F. Tad; Ortyl, Nicholas E.; Grant, Joseph


    Ten composite pressure vessels were instrumented with fiber Bragg grating sensors in order to assess the strain levels of the vessel under various loading conditions. This paper and presentation will discuss the testing methodology, the test results, compare the testing results to the analytical model, and present a possible methodology for predicting the failure location and strain level of composite pressure vessels.

  10. Testing and Life Prediction for Composite Rotor Hub Flexbeams (United States)

    Murri, Gretchen B.


    A summary of several studies of delamination in tapered composite laminates with internal ply-drops is presented. Initial studies used 2D FE models to calculate interlaminar stresses at the ply-ending locations in linear tapered laminates under tension loading. Strain energy release rates for delamination in these laminates indicated that delamination would likely start at the juncture of the tapered and thin regions and grow unstably in both directions. Tests of glass/epoxy and graphite/epoxy linear tapered laminates under axial tension delaminated as predicted. Nonlinear tapered specimens were cut from a full-size helicopter rotor hub and were tested under combined constant axial tension and cyclic transverse bending loading to simulate the loading experienced by a rotorhub flexbeam in flight. For all the tested specimens, delamination began at the tip of the outermost dropped ply group and grew first toward the tapered region. A 2D FE model was created that duplicated the test flexbeam layup, geometry, and loading. Surface strains calculated by the model agreed very closely with the measured surface strains in the specimens. The delamination patterns observed in the tests were simulated in the model by releasing pairs of MPCs along those interfaces. Strain energy release rates associated with the delamination growth were calculated for several configurations and using two different FE analysis codes. Calculations from the codes agreed very closely. The strain energy release rate results were used with material characterization data to predict fatigue delamination onset lives for nonlinear tapered flexbeams with two different ply-dropping schemes. The predicted curves agreed well with the test data for each case studied.

  11. Predicting the Atmospheric Composition of Extrasolar Giant Planets (United States)

    Sharp, A. G.; Moses, J. I.; Friedson, A. J.; Fegley, B., Jr.; Marley, M. S.; Lodders, K.


    To date, approximately 120 planet-sized objects have been discovered around other stars, mostly through the radial-velocity technique. This technique can provide information about a planet s minimum mass and its orbital period and distance; however, few other planetary data can be obtained at this point in time unless we are fortunate enough to find an extrasolar giant planet that transits its parent star (i.e., the orbit is edge-on as seen from Earth). In that situation, many physical properties of the planet and its parent star can be determined, including some compositional information. Our prospects of directly obtaining spectra from extrasolar planets may improve in the near future, through missions like NASA's Terrestrial Planet Finder. Most of the extrasolar giant planets (EGPs) discovered so far have masses equal to or greater than Jupiter's mass, and roughly 16% have orbital radii less than 0.1 AU - extremely close to the parent star by our own Solar-System standards (note that Mercury is located at a mean distance of 0.39 AU and Jupiter at 5.2 AU from the Sun). Although all EGPs are expected to have hydrogen-dominated atmospheres similar to Jupiter, the orbital distance can strongly affect the planet's temperature, physical, chemical, and spectral properties, and the abundance of minor, detectable atmospheric constituents. Thermochemical equilibrium models can provide good zero-order predictions for the atmospheric composition of EGPs. However, both the composition and spectral properties will depend in large part on disequilibrium processes like photochemistry, chemical kinetics, atmospheric transport, and haze formation. We have developed a photochemical kinetics, radiative transfer, and 1-D vertical transport model to study the atmospheric composition of EGPs. The chemical reaction list contains H-, C-, O-, and N-bearing species and is designed to be valid for atmospheric temperatures ranging from 100-3000 K and pressures up to 50 bar. Here we examine

  12. Fatigue life prediction in woven carbon fabric polyester composites

    International Nuclear Information System (INIS)

    Khan, Z.; Al-Sulaiman, F.S.; Farooqi, J.K.


    An analytical model, based on stiffness degradation during fatigue loading, which has been used for fatigue life predictions in the Fiber Reinforced Plastics (FRP), is employed to examine its validity to the fatigue life predictions in the Woven Fabric Reinforced Plastics. The rate of stiffness degradation (dE/dN) has been obtained from the constant amplitude fatigue testing of 8-ply coupons made from prepreg plain-weave woven carbon-carbon fabric having a polyester resin as the matrix material. The test coupons had three different ply stacking sequences, namely, the unidirectional (0)8,and two off axis plied (0,0,+45,-45)s, and (+45,-45,0,0)s orientations. The estimated fatigue lives obtained from the damage rate function dD/dN, which in turn was a function of the stiffness degradation rate dE/dN, were compared with the experimentally observed fatigue life data. It is shown that the stiffness degradation model provides reasonably good correlation between the analytically determined fatigue lives and the experimentally observed fatigue for the plain-weave woven Carbon-Carbon Fabric Reinforced Plastic Composites. (author)

  13. Constitutive modelling of composite biopolymer networks. (United States)

    Fallqvist, B; Kroon, M


    The mechanical behaviour of biopolymer networks is to a large extent determined at a microstructural level where the characteristics of individual filaments and the interactions between them determine the response at a macroscopic level. Phenomena such as viscoelasticity and strain-hardening followed by strain-softening are observed experimentally in these networks, often due to microstructural changes (such as filament sliding, rupture and cross-link debonding). Further, composite structures can also be formed with vastly different mechanical properties as compared to the individual networks. In this present paper, we present a constitutive model presented in a continuum framework aimed at capturing these effects. Special care is taken to formulate thermodynamically consistent evolution laws for dissipative effects. This model, incorporating possible anisotropic network properties, is based on a strain energy function, split into an isochoric and a volumetric part. Generalisation to three dimensions is performed by numerical integration over the unit sphere. Model predictions indicate that the constitutive model is well able to predict the elastic and viscoelastic response of biological networks, and to an extent also composite structures. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. Prediction of wrinklings and porosities of thermoplastic composits after thermostamping (United States)

    Hamila, Nahiene; Guzman-Maldonado, Eduardo; Xiong, Hu; Wang, Peng; Boisse, Philippe; Bikard, Jerome


    During thermoforming process, the consolidation deformation mode of thermoplastic prepregs is one of the key deformation modes especially in the consolidation step, where the two resin flow phenomena: resin percolation and transverse squeeze flow, play an important role. This occurs a viscosity behavior for consolidation mode. Based on a visco-hyper-elastic model for the characterization of thermoplastic prepregs proposed by Guzman, which involves different independent modes of deformation: elongation mode, bending mode with thermo-dependent, and viscoelastic in-plan shearing mode with thermo-dependent, a viscoelastic model completed with consolidation behavior will be presented in this paper. A completed three-dimensional mechanical behavior with compaction effect for thermoplastic pre-impregnated composites is constituted, and the associated parameters are identified by compaction test. Moreover, a seven-node prismatic solid-shell finite element approach is used for the forming simulation. To subdue transverse shear locking, an intermediate material frame related to the element sides is introduced in order to fix nodal transverse shear strain components. Indeed, the enhanced assumed strain method and a reduced integration scheme are combined offering a linear varying strain field along the thickness direction to circumvent thickness locking, and an hourglass stabilization procedure is employed in order to correct the element's rank deficiency for pinching. An additional node is added at the center providing a quadratic interpolation of the displacement in the thickness direction. The predominance of this element is the ability of three dimensional analysis, especially for the transverse stress existence through the thickness of material, which is essential for the consolidation modelling. Finally, an intimate contact model is employed to predict the evolution of the consolidation which permits the microstructure prediction of void presented through the prepreg

  15. Resin flow/fiber deformation model for composites

    International Nuclear Information System (INIS)

    Gutowski, T.G.


    This paper presents a resin flow/fiber deformation model that can be used to predict the behavior of composites during the molding cycle. The model can take into account time varying pressure and viscosity and output the time history of the fiber volume fraction. With this known, the composite thickness, resin pressure, and fiber pressure can all be determined as a function of time. The results of this model are in good agreement with experimentally measured values. 10 references, 9 figures

  16. Derivation of governing equation for predicting thermal conductivity of composites with spherical inclusions and its applications

    International Nuclear Information System (INIS)

    Lee, Jae-Kon; Kim, Jin-Gon


    A governing differential equation for predicting the effective thermal conductivity of composites with spherical inclusions is shown to be simply derived by using the result of the generalized self-consistent model. By applying the equation to composites including spherical inclusions such as graded spherical inclusions, microballoons, mutiply-coated spheres, and spherical inclusions with an interphase, their effective thermal conductivities are easily predicted. The results are compared with those in the literatures to be consistent. It can be stated from the investigations that the effective thermal conductivity of composites with spherical inclusions can be estimated as long as their conductivities are expressed as a function of their radius. -- Highlights: → We derive equation for predicting the effective thermal conductivity of composites. → The equation is derived using the results of the generalized self-consistent model. → The inclusions are graded sphere, microballoons, and mutiply-coated spheres.

  17. Meshfree modeling in laminated composites

    KAUST Repository

    Simkins, Daniel Craig; Collier, Nathan; Alford, Joseph B.


    A problem of increasing importance in the aerospace industry is in detailed modeling of explicit fracture in laminated composite materials. For design applications, the simulation must be capable of initiation and propagation of changes in the problem domain. Further, these changes must be able to be incorporated within a design-scale simulation. The use of a visibility condition, coupled with the local and dynamic nature of meshfree shape function construction allows one to initiate and explicitly open and propagate holes inside a previously continuous problem domain. The method to be presented naturally couples to a hierarchical multi-scale material model incorporating external knowldege bases to achieve the goal of a practical explicit fracture modeling capability for full-scale problems. © 2013 Springer-Verlag.

  18. Meshfree modeling in laminated composites

    KAUST Repository

    Simkins, Daniel Craig


    A problem of increasing importance in the aerospace industry is in detailed modeling of explicit fracture in laminated composite materials. For design applications, the simulation must be capable of initiation and propagation of changes in the problem domain. Further, these changes must be able to be incorporated within a design-scale simulation. The use of a visibility condition, coupled with the local and dynamic nature of meshfree shape function construction allows one to initiate and explicitly open and propagate holes inside a previously continuous problem domain. The method to be presented naturally couples to a hierarchical multi-scale material model incorporating external knowldege bases to achieve the goal of a practical explicit fracture modeling capability for full-scale problems. © 2013 Springer-Verlag.

  19. Mass generation in composite models

    International Nuclear Information System (INIS)

    Peccei, R.D.


    I discuss aspects of composite models of quarks and leptons connected with the dynamics of how these fermions acquire mass. Several issues related to the protection mechanisms necessary to keep quarks and leptons light are illustrated by means of concrete examples and a critical overview of suggestions for family replications is given. Some old and new ideas of how one may actually be able to generate small quark and lepton masses are examined, along with some of the difficulties they encounter in practice. (orig.)

  20. NOAA's National Air Quality Predictions and Development of Aerosol and Atmospheric Composition Prediction Components for the Next Generation Global Prediction System (United States)

    Stajner, I.; Hou, Y. T.; McQueen, J.; Lee, P.; Stein, A. F.; Tong, D.; Pan, L.; Huang, J.; Huang, H. C.; Upadhayay, S.


    NOAA provides operational air quality predictions using the National Air Quality Forecast Capability (NAQFC): ozone and wildfire smoke for the United States and airborne dust for the contiguous 48 states at NOAA's predictions of fine particulate matter (PM2.5) became publicly available in February 2016. Ozone and PM2.5 predictions are produced using a system that operationally links the Community Multiscale Air Quality (CMAQ) model with meteorological inputs from the North American mesoscale forecast Model (NAM). Smoke and dust predictions are provided using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Current NAQFC focus is on updating CMAQ to version 5.0.2, improving PM2.5 predictions, and updating emissions estimates, especially for NOx using recently observed trends. Wildfire smoke emissions from a newer version of the USFS BlueSky system are being included in a new configuration of the NAQFC NAM-CMAQ system, which is re-run for the previous 24 hours when the wildfires were observed from satellites, to better represent wildfire emissions prior to initiating predictions for the next 48 hours. In addition, NOAA is developing the Next Generation Global Prediction System (NGGPS) to represent the earth system for extended weather prediction. NGGPS will include a representation of atmospheric dynamics, physics, aerosols and atmospheric composition as well as coupling with ocean, wave, ice and land components. NGGPS is being developed with a broad community involvement, including community developed components and academic research to develop and test potential improvements for potentially inclusion in NGGPS. Several investigators at NOAA's research laboratories and in academia are working to improve the aerosol and gaseous chemistry representation for NGGPS, to develop and evaluate the representation of atmospheric composition, and to establish and improve the coupling with radiation and microphysics

  1. Bayesian inference model for fatigue life of laminated composites

    DEFF Research Database (Denmark)

    Dimitrov, Nikolay Krasimirov; Kiureghian, Armen Der; Berggreen, Christian


    A probabilistic model for estimating the fatigue life of laminated composite plates is developed. The model is based on lamina-level input data, making it possible to predict fatigue properties for a wide range of laminate configurations. Model parameters are estimated by Bayesian inference. The ...

  2. Prediction of U3SI2-Al burn-up and SiC/p-AI composition effects on its thermal conductivity using metal matrix composite (MMC) model containing progressive sub-dispersion

    International Nuclear Information System (INIS)



    The model takes into account the evolution of constituent volume fraction. Sub-dispersion of disperse contains fission gas bubbles that increase with bum-up. The metal matrix could contain pore and void, a different type of disperse that vary wth time. The model is previously aimed to dispersion-nuclear fuel element. The model consists of a combination of different conductance constituent of both matrix and sub-matrix. Application is carried out to predict the fuel swelling effect on thermal conductivity of U 3 SI 2 -Al dispersion, and to volume fraction effect on conductivity of SiC-particulate reinforced AI matrix. The model shows that both fuel fraction and fission gas swelling decrease the thermal conductivity. During the start-up period of swelling the conductivity increases as aluminum pore close. then decreases most linearly. SiC/p-AI conductivity decreases most linearly with particulate volume fraction, attains 57.6% of pure AI at 50 % v/v. The author conclude that the model developed is applicable for more general MMC. (author)

  3. Statistical model based gender prediction for targeted NGS clinical panels

    Directory of Open Access Journals (Sweden)

    Palani Kannan Kandavel


    The reference test dataset are being used to test the model. The sensitivity on predicting the gender has been increased from the current “genotype composition in ChrX” based approach. In addition, the prediction score given by the model can be used to evaluate the quality of clinical dataset. The higher prediction score towards its respective gender indicates the higher quality of sequenced data.

  4. Shared Task System Description: Frustratingly Hard Compositionality Prediction

    DEFF Research Database (Denmark)

    Johannsen, Anders Trærup; Martinez Alonso, Hector; Rishøj, Christian


    , and the likelihood of long translation equivalents in other languages. Many of the features we considered correlated significantly with human compositionality scores, but in support vector regression experiments we obtained the best results using only COALS-based endocentricity scores. Our system was nevertheless......We considered a wide range of features for the DiSCo 2011 shared task about compositionality prediction for word pairs, including COALS-based endocentricity scores, compositionality scores based on distributional clusters, statistics about wordnet-induced paraphrases, hyphenation...

  5. Waste vitrification: prediction of acceptable compositions in a lime-soda-silica glass-forming system

    International Nuclear Information System (INIS)

    Gilliam, T.M.; Jantzen, C.M.


    A model is presented based upon calculated bridging oxygens which allows the prediction of the region of acceptable glass compositions for a lime-soda-silica glass-forming system containing mixed waste. The model can be used to guide glass formulation studies (e.g., treatability studies) or assess the applicability of vitrification to candidate waste streams

  6. Body composition in elderly people: effect of criterion estimates on predictive equations

    International Nuclear Information System (INIS)

    Baumgartner, R.N.; Heymsfield, S.B.; Lichtman, S.; Wang, J.; Pierson, R.N. Jr.


    The purposes of this study were to determine whether there are significant differences between two- and four-compartment model estimates of body composition, whether these differences are associated with aqueous and mineral fractions of the fat-free mass (FFM); and whether the differences are retained in equations for predicting body composition from anthropometry and bioelectric resistance. Body composition was estimated in 98 men and women aged 65-94 y by using a four-compartment model based on hydrodensitometry, 3 H 2 O dilution, and dual-photon absorptiometry. These estimates were significantly different from those obtained by using Siri's two-compartment model. The differences were associated significantly (P less than 0.0001) with variation in the aqueous fraction of FFM. Equations for predicting body composition from anthropometry and resistance, when calibrated against two-compartment model estimates, retained these systematic errors. Equations predicting body composition in elderly people should be calibrated against estimates from multicompartment models that consider variability in FFM composition

  7. Predicting the flexure response of wood-plastic composites from uni-axial and shear data using a finite-element model (United States)

    Scott E. Hamel; John C. Hermanson; Steven M. Cramer


    Wood-plastic composites (WPCs), commonly used in residential decks and railings, exhibit mechanical behavior that is bimodal, anisotropic, and nonlinear viscoelastic. They exhibit different stress-strain responses to tension and compression, both of which are nonlinear. Their mechanical properties vary with respect to extrusion direction, their deformation under...

  8. Predicting the Chemical composition of herbaceous legumes using ...

    African Journals Online (AJOL)

    Predicting the Chemical composition of herbaceous legumes using Near Infrared Reflectance Spectroscopy. J F Mupangwa, N Berardo, N T Ngongoni, J H Topps, H Hamudikuwanda, M Ordoardi. Abstract. (Journal of Applied Science in Southern Africa: 2000 6(2): 107-114).

  9. Development of an Integrated Moisture Index for predicting species composition (United States)

    Louis R. Iverson; Charles T. Scott; Martin E. Dale; Anantha Prasad


    A geographic information system (GIS) approach was used to develop an Integrated Moisture Index (IMI), which was used to predict species composition for Ohio forests. Several landscape features (a slope-aspect shading index, cumulative flow of water downslope, curvature of the landscape, and the water-holding capacity of the soil) were derived from elevation and soils...

  10. 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


    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...

  11. Preon representations and composite models

    International Nuclear Information System (INIS)

    Kang, Kyungsik


    This is a brief report on the preon models which are investigated by In-Gyu Koh, A. N. Schellekens and myself and based on complex, anomaly-free and asymptotically free representations of SU(3) to SU(8), SO(4N+2) and E 6 with no more than two different preons. Complete list of the representations that are complex anomaly-free and asymptotically free has been given by E. Eichten, I.-G. Koh and myself. The assumptions made about the ground state composites and the role of Fermi statistics to determine the metaflavor wave functions are discussed in some detail. We explain the method of decompositions of tensor products with definite permutation properties which has been developed for this purpose by I.-G. Koh, A.N. Schellekens and myself. An example based on an anomaly-free representation of the confining metacolor group SU(5) is discussed

  12. Towards Quantitative Spatial Models of Seabed Sediment Composition.

    Directory of Open Access Journals (Sweden)

    David Stephens

    Full Text Available There is a need for fit-for-purpose maps for accurately depicting the types of seabed substrate and habitat and the properties of the seabed for the benefits of research, resource management, conservation and spatial planning. The aim of this study is to determine whether it is possible to predict substrate composition across a large area of seabed using legacy grain-size data and environmental predictors. The study area includes the North Sea up to approximately 58.44°N and the United Kingdom's parts of the English Channel and the Celtic Seas. The analysis combines outputs from hydrodynamic models as well as optical remote sensing data from satellite platforms and bathymetric variables, which are mainly derived from acoustic remote sensing. We build a statistical regression model to make quantitative predictions of sediment composition (fractions of mud, sand and gravel using the random forest algorithm. The compositional data is analysed on the additive log-ratio scale. An independent test set indicates that approximately 66% and 71% of the variability of the two log-ratio variables are explained by the predictive models. A EUNIS substrate model, derived from the predicted sediment composition, achieved an overall accuracy of 83% and a kappa coefficient of 0.60. We demonstrate that it is feasible to spatially predict the seabed sediment composition across a large area of continental shelf in a repeatable and validated way. We also highlight the potential for further improvements to the method.

  13. QuaBingo: A Prediction System for Protein Quaternary Structure Attributes Using Block Composition

    Directory of Open Access Journals (Sweden)

    Chi-Hua Tung


    Full Text Available Background. Quaternary structures of proteins are closely relevant to gene regulation, signal transduction, and many other biological functions of proteins. In the current study, a new method based on protein-conserved motif composition in block format for feature extraction is proposed, which is termed block composition. Results. The protein quaternary assembly states prediction system which combines blocks with functional domain composition, called QuaBingo, is constructed by three layers of classifiers that can categorize quaternary structural attributes of monomer, homooligomer, and heterooligomer. The building of the first layer classifier uses support vector machines (SVM based on blocks and functional domains of proteins, and the second layer SVM was utilized to process the outputs of the first layer. Finally, the result is determined by the Random Forest of the third layer. We compared the effectiveness of the combination of block composition, functional domain composition, and pseudoamino acid composition of the model. In the 11 kinds of functional protein families, QuaBingo is 23% of Matthews Correlation Coefficient (MCC higher than the existing prediction system. The results also revealed the biological characterization of the top five block compositions. Conclusions. QuaBingo provides better predictive ability for predicting the quaternary structural attributes of proteins.

  14. Composite Linear Models | Division of Cancer Prevention (United States)

    By Stuart G. Baker The composite linear models software is a matrix approach to compute maximum likelihood estimates and asymptotic standard errors for models for incomplete multinomial data. It implements the method described in Baker SG. Composite linear models for incomplete multinomial data. Statistics in Medicine 1994;13:609-622. The software includes a library of thirty

  15. Model predictive control using fuzzy decision functions

    NARCIS (Netherlands)

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


    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

  16. Review of probabilistic models of the strength of composite materials

    International Nuclear Information System (INIS)

    Sutherland, L.S.; Guedes Soares, C.


    The available literature concerning probabilistic models describing the strength of composite materials has been reviewed to highlight the important aspects of this behaviour which will be of interest to the modelling and analysis of a complex system. The success with which these theories have been used to predict experimental results has been discussed. Since the brittle reinforcement phase largely controls the strength of composites, the probabilistic theories used to describe the strength of brittle materials, fibres and bundles of fibres have been detailed. The use of these theories to predict the strength of composite materials has been considered, along with further developments incorporating the damage accumulation observed in the failure of such materials. Probabilistic theories of the strength of short-fibre composites have been outlined. Emphasis has been placed throughout on straightforward engineering explanations of these theories and how they may be used, rather than providing comprehensive statistical descriptions

  17. Service life prediction and fibre reinforced cementitious composites

    DEFF Research Database (Denmark)

    Stoklund Larsen, E.

    The present Ph.D.thesis addresses the service life concept on the fibre reinforced cementitious composites. The advantages and problems of adding fibre to a cementitious matrix and the influence on service life are described. In SBI Report 221, Service life prediction and cementitious somposites......, the factors affecting the pure cementitious composite are described. Different sizes and types of fibre reinforced crmentitious composites have been chosen to illustrate different ageing and deterioration mechanisms. Some ageing mechanisms can be accelerated and others cannot which is demonstrated in a test...... programme. Moisture, micro structural and mechanical properties were measured before, during and after ageing, with the purpose of giving a detailed "picture" of the materials during ageing....

  18. Prediction of strength of wood composite materials using ultrasonic

    International Nuclear Information System (INIS)

    Mahmoud, M.K.; Emam, A.


    Wood is a biological material integrating a very large variability of its mechanical properties (tensile and compressive), on the two directional longitudinal and transverse Ultrasonic method has been utilized to measure both wood physical and / or wood mechanical properties. The aim of this article is to show the development of ultrasonic technique for quality evaluation of trees, wood material and wood based composites. For quality assessment of these products we discuss the nondestructive evaluation of different factors such as: moisture content, temperature, biological degradation induced by bacterial attack and fungal attack. These techniques were adapted for trees, timber and wood based composites. The present study discusses the prediction of tensile and compressive strength of wood composite materials using ultrasonic testing. Empirical relationships between the tensile properties, compression strength and ultrasonic were proposed. The experimental results indicate the possibility of establishing a relationship between tensile strength and compression values. Moreover, the fractures in tensile and compressive are discussed by photographic

  19. The use of seemingly unrelated regression (SUR) to predict the carcass composition of lambs

    DEFF Research Database (Denmark)

    Cadavez, Vasco A. P.; Henningsen, Arne

    The aim of this study was to develop and evaluate models for predicting the carcass composition of lambs. Forty male lambs of two different breeds were included in our analysis. The lambs were slaughtered and their hot carcass weight was obtained. After cooling for 24 hours, the subcutaneous fat...

  20. The use of seemingly unrelated regression to predict the carcass composition of lambs

    DEFF Research Database (Denmark)

    Cadavez, V.A.P.; Henningsen, Arne


    The aim of this study was to develop and evaluate models for predicting the carcass composition of lambs. Forty male lambs were slaughtered and their carcasses were cooled for 24 hours. The subcutaneous fat thickness was measured between the 12th and 13th rib and breast bone tissue thickness...

  1. Composite Social Network for Predicting Mobile Apps Installation (United States)


    Social network tools (such as the Facebook app and the Twitter app) can observe users’ online friendship network . In this work, our key idea is...the friendship network from phones by collecting data from social networking apps such as the Facebook and Twitter apps. We summarize all the networks X iv :1 10 6. 03 59 v1 [ cs .S I] 2 J un 2 01 1 Composite Social Network for Predicting Mobile Apps Installation Wei Pan


    Directory of Open Access Journals (Sweden)



    Full Text Available The present study has investigated the finite element method (FEM techniques of composite beam subjected to combined axial tension and negative bending. The negative bending regions of composite beams are influenced by worsen failures due to various levels of axial tensile loads on steel section especially in the regions near internal supports. Three dimensional solid FEM model was developed to accurately predict the unfavourable phenomenon of cracking of concrete and compression of steel in the negative bending regions of composite beam due to axial tensile loads. The prediction of quasi-static solution was extensively analysed with various deformation speeds and energy stabilities. The FEM model was then validated with existing experimental data. Reasonable agreements were observed between the results of FEM model and experimental analysis in the combination of vertical-axial forces and failure modes on ultimate limit state behaviour. The local failure modes known as shear studs failure, excess yielding on steel beam and crushing on concrete were completely verified by extensive similarity between the numerical and experimental results. Finally, a proper way of modelling techniques for large FEM models by considering uncertainties of material behaviour due to biaxial loadings and complex contact interactions is discussed. Further, the model is suggested for the limit state prediction of composite beam with calibrating necessary degree of the combined axial loads.

  3. PVT characterization and viscosity modeling and prediction of crude oils

    DEFF Research Database (Denmark)

    Cisneros, Eduardo Salvador P.; Dalberg, Anders; Stenby, Erling Halfdan


    In previous works, the general, one-parameter friction theory (f-theory), models have been applied to the accurate viscosity modeling of reservoir fluids. As a base, the f-theory approach requires a compositional characterization procedure for the application of an equation of state (EOS), in most...... pressure, is also presented. The combination of the mass characterization scheme presented in this work and the f-theory, can also deliver accurate viscosity modeling results. Additionally, depending on how extensive the compositional characterization is, the approach,presented in this work may also...... deliver accurate viscosity predictions. The modeling approach presented in this work can deliver accurate viscosity and density modeling and prediction results over wide ranges of reservoir conditions, including the compositional changes induced by recovery processes such as gas injection....

  4. Modelling of Damage Evolution in Braided Composites: Recent Developments (United States)

    Wang, Chen; Roy, Anish; Silberschmidt, Vadim V.; Chen, Zhong


    Composites reinforced with woven or braided textiles exhibit high structural stability and excellent damage tolerance thanks to yarn interlacing. With their high stiffness-to-weight and strength-to-weight ratios, braided composites are attractive for aerospace and automotive components as well as sports protective equipment. In these potential applications, components are typically subjected to multi-directional static, impact and fatigue loadings. To enhance material analysis and design for such applications, understanding mechanical behaviour of braided composites and development of predictive capabilities becomes crucial. Significant progress has been made in recent years in development of new modelling techniques allowing elucidation of static and dynamic responses of braided composites. However, because of their unique interlacing geometric structure and complicated failure modes, prediction of damage initiation and its evolution in components is still a challenge. Therefore, a comprehensive literature analysis is presented in this work focused on a review of the state-of-the-art progressive damage analysis of braided composites with finite-element simulations. Recently models employed in the studies on mechanical behaviour, impact response and fatigue analyses of braided composites are presented systematically. This review highlights the importance, advantages and limitations of as-applied failure criteria and damage evolution laws for yarns and composite unit cells. In addition, this work provides a good reference for future research on FE simulations of braided composites.

  5. Finite elements modeling of delaminations in composite laminates

    DEFF Research Database (Denmark)

    Gaiotti, m.; Rizzo, C.M.; Branner, Kim


    of the buckling strength of composite laminates containing delaminations. Namely, non-linear buckling and post-buckling analyses are carried out to predict the critical buckling load of elementary composite laminates affected by rectangular delaminations of different sizes and locations, which are modelled......The application of composite materials in many structures poses to engineers the problem to create reliable and relatively simple methods, able to estimate the strength of multilayer composite structures. Multilayer composites, like other laminated materials, suffer from layer separation, i.......e., delaminations, which may affect the stiffness and stability of structural components. Especially deep delaminations in the mid surface of laminates are expected to reduce the effective flexural stiffness and lead to collapse, often due to buckling behaviour. This paper deals with the numerical modelling...

  6. Glass Transition Temperature- and Specific Volume- Composition Models for Tellurite Glasses

    Energy Technology Data Exchange (ETDEWEB)

    Riley, Brian J. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Vienna, John D. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)


    This report provides models for predicting composition-properties for tellurite glasses, namely specific gravity and glass transition temperature. Included are the partial specific coefficients for each model, the component validity ranges, and model fit parameters.

  7. Modeling strength loss in wood by chemical composition. Part I, An individual component model for southern pine (United States)

    J. E. Winandy; P. K. Lebow


    In this study, we develop models for predicting loss in bending strength of clear, straight-grained pine from changes in chemical composition. Although significant work needs to be done before truly universal predictive models are developed, a quantitative fundamental relationship between changes in chemical composition and strength loss for pine was demonstrated. In...

  8. A Local Composition Model for Paraffinic Solid Solutions

    DEFF Research Database (Denmark)

    Coutinho, A.P. João; Knudsen, Kim; Andersen, Simon Ivar


    The description of the solid-phase non-ideality remains the main obstacle in modelling the solid-liquid equilibrium of hydrocarbons. A theoretical model, based on the local composition concept, is developed for the orthorhombic phase of n-alkanes and tested against experimental data for binary sy...... systems. It is shown that it can adequately predict the experimental phase behaviour of paraffinic mixtures. This work extends the applicability of local composition models to the solid phase. Copyright (C) 1996 Elsevier Science Ltd....

  9. Modelling of volumetric composition and mechanical properties of unidirectional hemp/epoxy composites - Effect of enzymatic fibre treatment

    DEFF Research Database (Denmark)

    Liu, Ming; Thygesen, Anders; Meyer, Anne S.


    The objective of the present study is to assess the effect of enzymatic fibre treatments on the fibre performance in unidirectional hemp/epoxy composites by modelling the volumetric composition and mechanical properties of the composites. It is shown that the applied models can well predict...... the changes in volumetric composition and mechanical properties of the composites when differently treated hemp fibres are used. The decrease in the fibre correlated porosity factor with the enzymatic fibre treatments shows that the removal of pectin by pectinolytic enzymes results in a better fibre...

  10. Reliable prediction and determination of Norwegian lamb carcass composition and value

    International Nuclear Information System (INIS)

    Kongsro, Jørgen


    The main objective of this work was to study prediction and determination of Norwegian lamb carcass composition with different techniques spanning from subjective appraisal to computer-intensive methods. There is an increasing demand, both from farmers and processors of meats, for a more objective and reliable system for prediction of muscle (lean meat), fat, bone and value of a lamb carcass. When introducing new technologies for determination of lamb carcass composition, the reference method used for calibration must be precise and reliable. The precision and reliability of the current dissection reference for lamb carcass classification and grading has never been quantified. A poor reference method will not benefit even the most optimal system for prediction and determination of lamb carcasses. To help achieve reliable systems, the uncertainty or errors in the reference method and measuring systems needs to be quantified. Using proper calibration methods for the measuring systems, the uncertainty and modeling power can be determined for lamb carcasses. The results of the work presented in this thesis show that the current classification system using subjective appraisal (EUROP) is reliable; however the accuracy with respect to carcass composition, especially for lean meat or muscle and carcass value, is poor. The reference method used for determining lamb carcass composition with respect to lamb carcass classification and grading is precise and reliable for carcass composition. For the composition and yield of sub-primal cuts, the reliability varied, and was especially poor for the breast cut. Further attention is needed for jointing and cutting of sub-primals to achieve even higher precision and reliability of the reference method. As an alternative to butcher or manual dissection, Computer Tomography (CT) showed promising results with respect to prediction of lamb carcass composition. This method is nicknamed “virtual dissection”. By utilizing the

  11. 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.


    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

  12. Limitations of CT in the prediction of gallstone composition

    International Nuclear Information System (INIS)

    Brink, J.A.; Mueller, P.R.; Simeone, J.F.; Prien, E.L.; Saini, S.; Tung, G.; Ferrucci, J.T.


    Gallstones from 87 patients were harvested at cholecystectomy and selected for thin-section specimen CT and crystallographic analysis. A peripheral rim of density different from the center of the stone was noted in 32 (37%) of 87. The relative attenuations (HU) of the stone rim and stone core were recorded as the difference in attenuation from that of the distilled water bath. The correlation of these measurements with the percent cholesterol content in both the rim and core were poor (r = .57, 0.34, respectively). This paper reports the predictive ability of CT examined by defining predominantly cholesterol stones as having a relative core HV ≥ (62 of 87) and core cholesterol content ≥ 80% (59 of 87). Ct correctly predicted the composition as predominantly cholesterol or pigment in 68 of 87 (78%)

  13. Mechanical Model Development for Composite Structural Supercapacitors (United States)

    Ricks, Trenton M.; Lacy, Thomas E., Jr.; Santiago, Diana; Bednarcyk, Brett A.


    Novel composite structural supercapacitor concepts have recently been developed as a means both to store electrical charge and to provide modest mechanical load carrying capability. Double-layer composite supercapacitors are often fabricated by impregnating a woven carbon fiber fabric, which serves as the electrodes, with a structural polymer electrolyte. Polypropylene or a glass fabric is often used as the separator material. Recent research has been primarily limited to evaluating these composites experimentally. In this study, mechanical models based on the Multiscale Generalized Method of Cells (MSGMC) were developed and used to calculate the shear and tensile properties and response of two composite structural supercapacitors from the literature. The modeling approach was first validated against traditional composite laminate data. MSGMC models for composite supercapacitors were developed, and accurate elastic shear/tensile properties were obtained. It is envisioned that further development of the models presented in this work will facilitate the design of composite components for aerospace and automotive applications and can be used to screen candidate constituent materials for inclusion in future composite structural supercapacitor concepts.

  14. Modelling anisotropic water transport in polymer composite

    Indian Academy of Sciences (India)

    This work reports anisotropic water transport in a polymer composite consisting of an epoxy matrix reinforced with aligned triangular bars made of vinyl ester. By gravimetric experiments, water diffusion in resin and polymer composites were characterized. Parameters for Fickian diffusion and polymer relaxation models were ...

  15. Genome-Wide Prediction of DNA Methylation Using DNA Composition and Sequence Complexity in Human. (United States)

    Wu, Chengchao; Yao, Shixin; Li, Xinghao; Chen, Chujia; Hu, Xuehai


    DNA methylation plays a significant role in transcriptional regulation by repressing activity. Change of the DNA methylation level is an important factor affecting the expression of target genes and downstream phenotypes. Because current experimental technologies can only assay a small proportion of CpG sites in the human genome, it is urgent to develop reliable computational models for predicting genome-wide DNA methylation. Here, we proposed a novel algorithm that accurately extracted sequence complexity features (seven features) and developed a support-vector-machine-based prediction model with integration of the reported DNA composition features (trinucleotide frequency and GC content, 65 features) by utilizing the methylation profiles of embryonic stem cells in human. The prediction results from 22 human chromosomes with size-varied windows showed that the 600-bp window achieved the best average accuracy of 94.7%. Moreover, comparisons with two existing methods further showed the superiority of our model, and cross-species predictions on mouse data also demonstrated that our model has certain generalization ability. Finally, a statistical test of the experimental data and the predicted data on functional regions annotated by ChromHMM found that six out of 10 regions were consistent, which implies reliable prediction of unassayed CpG sites. Accordingly, we believe that our novel model will be useful and reliable in predicting DNA methylation.

  16. Iowa calibration of MEPDG performance prediction models. (United States)


    This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement : performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 : representative p...

  17. A study of composite models at LEP with ALEPH

    International Nuclear Information System (INIS)

    Badaud, F.


    Tests of composite models are performed in e + e - collisions in the vicinity of the Z 0 pole using the ALEPH detector. Two kinds of substructure effects are searched for: deviations of differential cross section for reactions e + e - → l + l - and e + e - → γ γ from standard model predictions, and direct search for excited neutrino. A new interaction, parametrized by a 4-fermion contact term, cell, is studied in lepton pair production reactions, assuming different chiralities of the currents. Lower limits on the compositeness scale Λ are obtained by fitting model predictions to the data. They are in the range from 1 to a few TeV depending on model and lepton flavour. Researches for the lightest excited particle that could be the excited neutrino, are presented

  18. Model complexity control for hydrologic prediction

    NARCIS (Netherlands)

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


    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

  19. Modelling anisotropic water transport in polymer composite ...

    Indian Academy of Sciences (India)

    Parameters for Fickian diffusion and polymer relaxation models were determined by .... Water transport process of resin and polymer composite specimens at ..... simulation. ... Kwon Y W and Bang H 1997 Finite element method using matlab.

  20. Text Comprehension Mediates Morphological Awareness, Syntactic Processing, and Working Memory in Predicting Chinese Written Composition Performance (United States)

    Guan, Connie Qun; Ye, Feifei; Wagner, Richard K.; Meng, Wanjin; Leong, Che Kan


    The goal of the present study was to test opposing views about four issues concerning predictors of individual differences in Chinese written composition: (a) Whether morphological awareness, syntactic processing, and working memory represent distinct and measureable constructs in Chinese or are just manifestations of general language ability; (b) whether they are important predictors of Chinese written composition, and if so, the relative magnitudes and independence of their predictive relations; (c) whether observed predictive relations are mediated by text comprehension; and (d) whether these relations vary or are developmentally invariant across three years of writing development. Based on analyses of the performance of students in grades 4 (n = 246), 5 (n = 242) and 6 (n = 261), the results supported morphological awareness, syntactic processing, and working memory as distinct yet correlated abilities that made independent contributions to predicting Chinese written composition, with working memory as the strongest predictor. However, predictive relations were mediated by text comprehension. The final model accounted for approximately 75 percent of the variance in Chinese written composition. The results were largely developmentally invariant across the three grades from which participants were drawn. PMID:25530630

  1. 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


    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

  2. Mathematical methods and models in composites

    CERN Document Server

    Mantic, Vladislav


    This book provides a representative selection of the most relevant, innovative, and useful mathematical methods and models applied to the analysis and characterization of composites and their behaviour on micro-, meso-, and macroscale. It establishes the fundamentals for meaningful and accurate theoretical and computer modelling of these materials in the future. Although the book is primarily concerned with fibre-reinforced composites, which have ever-increasing applications in fields such as aerospace, many of the results presented can be applied to other kinds of composites. The topics cover

  3. Evaluation of the MMCLIFE 3.0 code in predicting crack growth in titanium aluminide composites

    International Nuclear Information System (INIS)

    Harmon, D.; Larsen, J.M.


    Crack growth and fatigue life predictions made with the MMCLIFE 3.0 code are compared to test data for unidirectional, continuously reinforced SCS-6/Ti-14Al-21Nb (wt pct) composite laminates. The MMCLIFE 3.0 analysis package is a design tool capable of predicting strength and fatigue performance in metal matrix composite (MMC) laminates. The code uses a combination of micromechanic lamina and macromechanic laminate analyses to predict stresses and uses linear elastic fracture mechanics to predict crack growth. The crack growth analysis includes a fiber bridging model to predict the growth of matrix flaws in 0 degree laminates and is capable of predicting the effects of interfacial shear stress and thermal residual stresses. The code has also been modified to include edge-notch flaws in addition to center-notch flaws. The model was correlated with constant amplitude, isothermal data from crack growth tests conducted on 0- and 90 degree SCS-6/Ti-14-21 laminates. Spectrum fatigue tests were conducted, which included dwell times and frequency effects. Strengths and areas for improvement for the analysis are discussed

  4. Nonlinear chaotic model for predicting storm surges

    Directory of Open Access Journals (Sweden)

    M. Siek


    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.

  5. Honey bee success predicted by landscape composition in Ohio, USA. (United States)

    Sponsler, D B; Johnson, R M


    Foraging honey bees (Apis mellifera L.) can routinely travel as far as several kilometers from their hive in the process of collecting nectar and pollen from floral patches within the surrounding landscape. Since the availability of floral resources at the landscape scale is a function of landscape composition, apiculturists have long recognized that landscape composition is a critical determinant of honey bee colony success. Nevertheless, very few studies present quantitative data relating colony success metrics to local landscape composition. We employed a beekeeper survey in conjunction with GIS-based landscape analysis to model colony success as a function of landscape composition in the State of Ohio, USA, a region characterized by intensive cropland, urban development, deciduous forest, and grassland. We found that colony food accumulation and wax production were positively related to cropland and negatively related to forest and grassland, a pattern that may be driven by the abundance of dandelion and clovers in agricultural areas compared to forest or mature grassland. Colony food accumulation was also negatively correlated with urban land cover in sites dominated by urban and agricultural land use, which does not support the popular opinion that the urban environment is more favorable to honey bees than cropland.

  6. Honey bee success predicted by landscape composition in Ohio, USA

    Directory of Open Access Journals (Sweden)

    DB Sponsler


    Full Text Available Foraging honey bees (Apis mellifera L. can routinely travel as far as several kilometers from their hive in the process of collecting nectar and pollen from floral patches within the surrounding landscape. Since the availability of floral resources at the landscape scale is a function of landscape composition, apiculturists have long recognized that landscape composition is a critical determinant of honey bee colony success. Nevertheless, very few studies present quantitative data relating colony success metrics to local landscape composition. We employed a beekeeper survey in conjunction with GIS-based landscape analysis to model colony success as a function of landscape composition in the State of Ohio, USA, a region characterized by intensive cropland, urban development, deciduous forest, and grassland. We found that colony food accumulation and wax production were positively related to cropland and negatively related to forest and grassland, a pattern that may be driven by the abundance of dandelion and clovers in agricultural areas compared to forest or mature grassland. Colony food accumulation was also negatively correlated with urban land cover in sites dominated by urban and agricultural land use, which does not support the popular opinion that the urban environment is more favorable to honey bees than cropland.

  7. 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

  8. Predictive user modeling with actionable attributes

    NARCIS (Netherlands)

    Zliobaite, I.; Pechenizkiy, M.


    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



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


    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...

  10. Regression methodology in groundwater composition estimation with composition predictions for Romuvaara borehole KR10

    Energy Technology Data Exchange (ETDEWEB)

    Luukkonen, A.; Korkealaakso, J.; Pitkaenen, P. [VTT Communities and Infrastructure, Espoo (Finland)


    Teollisuuden Voima Oy selected five investigation areas for preliminary site studies (1987Ae1992). The more detailed site investigation project, launched at the beginning of 1993 and presently supervised by Posiva Oy, is concentrated to three investigation areas. Romuvaara at Kuhmo is one of the present target areas, and the geochemical, structural and hydrological data used in this study are extracted from there. The aim of the study is to develop suitable methods for groundwater composition estimation based on a group of known hydrogeological variables. The input variables used are related to the host type of groundwater, hydrological conditions around the host location, mixing potentials between different types of groundwater, and minerals equilibrated with the groundwater. The output variables are electrical conductivity, Ca, Mg, Mn, Na, K, Fe, Cl, S, HS, SO{sub 4}, alkalinity, {sup 3}H, {sup 14}C, {sup 13}C, Al, Sr, F, Br and I concentrations, and pH of the groundwater. The methodology is to associate the known hydrogeological conditions (i.e. input variables), with the known water compositions (output variables), and to evaluate mathematical relations between these groups. Output estimations are done with two separate procedures: partial least squares regressions on the principal components of input variables, and by training neural networks with input-output pairs. Coefficients of linear equations and trained networks are optional methods for actual predictions. The quality of output predictions are monitored with confidence limit estimations, evaluated from input variable covariances and output variances, and with charge balance calculations. Groundwater compositions in Romuvaara borehole KR10 are predicted at 10 metre intervals with both prediction methods. 46 refs.

  11. Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition. (United States)

    Hayat, Maqsood; Khan, Asifullah


    Membrane proteins are vital type of proteins that serve as channels, receptors, and energy transducers in a cell. Prediction of membrane protein types is an important research area in bioinformatics. Knowledge of membrane protein types provides some valuable information for predicting novel example of the membrane protein types. However, classification of membrane protein types can be both time consuming and susceptible to errors due to the inherent similarity of membrane protein types. In this paper, neural networks based membrane protein type prediction system is proposed. Composite protein sequence representation (CPSR) is used to extract the features of a protein sequence, which includes seven feature sets; amino acid composition, sequence length, 2 gram exchange group frequency, hydrophobic group, electronic group, sum of hydrophobicity, and R-group. Principal component analysis is then employed to reduce the dimensionality of the feature vector. The probabilistic neural network (PNN), generalized regression neural network, and support vector machine (SVM) are used as classifiers. A high success rate of 86.01% is obtained using SVM for the jackknife test. In case of independent dataset test, PNN yields the highest accuracy of 95.73%. These classifiers exhibit improved performance using other performance measures such as sensitivity, specificity, Mathew's correlation coefficient, and F-measure. The experimental results show that the prediction performance of the proposed scheme for classifying membrane protein types is the best reported, so far. This performance improvement may largely be credited to the learning capabilities of neural networks and the composite feature extraction strategy, which exploits seven different properties of protein sequences. The proposed Mem-Predictor can be accessed at Copyright © 2010 Elsevier Ltd. All rights reserved.

  12. Robust predictions of the interacting boson model

    International Nuclear Information System (INIS)

    Casten, R.F.; Koeln Univ.


    While most recognized for its symmetries and algebraic structure, the IBA model has other less-well-known but equally intrinsic properties which give unavoidable, parameter-free predictions. These predictions concern central aspects of low-energy nuclear collective structure. This paper outlines these ''robust'' predictions and compares them with the data

  13. Comparison of Prediction-Error-Modelling Criteria

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay


    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...

  14. Prediction of mechanical properties of composites of HDPE/HA/EAA. (United States)

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


    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.

  15. A model for simulation of coupled microstructural and compositional evolution

    International Nuclear Information System (INIS)

    Tikare, Veena; Homer, Eric R.; Holm, Elizabeth A.


    The formation, transport and segregation of components in nuclear fuels fundamentally control their behavior, performance, longevity and safety. Most nuclear fuels enter service with a uniform composition consisting of a single phase with two or three components. Fission products form, introducing more components. The segregation and transport of the components is complicated by the underlying microstructure consisting of grains, pores, bubbles and more, which is evolving under temperature gradients during service. As they evolve, components and microstructural features interact such that composition affects microstructure and vice versa. The ability to predict the interdependent compositional and microstructural evolution in 3D as a function of burn-up would greatly improve the ability to design safe, high burn-up nuclear fuels. We present a model that combines elements of Potts Monte Carlo, MC, and the phase-field model to treat coupled microstructural-compositional evolution. This hybrid model uses an equation of state, EOS, based on the microstructural state and the composition. The microstructural portion uses the traditional MC EOS and the compositional portion uses the phase-field EOS: E hyb = N Σ i=1 (E v (q i ,C)+1/2 n Σ j=1 J(q i ,q j )) + ∫κ c (∇C) 2 dV. E v is the bulk free energy of each site i and J is the bond energy between neighboring sites i and j; thus, this term defines the microstructural interfacial energy. The last term is the compositional interfacial energy as defined in the traditional phase-field model. Evolution of coupled microstructure-composition is simulated by minimizing free energy in a path dependent manner. This model will be presented and will be demonstrated by applying it to evolution of nuclear fuels during service. (author)

  16. Extracting falsifiable predictions from sloppy models. (United States)

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


    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.

  17. The prediction of epidemics through mathematical modeling. (United States)

    Schaus, Catherine


    Mathematical models may be resorted to in an endeavor to predict the development of epidemics. The SIR model is one of the applications. Still too approximate, the use of statistics awaits more data in order to come closer to reality.

  18. Calibration of PMIS pavement performance prediction models. (United States)


    Improve the accuracy of TxDOTs existing pavement performance prediction models through calibrating these models using actual field data obtained from the Pavement Management Information System (PMIS). : Ensure logical performance superiority patte...

  19. Multiscale Modeling of Ceramic Matrix Composites (United States)

    Bednarcyk, Brett A.; Mital, Subodh K.; Pineda, Evan J.; Arnold, Steven M.


    Results of multiscale modeling simulations of the nonlinear response of SiC/SiC ceramic matrix composites are reported, wherein the microstructure of the ceramic matrix is captured. This micro scale architecture, which contains free Si material as well as the SiC ceramic, is responsible for residual stresses that play an important role in the subsequent thermo-mechanical behavior of the SiC/SiC composite. Using the novel Multiscale Generalized Method of Cells recursive micromechanics theory, the microstructure of the matrix, as well as the microstructure of the composite (fiber and matrix) can be captured.

  20. Evaluating Predictive Uncertainty of Hyporheic Exchange Modelling (United States)

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


    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

  1. Compositional Modelling of Stochastic Hybrid Systems

    NARCIS (Netherlands)

    Strubbe, S.N.


    In this thesis we present a modelling framework for compositional modelling of stochastic hybrid systems. Hybrid systems consist of a combination of continuous and discrete dynamics. The state space of a hybrid system is hybrid in the sense that it consists of a continuous component and a discrete

  2. Predicting invertebrate assemblage composition from harvesting pressure and environmental characteristics on tropical reef flats (United States)

    Jimenez, H.; Dumas, P.; Ponton, D.; Ferraris, J.


    Invertebrates represent an essential component of coral reef ecosystems; they are ecologically important and a major resource, but their assemblages remain largely unknown, particularly on Pacific islands. Understanding their distribution and building predictive models of community composition as a function of environmental variables therefore constitutes a key issue for resource management. The goal of this study was to define and classify the main environmental factors influencing tropical invertebrate distributions in New Caledonian reef flats and to test the resulting predictive model. Invertebrate assemblages were sampled by visual counting during 2 years and 2 seasons, then coupled to different environmental conditions (habitat composition, hydrodynamics and sediment characteristics) and harvesting status (MPA vs. non-MPA and islets vs. coastal flats). Environmental conditions were described by a principal component analysis (PCA), and contributing variables were selected. Permutational analysis of variance (PERMANOVA) was used to test the effects of different factors (status, flat, year and season) on the invertebrate assemblage composition. Multivariate regression trees (MRT) were then used to hierarchically classify the effects of environmental and harvesting variables. MRT model explained at least 60% of the variation in structure of invertebrate communities. Results highlighted the influence of status (MPA vs. non-MPA) and location (islet vs. coastal flat), followed by habitat composition, organic matter content, hydrodynamics and sampling year. Predicted assemblages defined by indicator families were very different for each environment-exploitation scenario and correctly matched a calibration data matrix. Predictions from MRT including both environmental variables and harvesting pressure can be useful for management of invertebrates in coral reef environments.

  3. Case studies in archaeological predictive modelling

    NARCIS (Netherlands)

    Verhagen, Jacobus Wilhelmus Hermanus Philippus


    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

  4. Prediction of composite fatigue life under variable amplitude loading using artificial neural network trained by genetic algorithm (United States)

    Rohman, Muhamad Nur; Hidayat, Mas Irfan P.; Purniawan, Agung


    Neural networks (NN) have been widely used in application of fatigue life prediction. In the use of fatigue life prediction for polymeric-base composite, development of NN model is necessary with respect to the limited fatigue data and applicable to be used to predict the fatigue life under varying stress amplitudes in the different stress ratios. In the present paper, Multilayer-Perceptrons (MLP) model of neural network is developed, and Genetic Algorithm was employed to optimize the respective weights of NN for prediction of polymeric-base composite materials under variable amplitude loading. From the simulation result obtained with two different composite systems, named E-glass fabrics/epoxy (layups [(±45)/(0)2]S), and E-glass/polyester (layups [90/0/±45/0]S), NN model were trained with fatigue data from two different stress ratios, which represent limited fatigue data, can be used to predict another four and seven stress ratios respectively, with high accuracy of fatigue life prediction. The accuracy of NN prediction were quantified with the small value of mean square error (MSE). When using 33% from the total fatigue data for training, the NN model able to produce high accuracy for all stress ratios. When using less fatigue data during training (22% from the total fatigue data), the NN model still able to produce high coefficient of determination between the prediction result compared with obtained by experiment.

  5. Dust Composition in Climate Models: Current Status and Prospects (United States)

    Pérez García-Pando, C.; Miller, R. L.; Perlwitz, J. P.; Kok, J. F.; Scanza, R.; Mahowald, N. M.


    Mineral dust created by wind erosion of soil particles is the dominant aerosol by mass in the atmosphere. It exerts significant effects on radiative fluxes, clouds, ocean biogeochemistry, and human health. Models that predict the lifecycle of mineral dust aerosols generally assume a globally uniform mineral composition. However, this simplification limits our understanding of the role of dust in the Earth system, since the effects of dust strongly depend on the particles' physical and chemical properties, which vary with their mineral composition. Hence, not only a detailed understanding of the processes determining the dust emission flux is needed, but also information about its size dependent mineral composition. Determining the mineral composition of dust aerosols is complicated. The largest uncertainty derives from the current atlases of soil mineral composition. These atlases provide global estimates of soil mineral fractions, but they are based upon massive extrapolation of a limited number of soil samples assuming that mineral composition is related to soil type. This disregards the potentially large variability of soil properties within each defined soil type. In addition, the analysis of these soil samples is based on wet sieving, a technique that breaks the aggregates found in the undisturbed parent soil. During wind erosion, these aggregates are subject to partial fragmentation, which generates differences on the size distribution and composition between the undisturbed parent soil and the emitted dust aerosols. We review recent progress on the representation of the mineral and chemical composition of dust in climate models. We discuss extensions of brittle fragmentation theory to prescribe the emitted size-resolved dust composition, and we identify key processes and uncertainties based upon model simulations and an unprecedented compilation of observations.

  6. Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database. (United States)

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


    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.

  7. Silkworm cocoons inspire models for random fiber and particulate composites

    Energy Technology Data Exchange (ETDEWEB)

    Fujia, Chen; Porter, David; Vollrath, Fritz [Department of Zoology, University of Oxford, Oxford OX1 3PS (United Kingdom)


    The bioengineering design principles evolved in silkworm cocoons make them ideal natural prototypes and models for structural composites. Cocoons depend for their stiffness and strength on the connectivity of bonding between their constituent materials of silk fibers and sericin binder. Strain-activated mechanisms for loss of bonding connectivity in cocoons can be translated directly into a surprisingly simple yet universal set of physically realistic as well as predictive quantitative structure-property relations for a wide range of technologically important fiber and particulate composite materials.

  8. A composite computational model of liver glucose homeostasis. I. Building the composite model. (United States)

    Hetherington, J; Sumner, T; Seymour, R M; Li, L; Rey, M Varela; Yamaji, S; Saffrey, P; Margoninski, O; Bogle, I D L; Finkelstein, A; Warner, A


    A computational model of the glucagon/insulin-driven liver glucohomeostasis function, focusing on the buffering of glucose into glycogen, has been developed. The model exemplifies an 'engineering' approach to modelling in systems biology, and was produced by linking together seven component models of separate aspects of the physiology. The component models use a variety of modelling paradigms and degrees of simplification. Model parameters were determined by an iterative hybrid of fitting to high-scale physiological data, and determination from small-scale in vitro experiments or molecular biological techniques. The component models were not originally designed for inclusion within such a composite model, but were integrated, with modification, using our published modelling software and computational frameworks. This approach facilitates the development of large and complex composite models, although, inevitably, some compromises must be made when composing the individual models. Composite models of this form have not previously been demonstrated.

  9. Component Composition Using Feature Models

    DEFF Research Database (Denmark)

    Eichberg, Michael; Klose, Karl; Mitschke, Ralf


    interface description languages. If this variability is relevant when selecting a matching component then human interaction is required to decide which components can be bound. We propose to use feature models for making this variability explicit and (re-)enabling automatic component binding. In our...... approach, feature models are one part of service specifications. This enables to declaratively specify which service variant is provided by a component. By referring to a service's variation points, a component that requires a specific service can list the requirements on the desired variant. Using...... these specifications, a component environment can then determine if a binding of the components exists that satisfies all requirements. The prototypical environment Columbus demonstrates the feasibility of the approach....

  10. Compositional models for credal sets

    Czech Academy of Sciences Publication Activity Database

    Vejnarová, Jiřina


    Roč. 90, č. 1 (2017), s. 359-373 ISSN 0888-613X R&D Projects: GA ČR(CZ) GA16-12010S Institutional support: RVO:67985556 Keywords : Imprecise probabilities * Credal sets * Multidimensional models * Conditional independence Subject RIV: BA - General Mathematics OBOR OECD: Pure mathematics Impact factor: 2.845, year: 2016

  11. A conjugate thermo-electric model for a composite medium.

    Directory of Open Access Journals (Sweden)

    Oscar Chávez

    Full Text Available Electrical transmission signals have been used for decades to characterize the internal structure of composite materials. We theoretically analyze the transmission of an electrical signal through a composite material which consists of two phases with different chemical compositions. We assume that the temperature of the biphasic system increases as a result of Joule heating and its electrical resistivity varies linearly with temperature; this last consideration leads to simultaneously study the electrical and thermal effects. We propose a nonlinear conjugate thermo-electric model, which is solved numerically to obtain the current density and temperature profiles for each phase. We study the effect of frequency, resistivities and thermal conductivities on the current density and temperature. We validate the prediction of the model with comparisons with experimental data obtained from rock characterization tests.

  12. Incorporating uncertainty in predictive species distribution modelling. (United States)

    Beale, Colin M; Lennon, Jack J


    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.

  13. 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...

  14. Multi-scale modeling of composites

    DEFF Research Database (Denmark)

    Azizi, Reza

    A general method to obtain the homogenized response of metal-matrix composites is developed. It is assumed that the microscopic scale is sufficiently small compared to the macroscopic scale such that the macro response does not affect the micromechanical model. Therefore, the microscopic scale......-Mandel’s energy principle is used to find macroscopic operators based on micro-mechanical analyses using the finite element method under generalized plane strain condition. A phenomenologically macroscopic model for metal matrix composites is developed based on constitutive operators describing the elastic...... to plastic deformation. The macroscopic operators found, can be used to model metal matrix composites on the macroscopic scale using a hierarchical multi-scale approach. Finally, decohesion under tension and shear loading is studied using a cohesive law for the interface between matrix and fiber....

  15. The Composite OLAP-Object Data Model

    Energy Technology Data Exchange (ETDEWEB)

    Pourabbas, Elaheh; Shoshani, Arie


    In this paper, we define an OLAP-Object model that combines the main characteristics of OLAP and Object data models in order to achieve their functionalities in a common framework. We classify three different object classes: primitive, regular and composite. Then, we define a query language which uses the path concept in order to facilitate data navigation and data manipulation. The main feature of the proposed language is an anchor. It allows us to fix dynamically an object class (primitive, regular or composite) along the paths over the OLAP-Object data model for expressing queries. The queries can be formulated on objects, composite objects and combination of both. The power of the proposed query language is investigated through multiple query examples. The semantic of different clauses and syntax of the proposed language are investigated.

  16. Evaluating the Predictive Value of Growth Prediction Models (United States)

    Murphy, Daniel L.; Gaertner, Matthew N.


    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…

  17. Flavor and CP invariant composite Higgs models

    International Nuclear Information System (INIS)

    Redi, Michele; Weiler, Andreas


    The flavor protection in composite Higgs models with partial compositeness is known to be insufficient. We explore the possibility to alleviate the tension with CP odd observables by assuming that flavor or CP are symmetries of the composite sector, broken by the coupling to Standard Model fields. One realization is that the composite sector has a flavor symmetry SU(3) or SU(3) U x SU(3) D which allows us to realize Minimal Flavor Violation. We show how to avoid the previously problematic tension between a flavor symmetric composite sector and electro-weak precision tests. Some of the light quarks are substantially or even fully composite with striking signals at the LHC. We discuss the constraints from recent dijet mass measurements and give an outlook on the discovery potential. We also present a different protection mechanism where we separate the generation of flavor hierarchies and the origin of CP violation. This can eliminate or safely reduce unwanted CP violating effects, realizing effectively ''Minimal CP Violation'' and is compatible with a dynamical generation of flavor at low scales. (orig.)

  18. Flavor and CP invariant composite Higgs models

    Energy Technology Data Exchange (ETDEWEB)

    Redi, Michele [CERN - European Organization for Nuclear Research, Geneva (Switzerland). Theory Div.; INFN, Firenze (Italy); Weiler, Andreas [CERN - European Organization for Nuclear Research, Geneva (Switzerland). Theory Div.; Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)


    The flavor protection in composite Higgs models with partial compositeness is known to be insufficient. We explore the possibility to alleviate the tension with CP odd observables by assuming that flavor or CP are symmetries of the composite sector, broken by the coupling to Standard Model fields. One realization is that the composite sector has a flavor symmetry SU(3) or SU(3){sub U} x SU(3){sub D} which allows us to realize Minimal Flavor Violation. We show how to avoid the previously problematic tension between a flavor symmetric composite sector and electro-weak precision tests. Some of the light quarks are substantially or even fully composite with striking signals at the LHC. We discuss the constraints from recent dijet mass measurements and give an outlook on the discovery potential. We also present a different protection mechanism where we separate the generation of flavor hierarchies and the origin of CP violation. This can eliminate or safely reduce unwanted CP violating effects, realizing effectively ''Minimal CP Violation'' and is compatible with a dynamical generation of flavor at low scales. (orig.)

  19. A fiber bridging model for fatigue delamination in composite materials

    International Nuclear Information System (INIS)

    Gregory, Jeremy R.; Spearing, S. Mark


    A fiber bridging model has been created to examine the effects of bridging on Mode I delamination fatigue fracture in a carbon fiber polymer-matrix composite. The model uses a cohesive zone law that is derived from quasi-static R-curves to determine the bridging energy applied in the bridged region. Timoshenko beam theory and an iterative self-consistent scheme are used to calculate the bridging tractions and displacements. After applying the bridging model to crack propagation data the scatter in the data was significantly reduced and clear trends were observed as a function of temperature that were not apparent previously. This indicated that the model appropriately accounted for the bridging in the experiments. Scanning electron microscopy crack opening displacement measurements were performed to validate the model's predictions. The measurements showed that the predictions were close to the actual bridging levels in the specimen

  20. Consolidation modelling for thermoplastic composites forming simulation (United States)

    Xiong, H.; Rusanov, A.; Hamila, N.; Boisse, P.


    Pre-impregnated thermoplastic composites are widely used in the aerospace industry for their excellent mechanical properties, Thermoforming thermoplastic prepregs is a fast manufacturing process, the automotive industry has shown increasing interest in this manufacturing processes, in which the reconsolidation is an essential stage. The model of intimate contact is investigated as the consolidation model, compression experiments have been launched to identify the material parameters, several numerical tests show the influents of the temperature and pressure applied during processing. Finally, a new solid-shell prismatic element has been presented for the simulation of consolidation step in the thermoplastic composites forming process.

  1. Model predictive control classical, robust and stochastic

    CERN Document Server

    Kouvaritakis, Basil


    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...

  2. Applications of a composite model of microstructural evolution

    International Nuclear Information System (INIS)

    Stoller, R.E.


    Near-term fusion reactors will have to be designed using radiation effects data from experiments conducted in fast fission reactors. These fast reactors generate atomic displacements at a rate similar to that expected in a DT fusion reactor first wall. However, the transmutant helium production in an austenitic stainless steel first wall will exceed that in fast reactor fuel cladding by about a factor of 30. Hence, the use of the fast reactor data will involve some extrapolation. A major goal of this work is to develop theoretical models of microstructural evolution to aid in this extrapolation. In the present work a detailed rate-theory-based model of microstructural evolution under fast neutron irradiation has been developed. The prominent new aspect of this model is a treatment of dislocation evolution in which Frank faulted loops nucleate, grow and unfault to provide a source for network dislocations while the dislocation network can be simultaneously annihilated by a climb/glide process. The predictions of this model compare very favorably with the observed dose and temperature dependence of these key microstructural features over a broad range. In addition, this new description of dislocation evolution has been coupled with a previously developed model of cavity evolution and good agreement has been obtained between the predictions of the composite model and fast reactor swelling data. The results from the composite model also reveal that the various components of the irradiation-induced microstructure evolve in a highly coupled manner. The predictions of the composite model are more sensitive to parametric variations than more simple models. Hence, its value as a tool in data analysis and extrapolation is enhanced

  3. Modeling, robust and distributed model predictive control for freeway networks

    NARCIS (Netherlands)

    Liu, S.


    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

  4. Deep Predictive Models in Interactive Music


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


    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...

  5. Design and performance prediction of a new generation adsorption chiller using composite adsorbent

    International Nuclear Information System (INIS)

    Gong, L.X.; Wang, R.Z.; Xia, Z.Z.; Chen, C.J.


    Research highlights: → Composite adsorbent 'employing lithium chloride in silica gel' and water as working pair. → A new type adsorbent bed is used to accommodate the composite adsorbent. → A dynamic model of the adsorption chiller is built. → The coefficient of performance (COP) and the cooling capacity will be improved. -- Abstract: This paper presents a novel adsorption chiller using composite adsorbent 'employing lithium chloride in silica gel' as adsorbent and water as adsorbate. A new type adsorbent bed is used to accommodate the composite adsorbent. The mass recovery between two adsorbent beds usually results in the adsorbate unbalance. So a novel auto water makeup unite is used to solve the problem. A dynamic model of the adsorption chiller is built based on the adsorption isotherms to predict the performance. The simulation result shows that the coefficient of performance (COP) and the cooling capacity will increase by using this new composite adsorbent. When the temperatures of hot water inlet, cooling water inlet, and chilled water inlet are 363, 303 and 293 K, COP will be 0.43, and the cooling capacity will be 5.295 kW. Also operation strategy is optimized. Different temperatures of hot water inlet, cooling water inlet and chilling water inlet will result in different COP and cooling capacity.

  6. Fabrication and modelling of 3-3 piezoelectric composites

    Energy Technology Data Exchange (ETDEWEB)

    Perry, Andrew John


    Three-dimensional modelling of a 3-3 piezoelectric structure was carried out using ANSYS finite element modelling software. Hydrophone figures of merit were calculated for structures with increasing amounts of interconnecting porosity. In addition to air being the second phase, polymer fillers were added to the three dimensional model in order to observe the effect of polymer Young's modulus and Poisson's ratio on the piezoelectric response of the composite material. Results show that increasing the porosity has the effect of improving the hydrostatic piezoelectric properties for applications such as low frequency hydrophones. The optimum amount of porosity depends on the figure of merit to be maximised. In order to validate model predictions, porous piezoelectric structures were fabricated by either the BurPS (Burnt out Polymer Spheres) method or polymer foam reticulation. Corresponding measurements of piezoelectric coefficients were carried out on the porous samples. Experimental results confirmed finite element modelling predictions. PZT-porosity composites and PZT-polymer composites were produced exhibiting superior hydrostatic strain constant (d{sub h}), hydrostatic voltage constant (g{sub h}) and hydrostatic figure of merit (d{sub h}g{sub h}) compared to that of dense PZT. (author)

  7. Composite dark matter from a model with composite Higgs boson

    International Nuclear Information System (INIS)

    Khlopov, Maxim Yu.; Kouvaris, Chris


    In a previous paper [Phys. Rev. D77, 065002 (2008)], we showed how the minimal walking technicolor model can provide a composite dark matter candidate, by forming bound states between a -2 electrically charged techniparticle and a 4 He ++ . We studied the properties of these techni-O-helium tOHe''atoms,'' which behave as warmer dark matter rather than cold. In this paper, we extend our work on several different aspects. We study the possibility of a mixed scenario where both tOHe and bound states between +2 and -2 electrically charged techniparticles coexist in the dark matter density. We argue that these newly proposed bound states are solely made of techniparticles, although they behave as weakly interacting massive particles, due to their large elastic cross section with nuclei, can only account for a small percentage of the dark matter density. Therefore, we conclude that within the minimal walking technicolor model, composite dark matter should be mostly composed of tOHe. Moreover, in this paper, we put cosmological bounds in the masses of the techniparticles, if they compose the dark matter density. Finally, we propose within this setup, a possible explanation of the discrepancy between the DAMA/NaI and DAMA/LIBRA findings and the negative results of CDMS and other direct dark matter searches that imply nuclear recoil measurement, which should accompany ionization.

  8. Host Genome Influence on Gut Microbial Composition and Microbial Prediction of Complex Traits in Pigs. (United States)

    Camarinha-Silva, Amelia; Maushammer, Maria; Wellmann, Robin; Vital, Marius; Preuss, Siegfried; Bennewitz, Jörn


    The aim of the present study was to analyze the interplay between gastrointestinal tract (GIT) microbiota, host genetics, and complex traits in pigs using extended quantitative-genetic methods. The study design consisted of 207 pigs that were housed and slaughtered under standardized conditions, and phenotyped for daily gain, feed intake, and feed conversion rate. The pigs were genotyped with a standard 60 K SNP chip. The GIT microbiota composition was analyzed by 16S rRNA gene amplicon sequencing technology. Eight from 49 investigated bacteria genera showed a significant narrow sense host heritability, ranging from 0.32 to 0.57. Microbial mixed linear models were applied to estimate the microbiota variance for each complex trait. The fraction of phenotypic variance explained by the microbial variance was 0.28, 0.21, and 0.16 for daily gain, feed conversion, and feed intake, respectively. The SNP data and the microbiota composition were used to predict the complex traits using genomic best linear unbiased prediction (G-BLUP) and microbial best linear unbiased prediction (M-BLUP) methods, respectively. The prediction accuracies of G-BLUP were 0.35, 0.23, and 0.20 for daily gain, feed conversion, and feed intake, respectively. The corresponding prediction accuracies of M-BLUP were 0.41, 0.33, and 0.33. Thus, in addition to SNP data, microbiota abundances are an informative source of complex trait predictions. Since the pig is a well-suited animal for modeling the human digestive tract, M-BLUP, in addition to G-BLUP, might be beneficial for predicting human predispositions to some diseases, and, consequently, for preventative and personalized medicine. Copyright © 2017 by the Genetics Society of America.

  9. Impact damages modeling in laminated composite structures

    Directory of Open Access Journals (Sweden)

    Kreculj Dragan D.


    Full Text Available Laminated composites have an important application in modern engineering structures. They are characterized by extraordinary properties, such as: high strength and stiffness and lightweight. Nevertheless, a serious obstacle to more widespread use of those materials is their sensitivity to the impact loads. Impacts cause initiation and development of certain types of damages. Failures that occur in laminated composite structures can be intralaminar and interlaminar. To date it was developed a lot of simulation models for impact damages analysis in laminates. Those models can replace real and expensive testing in laminated structures with a certain accuracy. By using specialized software the damage parameters and distributions can be determined (at certain conditions on laminate structures. With performing numerical simulation of impact on composite laminates there are corresponding results valid for the analysis of these structures.

  10. Numerical method for the prediction of bending properties of glass-epoxy composites

    Directory of Open Access Journals (Sweden)

    Stamenović Marina R.


    Full Text Available Mechanical properties of composite materials are conditioned by their structure and depend on the characteristics of structural components. In this paper is presented a numerical model by which the bending properties can be predicted on the basis of known mechanical properties of tension and pressure. Determining the relationship between these properties is justified having in mind the mechanics of fracture during bending, where the fracture takes place on the outer layer which is subjected to bending while the break ends on the layer subjected to pressure. The paper gives the values of tension, pressure and bending properties obtained by the corresponding mechanical test. A comparison of the numerical results of bending properties obtained on the basis of the model with the experimental ones, shows their satisfactory agreement. Therefore, this model can be used for some future research to predict bending properties without experiments.

  11. In vitro/in silico investigation of failure criteria to predict flexural strength of composite resins. (United States)

    Yamaguchi, Satoshi; Mehdawi, Idris Mohamed; Sakai, Takahiko; Abe, Tomohiro; Inoue, Sayuri; Imazato, Satoshi


    The aim of this study was to investigate a failure criterion to predict flexural strengths of composite resins (CR) by three-dimensional finite element analysis (3D-FEA). Models of flexural strength for test specimens of CR and rods comprising a three-point loading were designed. Calculation of Young's moduli and Poisson's ratios of CR were conducted using a modified McGee-McCullough model. Using the experimental CR, flexural strengths were measured by three-point bending tests with crosshead speed 1.0 mm/min and compared with the values determined by in silico analysis. The flexural strengths of experimental CR calculated using the maximum principal strain significantly correlated with those obtained in silico amongst the four types of failure criteria applied. The in silico analytical model established in this study was found to be effective to predict the flexural strengths of CR incorporating various silica filler contents by maximum principal strain.

  12. Unreachable Setpoints in Model Predictive Control

    DEFF Research Database (Denmark)

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


    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...

  13. Bayesian Predictive Models for Rayleigh Wind Speed

    DEFF Research Database (Denmark)

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


    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....

  14. Predictive Modelling and Time: An Experiment in Temporal Archaeological Predictive Models


    David Ebert


    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...

  15. Fingerprint verification prediction model in hand dermatitis. (United States)

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


    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.

  16. Massive Predictive Modeling using Oracle R Enterprise

    CERN Multimedia

    CERN. Geneva


    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...

  17. Multi-model analysis in hydrological prediction (United States)

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


    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

  18. Using physicochemical and compositional characteristics of DNA sequence for prediction of genomic signals

    KAUST Repository

    Mulamba, Pierre Abraham


    The challenge in finding genes in eukaryotic organisms using computational methods is an ongoing problem in the biology. Based on various genomic signals found in eukaryotic genomes, this problem can be divided into many different sub­‐problems such as identification of transcription start sites, translation initiation sites, splice sites, poly (A) signals, etc. Each sub-­problem deals with a particular type of genomic signals and various computational methods are used to solve each sub-­problem. Aggregating information from all these individual sub-­problems can lead to a complete annotation of a gene and its component signals. The fundamental principle of most of these computational methods is the mapping principle – building an input-­output model for the prediction of a particular genomic signal based on a set of known input signals and their corresponding output signal. The type of input signals used to build the model is an essential element in most of these computational methods. The common factor of most of these methods is that they are mainly based on the statistical analysis of the basic nucleotide sequence string composition. 4 Our study is based on a novel approach to predict genomic signals in which uniquely generated structural profiles that combine compressed physicochemical properties with topological and compositional properties of DNA sequences are used to develop machine learning predictive models. The compression of the physicochemical properties is made using principal component analysis transformation. Our ideas are evaluated through prediction models of canonical splice sites using support vector machine models. We demonstrate across several species that the proposed methodology has resulted in the most accurate splice site predictors that are publicly available or described. We believe that the approach in this study is quite general and has various applications in other biological modeling problems.

  19. Prostate Cancer Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  20. Colorectal Cancer Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  1. Esophageal Cancer Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  2. Bladder Cancer Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  3. Lung Cancer Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  4. Breast Cancer Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  5. Pancreatic Cancer Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  6. Ovarian Cancer Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  7. Liver Cancer Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  8. Testicular Cancer Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  9. Cervical Cancer Risk Prediction Models (United States)

    Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  10. Modeling and Prediction Using Stochastic Differential Equations

    DEFF Research Database (Denmark)

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


    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...

  11. Using saturation water percentage data to predict mechanical composition of soils

    International Nuclear Information System (INIS)

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


    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

  12. Predicting bioactive glass properties from the molecular chemical composition: glass transition temperature. (United States)

    O'Donnell, Matthew D


    The glass transition temperature (T(g)) of inorganic glasses is an important parameter than can be used to correlate with other glass properties, such as dissolution rate, which governs in vitro and in vivo bioactivity. Seven bioactive glass compositional series reported in the literature (77 in total) were analysed here with T(g) values obtained by a number of different methods: differential thermal analysis, differential scanning calorimetry and dilatometry. An iterative least-squares fitting method was used to correlate T(g) from thermal analysis of these compositions with the levels of individual oxide and fluoride components in the glasses. When all seven series were fitted a reasonable correlation was found between calculated and experimental values (R(2)=0.89). When the two compositional series that were designed in weight percentages (the remaining five were designed in molar percentage) were removed from the model an improved fit was achieved (R(2)=0.97). This study shows that T(g) for a wide range in compositions (e.g. SiO(2) content of 37.3-68.4 mol.%) can be predicted to reasonable accuracy enabling processing parameters to be predicted such as annealing, fibre-drawing and sintering temperatures. Copyright © 2011 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

  13. Finite element modelling of composite castellated beam

    Directory of Open Access Journals (Sweden)

    Frans Richard


    Full Text Available Nowadays, castellated beam becomes popular in building structural as beam members. This is due to several advantages of castellated beam such as increased depth without any additional mass, passing the underfloor service ducts without changing of story elevation. However, the presence of holes can develop various local effects such as local buckling, lateral torsional buckling caused by compression force at the flange section of the steel beam. Many studies have investigated the failure mechanism of castellated beam and one technique which can prevent the beam fall into local failure is the use of reinforced concrete slab as lateral support on castellated beam, so called composite castellated beam. Besides of preventing the local failure of castellated beam, the concrete slab can increase the plasticity moment of the composite castellated beam section which can deliver into increasing the ultimate load of the beam. The aim of this numerical studies of composite castellated beam on certain loading condition (monotonic quasi-static loading. ABAQUS was used for finite element modelling purpose and compared with the experimental test for checking the reliability of the model. The result shows that the ultimate load of the composite castellated beam reached 6.24 times than the ultimate load of the solid I beam and 1.2 times compared the composite beam.

  14. Predictive Model of Systemic Toxicity (SOT) (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 ...

  15. Predicting the Composition of Red Wine Blends Using an Array of Multicomponent Peptide-Based Sensors

    Directory of Open Access Journals (Sweden)

    Eman Ghanem


    Full Text Available Differential sensing using synthetic receptors as mimics of the mammalian senses of taste and smell is a powerful approach for the analysis of complex mixtures. Herein, we report on the effectiveness of a cross-reactive, supramolecular, peptide-based sensing array in differentiating and predicting the composition of red wine blends. Fifteen blends of Cabernet Sauvignon, Merlot and Cabernet Franc, in addition to the mono varietals, were used in this investigation. Linear Discriminant Analysis (LDA showed a clear differentiation of blends based on tannin concentration and composition where certain mono varietals like Cabernet Sauvignon seemed to contribute less to the overall characteristics of the blend. Partial Least Squares (PLS Regression and cross validation were used to build a predictive model for the responses of the receptors to eleven binary blends and the three mono varietals. The optimized model was later used to predict the percentage of each mono varietal in an independent test set composted of four tri-blends with a 15% average error. A partial least square regression model using the mouth-feel and taste descriptive sensory attributes of the wine blends revealed a strong correlation of the receptors to perceived astringency, which is indicative of selective binding to polyphenols in wine.

  16. Predicting the composition of red wine blends using an array of multicomponent Peptide-based sensors. (United States)

    Ghanem, Eman; Hopfer, Helene; Navarro, Andrea; Ritzer, Maxwell S; Mahmood, Lina; Fredell, Morgan; Cubley, Ashley; Bolen, Jessica; Fattah, Rabia; Teasdale, Katherine; Lieu, Linh; Chua, Tedmund; Marini, Federico; Heymann, Hildegarde; Anslyn, Eric V


    Differential sensing using synthetic receptors as mimics of the mammalian senses of taste and smell is a powerful approach for the analysis of complex mixtures. Herein, we report on the effectiveness of a cross-reactive, supramolecular, peptide-based sensing array in differentiating and predicting the composition of red wine blends. Fifteen blends of Cabernet Sauvignon, Merlot and Cabernet Franc, in addition to the mono varietals, were used in this investigation. Linear Discriminant Analysis (LDA) showed a clear differentiation of blends based on tannin concentration and composition where certain mono varietals like Cabernet Sauvignon seemed to contribute less to the overall characteristics of the blend. Partial Least Squares (PLS) Regression and cross validation were used to build a predictive model for the responses of the receptors to eleven binary blends and the three mono varietals. The optimized model was later used to predict the percentage of each mono varietal in an independent test set composted of four tri-blends with a 15% average error. A partial least square regression model using the mouth-feel and taste descriptive sensory attributes of the wine blends revealed a strong correlation of the receptors to perceived astringency, which is indicative of selective binding to polyphenols in wine.

  17. Composite model for quarks and leptons

    International Nuclear Information System (INIS)

    Harari, H.


    We discuss the motivation for constructing composite models for quarks and leptons, the hopes we have for a successful model and the difficulties encountered, so far, in this field. This paper corresponds to the contents of lectures given at the SLAC Summer Institute (August 1982), at the DESY Workshop on ''Electroweak Interactions at High Energies'' (September 1982) and at the Solvay Conference at the University of Texas, Austin, Texas (November 1982). (author)

  18. 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


    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...

  19. Spent fuel: prediction model development

    International Nuclear Information System (INIS)

    Almassy, M.Y.; Bosi, D.M.; Cantley, D.A.


    The need for spent fuel disposal performance modeling stems from a requirement to assess the risks involved with deep geologic disposal of spent fuel, and to support licensing and public acceptance of spent fuel repositories. Through the balanced program of analysis, diagnostic testing, and disposal demonstration tests, highlighted in this presentation, the goal of defining risks and of quantifying fuel performance during long-term disposal can be attained

  20. Navy Recruit Attrition Prediction Modeling (United States)


    have high correlation with attrition, such as age, job characteristics, command climate, marital status, behavior issues prior to recruitment, and the...the additive model. glm(formula = Outcome ~ Age + Gender + Marital + AFQTCat + Pay + Ed + Dep, family = binomial, data = ltraining) Deviance ...0.1 ‘ ‘ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance : 105441 on 85221 degrees of freedom Residual deviance

  1. Predicting and Modeling RNA Architecture (United States)

    Westhof, Eric; Masquida, Benoît; Jossinet, Fabrice


    SUMMARY A general approach for modeling the architecture of large and structured RNA molecules is described. The method exploits the modularity and the hierarchical folding of RNA architecture that is viewed as the assembly of preformed double-stranded helices defined by Watson-Crick base pairs and RNA modules maintained by non-Watson-Crick base pairs. Despite the extensive molecular neutrality observed in RNA structures, specificity in RNA folding is achieved through global constraints like lengths of helices, coaxiality of helical stacks, and structures adopted at the junctions of helices. The Assemble integrated suite of computer tools allows for sequence and structure analysis as well as interactive modeling by homology or ab initio assembly with possibilities for fitting within electronic density maps. The local key role of non-Watson-Crick pairs guides RNA architecture formation and offers metrics for assessing the accuracy of three-dimensional models in a more useful way than usual root mean square deviation (RMSD) values. PMID:20504963

  2. Predictions of the electro-mechanical response of conductive CNT-polymer composites (United States)

    Matos, Miguel A. S.; Tagarielli, Vito L.; Baiz-Villafranca, Pedro M.; Pinho, Silvestre T.


    We present finite element simulations to predict the conductivity, elastic response and strain-sensing capability of conductive composites comprising a polymeric matrix and carbon nanotubes. Realistic representative volume elements (RVE) of the microstructure are generated and both constituents are modelled as linear elastic solids, with resistivity independent of strain; the electrical contact between nanotubes is represented by a new element which accounts for quantum tunnelling effects and captures the sensitivity of conductivity to separation. Monte Carlo simulations are conducted and the sensitivity of the predictions to RVE size is explored. Predictions of modulus and conductivity are found in good agreement with published results. The strain-sensing capability of the material is explored for multiaxial strain states.

  3. Predictive Models and Computational Toxicology (II IBAMTOX) (United States)

    EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

  4. Finding furfural hydrogenation catalysts via predictive modelling

    NARCIS (Netherlands)

    Strassberger, Z.; Mooijman, M.; Ruijter, E.; Alberts, A.H.; Maldonado, A.G.; Orru, R.V.A.; Rothenberg, G.


    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


    African Journals Online (AJOL)

    FINITE ELEMENT MODEL FOR PREDICTING RESIDUAL STRESSES IN ... the transverse residual stress in the x-direction (σx) had a maximum value of 375MPa ... the finite element method are in fair agreement with the experimental results.

  6. Evaluation of CASP8 model quality predictions

    KAUST Repository

    Cozzetto, Domenico; Kryshtafovych, Andriy; Tramontano, Anna


    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

  7. Composite dark matter from a model with composite Higgs boson

    DEFF Research Database (Denmark)

    Yu. Khlopov, Maxim; Kouvaris, Christoforos


    In a previous paper \\cite{Khlopov:2007ic}, we showed how the minimal walking technicolor model (WTC) can provide a composite dark matter candidate, by forming bound states between a -2 electrically charged techniparticle and a $^4He^{++}$. We studied the properties of these \\emph......{techni-O-helium} $tOHe$ "atoms", which behave as warmer dark matter rather than cold. In this paper we extend our work on several different aspects. We study the possibility of a mixed scenario where both $tOHe$ and bound states between +2 and -2 electrically charged techniparticles coexist in the dark matter density....... We argue that these newly proposed bound states solely made of techniparticles, although they behave as Weakly Interacting Massive Particles (WIMPs), due to their large elastic cross section with nuclei, can only account for a small percentage of the dark matter density. Therefore we conclude...

  8. Mental models accurately predict emotion transitions. (United States)

    Thornton, Mark A; Tamir, Diana I


    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.

  9. Mental models accurately predict emotion transitions (United States)

    Thornton, Mark A.; Tamir, Diana I.


    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

  10. Modelling hydrodynamic parameters to predict flow assisted corrosion

    International Nuclear Information System (INIS)

    Poulson, B.; Greenwell, B.; Chexal, B.; Horowitz, J.


    During the past 15 years, flow assisted corrosion has been a worldwide problem in the power generating industry. The phenomena is complex and depends on environment, material composition, and hydrodynamic factors. Recently, modeling of flow assisted corrosion has become a subject of great importance. A key part of this effort is modeling the hydrodynamic aspects of this issue. This paper examines which hydrodynamic parameter should be used to correlate the occurrence and rate of flow assisted corrosion with physically meaningful parameters, discusses ways of measuring the relevant hydrodynamic parameter, and describes how the hydrodynamic data is incorporated into the predictive model

  11. Return Predictability, Model Uncertainty, and Robust Investment

    DEFF Research Database (Denmark)

    Lukas, Manuel

    Stock return predictability is subject to great uncertainty. In this paper we use the model confidence set approach to quantify uncertainty about expected utility from investment, accounting for potential return predictability. For monthly US data and six representative return prediction models, we...... find that confidence sets are very wide, change significantly with the predictor variables, and frequently include expected utilities for which the investor prefers not to invest. The latter motivates a robust investment strategy maximizing the minimal element of the confidence set. The robust investor...... allocates a much lower share of wealth to stocks compared to a standard investor....

  12. Model predictive Controller for Mobile Robot


    Alireza Rezaee


    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...

  13. Minimal composite Higgs models at the LHC (United States)

    Carena, Marcela; Da Rold, Leandro; Pontón, Eduardo


    We consider composite Higgs models where the Higgs is a pseudo-Nambu Goldstone boson arising from the spontaneous breaking of an approximate global symmetry by some underlying strong dynamics. We focus on the SO(5) → SO(4) symmetry breaking pattern, assuming the "partial compositeness" paradigm. We study the consequences on Higgs physics of the fermionic representations produced by the strong dynamics, that mix with the Standard Model (SM) degrees of freedom. We consider models based on the lowest-dimensional representations of SO(5) that allow for the custodial protection of the coupling, i.e. the 5, 10 and 14. We find a generic suppression of the gluon fusion process, while the Higgs branching fractions can be enhanced or suppressed compared to the SM. Interestingly, a precise measurement of the Higgs boson couplings can distinguish between different realizations in the fermionic sector, thus providing crucial information about the nature of the UV dynamics.

  14. Minimal composite Higgs models at the LHC

    International Nuclear Information System (INIS)

    Carena, Marcela; Rold, Leandro Da; Pontón, Eduardo


    We consider composite Higgs models where the Higgs is a pseudo-Nambu Goldstone boson arising from the spontaneous breaking of an approximate global symmetry by some underlying strong dynamics. We focus on the SO(5)→SO(4) symmetry breaking pattern, assuming the “partial compositeness" paradigm. We study the consequences on Higgs physics of the fermionic representations produced by the strong dynamics, that mix with the Standard Model (SM) degrees of freedom. We consider models based on the lowest-dimensional representations of SO(5) that allow for the custodial protection of the Zb-barb coupling, i.e. the 5, 10 and 14. We find a generic suppression of the gluon fusion process, while the Higgs branching fractions can be enhanced or suppressed compared to the SM. Interestingly, a precise measurement of the Higgs boson couplings can distinguish between different realizations in the fermionic sector, thus providing crucial information about the nature of the UV dynamics.

  15. Spatial Economics Model Predicting Transport Volume

    Directory of Open Access Journals (Sweden)

    Lu Bo


    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.

  16. Accuracy assessment of landslide prediction models

    International Nuclear Information System (INIS)

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


    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

  17. Simple theoretical models for composite rotor blades (United States)

    Valisetty, R. R.; Rehfield, L. W.


    The development of theoretical rotor blade structural models for designs based upon composite construction is discussed. Care was exercised to include a member of nonclassical effects that previous experience indicated would be potentially important to account for. A model, representative of the size of a main rotor blade, is analyzed in order to assess the importance of various influences. The findings of this model study suggest that for the slenderness and closed cell construction considered, the refinements are of little importance and a classical type theory is adequate. The potential of elastic tailoring is dramatically demonstrated, so the generality of arbitrary ply layup in the cell wall is needed to exploit this opportunity.

  18. 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

  19. Predictive Validation of an Influenza Spread Model (United States)

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


    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

  20. Prediction of Mold Spoilage for Soy/Polyethylene Composite Fibers

    Directory of Open Access Journals (Sweden)

    Chinmay Naphade


    Full Text Available Mold spoilage was determined over 109 days on soy/PE fibers held under controlled temperatures (T ranging from 10°C to 40°C and water activities (aw from 0.11 to 0.98. Water activities were created in sealed containers using saturated salt solutions and placed in temperature-controlled incubators. Soy/PE fibers that were held at 0.823 aw or higher exhibited mold growth at all temperatures. As postulated, increased water activity (greater than 0.89 and temperature (higher than 25°C accelerated mold growth on soy/PE fibers. A slower mold growth was observed on soy/PE fibers that were held at 0.87 aw and 10°C. A Weibull model was employed to fit the observed logarithmic values of T, aw, and an interaction term log⁡T×log⁡aw and was chosen as the final model as it gave the best fit to the raw mold growth data. These growth models predict the expected mold-free storage period of soy/PE fibers when exposed to various environmental temperatures and humidities.

  1. Statistical Models for Inferring Vegetation Composition from Fossil Pollen (United States)

    Paciorek, C.; McLachlan, J. S.; Shang, Z.


    Fossil pollen provide information about vegetation composition that can be used to help understand how vegetation has changed over the past. However, these data have not traditionally been analyzed in a way that allows for statistical inference about spatio-temporal patterns and trends. We build a Bayesian hierarchical model called STEPPS (Spatio-Temporal Empirical Prediction from Pollen in Sediments) that predicts forest composition in southern New England, USA, over the last two millenia based on fossil pollen. The critical relationships between abundances of tree taxa in the pollen record and abundances in actual vegetation are estimated using modern (Forest Inventory Analysis) data and (witness tree) data from colonial records. This gives us two time points at which both pollen and direct vegetation data are available. Based on these relationships, and incorporating our uncertainty about them, we predict forest composition using fossil pollen. We estimate the spatial distribution and relative abundances of tree species and draw inference about how these patterns have changed over time. Finally, we describe ongoing work to extend the modeling to the upper Midwest of the U.S., including an approach to infer tree density and thereby estimate the prairie-forest boundary in Minnesota and Wisconsin. This work is part of the PalEON project, which brings together a team of ecosystem modelers, paleoecologists, and statisticians with the goal of reconstructing vegetation responses to climate during the last two millenia in the northeastern and midwestern United States. The estimates from the statistical modeling will be used to assess and calibrate ecosystem models that are used to project ecological changes in response to global change.

  2. Finding Furfural Hydrogenation Catalysts via Predictive Modelling. (United States)

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


    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.

  3. Corporate prediction models, ratios or regression analysis?

    NARCIS (Netherlands)

    Bijnen, E.J.; Wijn, M.F.C.M.


    The models developed in the literature with respect to the prediction of a company s failure are based on ratios. It has been shown before that these models should be rejected on theoretical grounds. Our study of industrial companies in the Netherlands shows that the ratios which are used in

  4. Predicting Protein Secondary Structure with Markov Models

    DEFF Research Database (Denmark)

    Fischer, Paul; Larsen, Simon; Thomsen, Claus


    we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown protein sequences as sheets, helices or coils. We show how to exploit the directional information contained...... in the Markov model for this task. Classifications that are purely based on statistical models might not always be biologically meaningful. We present combinatorial methods to incorporate biological background knowledge to enhance the prediction performance....

  5. Modelling of a multi-temperature plasma composition

    International Nuclear Information System (INIS)

    Liani, B.; Benallal, R.; Bentalha, Z.


    Knowledge of plasma composition is very important for various plasma applications and prediction of plasma properties. The authors use the Saha equation and Debye length equation to calculate the non-local thermodynamic-equilibrium plasma composition. It has been shown that the model to 2T with T representing the temperature (electron temperature and heavy-particle temperature) described by Chen and Han [J. Phys. D 32(1999)1711] can be applied for a mixture of gases, where each atomic species has its own temperature, but the model to 4T is more general because it can be applicable to temperatures distant enough of the heavy particles. This can occur in a plasma composed of big- or macro-molecules. The electron temperature T e varies in the range 8000∼20000 K at atmospheric pressure. (authors)

  6. Modelling low velocity impact induced damage in composite laminates (United States)

    Shi, Yu; Soutis, Constantinos


    The paper presents recent progress on modelling low velocity impact induced damage in fibre reinforced composite laminates. It is important to understand the mechanisms of barely visible impact damage (BVID) and how it affects structural performance. To reduce labour intensive testing, the development of finite element (FE) techniques for simulating impact damage becomes essential and recent effort by the composites research community is reviewed in this work. The FE predicted damage initiation and propagation can be validated by Non Destructive Techniques (NDT) that gives confidence to the developed numerical damage models. A reliable damage simulation can assist the design process to optimise laminate configurations, reduce weight and improve performance of components and structures used in aircraft construction.

  7. Energy based prediction models for building acoustics

    DEFF Research Database (Denmark)

    Brunskog, Jonas


    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....

  8. Comparative Study of Bancruptcy Prediction Models

    Directory of Open Access Journals (Sweden)

    Isye Arieshanti


    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%

  9. Resin infusion of large composite structures modeling and manufacturing process

    Energy Technology Data Exchange (ETDEWEB)

    Loos, A.C. [Michigan State Univ., Dept. of Mechanical Engineering, East Lansing, MI (United States)


    The resin infusion processes resin transfer molding (RTM), resin film infusion (RFI) and vacuum assisted resin transfer molding (VARTM) are cost effective techniques for the fabrication of complex shaped composite structures. The dry fibrous preform is placed in the mold, consolidated, resin impregnated and cured in a single step process. The fibrous performs are often constructed near net shape using highly automated textile processes such as knitting, weaving and braiding. In this paper, the infusion processes RTM, RFI and VARTM are discussed along with the advantages of each technique compared with traditional composite fabrication methods such as prepreg tape lay up and autoclave cure. The large number of processing variables and the complex material behavior during infiltration and cure make experimental optimization of the infusion processes costly and inefficient. Numerical models have been developed which can be used to simulate the resin infusion processes. The model formulation and solution procedures for the VARTM process are presented. A VARTM process simulation of a carbon fiber preform was presented to demonstrate the type of information that can be generated by the model and to compare the model predictions with experimental measurements. Overall, the predicted flow front positions, resin pressures and preform thicknesses agree well with the measured values. The results of the simulation show the potential cost and performance benefits that can be realized by using a simulation model as part of the development process. (au)

  10. Prediction Models for Dynamic Demand Response

    Energy Technology Data Exchange (ETDEWEB)

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


    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

  11. Probabilistic predictive modelling of carbon nanocomposites for medical implants design. (United States)

    Chua, Matthew; Chui, Chee-Kong


    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.

  12. Models of fragmentation with composite power laws (United States)

    Tavassoli, Z.; Rodgers, G. J.


    Some models for binary fragmentation are introduced in which a time dependent transition size produces two regions of fragment sizes above and below the transition size. In the first model we assume a fixed rate of fragmentation for the largest fragment and two different rates of fragmentation in the two regions of sizes above and below the transition size. The model is solved exactly in the long time limit to reveal stable time-invariant solutions for the fragment size and mass distributions. These solutions exhibit composite power law behaviours; power laws with two different exponents for fragments in smaller and larger regions. A special case of the model with no fragmentation in the smaller size region is also examined. Another model is also introduced which have three regions of fragment sizes with different rates of fragmentation. The similarities between the stable distributions in our models and composite power law distributions from experimental work on shock fragmentation of long thin glass rods and thick clay plates are discussed.

  13. An Experimental Simulation to Validate FEM to Predict Transverse Young’s Modulus of FRP Composites

    Directory of Open Access Journals (Sweden)

    V. S. Sai


    Full Text Available Finite element method finds application in the analysis of FRP composites due to its versatility in getting the solution for complex cases which are not possible by exact classical analytical approaches. The finite element result is questionable unless it is obtained from converged mesh and properly validated. In the present work specimens are prepared with metallic materials so that the arrangement of fibers is close to hexagonal packing in a matrix as similar arrangement in case of FRP is complex due to the size of fibers. Transverse Young’s moduli of these specimens are determined experimentally. Equivalent FE models are designed and corresponding transverse Young’s moduli are compared with the experimental results. It is observed that the FE values are in good agreement with the experimental results, thus validating FEM for predicting transverse modulus of FRP composites.

  14. Evaluation of CASP8 model quality predictions

    KAUST Repository

    Cozzetto, Domenico


    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.

  15. Finding Furfural Hydrogenation Catalysts via Predictive Modelling (United States)

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


    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

  16. Modeling Non-Linear Material Properties in Composite Materials (United States)


    Technical Report ARWSB-TR-16013 MODELING NON-LINEAR MATERIAL PROPERTIES IN COMPOSITE MATERIALS Michael F. Macri Andrew G...REPORT TYPE Technical 3. DATES COVERED (From - To) 4. TITLE AND SUBTITLE MODELING NON-LINEAR MATERIAL PROPERTIES IN COMPOSITE MATERIALS are increasingly incorporating composite materials into their design. Many of these systems subject the composites to environmental conditions

  17. Wind farm production prediction - The Zephyr model

    Energy Technology Data Exchange (ETDEWEB)

    Landberg, L. [Risoe National Lab., Wind Energy Dept., Roskilde (Denmark); Giebel, G. [Risoe National Lab., Wind Energy Dept., Roskilde (Denmark); Madsen, H. [IMM (DTU), Kgs. Lyngby (Denmark); Nielsen, T.S. [IMM (DTU), Kgs. Lyngby (Denmark); Joergensen, J.U. [Danish Meteorologisk Inst., Copenhagen (Denmark); Lauersen, L. [Danish Meteorologisk Inst., Copenhagen (Denmark); Toefting, J. [Elsam, Fredericia (DK); Christensen, H.S. [Eltra, Fredericia (Denmark); Bjerge, C. [SEAS, Haslev (Denmark)


    This report describes a project - funded by the Danish Ministry of Energy and the Environment - which developed a next generation prediction system called Zephyr. The Zephyr system is a merging between two state-of-the-art prediction systems: Prediktor of Risoe National Laboratory and WPPT of IMM at the Danish Technical University. The numerical weather predictions were generated by DMI's HIRLAM model. Due to technical difficulties programming the system, only the computational core and a very simple version of the originally very complex system were developed. The project partners were: Risoe, DMU, DMI, Elsam, Eltra, Elkraft System, SEAS and E2. (au)

  18. Evaluation and modeling of the eutectic composition of various drug-polyethylene glycol solid dispersions. (United States)

    Baird, Jared A; Taylor, Lynne S


    The purpose of this study was to gain a better understanding of which factors contribute to the eutectic composition of drug-polyethylene glycol (PEG) blends and to compare experimental values with predictions from the semi-empirical model developed by Lacoulonche et al. Eutectic compositions of various drug-PEG 3350 solid dispersions were predicted, assuming athermal mixing, and compared to experimentally determined eutectic points. The presence or absence of specific interactions between the drug and PEG 3350 were investigated using Fourier transform infrared (FT-IR) spectroscopy. The eutectic composition for haloperidol-PEG and loratadine-PEG solid dispersions was accurately predicted using the model, while predictions for aceclofenac-PEG and chlorpropamide-PEG were very different from those experimentally observed. Deviations in the model prediction from ideal behavior for the systems evaluated were confirmed to be due to the presence of specific interactions between the drug and polymer, as demonstrated by IR spectroscopy. Detailed analysis showed that the eutectic composition prediction from the model is interdependent on the crystal lattice energy of the drug compound (evaluated from the melting temperature and the heat of fusion) as well as the nature of the drug-polymer interactions. In conclusion, for compounds with melting points less than 200°C, the model is ideally suited for predicting the eutectic composition of systems where there is an absence of drug-polymer interactions.

  19. Model predictive controller design of hydrocracker reactors


    GÖKÇE, Dila


    This study summarizes the design of a Model Predictive Controller (MPC) in Tüpraş, İzmit Refinery Hydrocracker Unit Reactors. Hydrocracking process, in which heavy vacuum gasoil is converted into lighter and valuable products at high temperature and pressure is described briefly. Controller design description, identification and modeling studies are examined and the model variables are presented. WABT (Weighted Average Bed Temperature) equalization and conversion increase are simulate...

  20. An online model composition tool for system biology models. (United States)

    Coskun, Sarp A; Cicek, A Ercument; Lai, Nicola; Dash, Ranjan K; Ozsoyoglu, Z Meral; Ozsoyoglu, Gultekin


    There are multiple representation formats for Systems Biology computational models, and the Systems Biology Markup Language (SBML) is one of the most widely used. SBML is used to capture, store, and distribute computational models by Systems Biology data sources (e.g., the BioModels Database) and researchers. Therefore, there is a need for all-in-one web-based solutions that support advance SBML functionalities such as uploading, editing, composing, visualizing, simulating, querying, and browsing computational models. We present the design and implementation of the Model Composition Tool (Interface) within the PathCase-SB (PathCase Systems Biology) web portal. The tool helps users compose systems biology models to facilitate the complex process of merging systems biology models. We also present three tools that support the model composition tool, namely, (1) Model Simulation Interface that generates a visual plot of the simulation according to user's input, (2) iModel Tool as a platform for users to upload their own models to compose, and (3) SimCom Tool that provides a side by side comparison of models being composed in the same pathway. Finally, we provide a web site that hosts BioModels Database models and a separate web site that hosts SBML Test Suite models. Model composition tool (and the other three tools) can be used with little or no knowledge of the SBML document structure. For this reason, students or anyone who wants to learn about systems biology will benefit from the described functionalities. SBML Test Suite models will be a nice starting point for beginners. And, for more advanced purposes, users will able to access and employ models of the BioModels Database as well.

  1. Supervisory Model Predictive Control of the Heat Integrated Distillation Column

    DEFF Research Database (Denmark)

    Meyer, Kristian; Bisgaard, Thomas; Huusom, Jakob Kjøbsted


    This paper benchmarks a centralized control system based on model predictive control for the operation of the heat integrated distillation column (HIDiC) against a fully decentralized control system using the most complete column model currently available in the literature. The centralized control...... system outperforms the decentralized system, because it handles the interactions in the HIDiC process better. The integral absolute error (IAE) is reduced by a factor of 2 and a factor of 4 for control of the top and bottoms compositions, respectively....

  2. Development and Validation of a Constitutive Model for Dental Composites during the Curing Process (United States)

    Wickham Kolstad, Lauren

    Debonding is a critical failure of a dental composites used for dental restorations. Debonding of dental composites can be determined by comparing the shrinkage stress of to the debonding strength of the adhesive that bonds it to the tooth surface. It is difficult to measure shrinkage stress experimentally. In this study, finite element analysis is used to predict the stress in the composite during cure. A new constitutive law is presented that will allow composite developers to evaluate composite shrinkage stress at early stages in the material development. Shrinkage stress and shrinkage strain experimental data were gathered for three dental resins, Z250, Z350, and P90. Experimental data were used to develop a constitutive model for the Young's modulus as a function of time of the dental composite during cure. A Maxwell model, spring and dashpot in series, was used to simulate the composite. The compliance of the shrinkage stress device was also taken into account by including a spring in series with the Maxwell model. A coefficient of thermal expansion was also determined for internal loading of the composite by dividing shrinkage strain by time. Three FEA models are presented. A spring-disk model validates that the constitutive law is self-consistent. A quarter cuspal deflection model uses separate experimental data to verify that the constitutive law is valid. Finally, an axisymmetric tooth model is used to predict interfacial stresses in the composite. These stresses are compared to the debonding strength to check if the composite debonds. The new constitutive model accurately predicted cuspal deflection data. Predictions for interfacial bond stress in the tooth model compare favorably with debonding characteristics observed in practice for dental resins.

  3. Finite element modeling of small-scale tapered wood-laminated composite poles with biomimicry features (United States)

    Cheng Piao; Todd F. Shupe; R.C. Tang; Chung Y. Hse


    Tapered composite poles with biomimicry features as in bamboo are a new generation of wood laminated composite poles that may some day be considered as an alternative to solid wood poles that are widely used in the transmission and telecommunication fields. Five finite element models were developed with ANSYS to predict and assess the performance of five types of...

  4. 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.


    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,

  5. Multi-Model Ensemble Wake Vortex Prediction (United States)

    Koerner, Stephan; Holzaepfel, Frank; Ahmad, Nash'at N.


    Several multi-model ensemble methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model ensemble capability using their wake models. An overview of different multi-model ensemble methods and their feasibility for wake applications is presented. The methods include Reliability Ensemble Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.

  6. Risk terrain modeling predicts child maltreatment. (United States)

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


    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 © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  7. Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques. (United States)

    Alejo, Luz; Atkinson, John; Guzmán-Fierro, Víctor; Roeckel, Marlene


    Computational self-adapting methods (Support Vector Machines, SVM) are compared with an analytical method in effluent composition prediction of a two-stage anaerobic digestion (AD) process. Experimental data for the AD of poultry manure were used. The analytical method considers the protein as the only source of ammonia production in AD after degradation. Total ammonia nitrogen (TAN), total solids (TS), chemical oxygen demand (COD), and total volatile solids (TVS) were measured in the influent and effluent of the process. The TAN concentration in the effluent was predicted, this being the most inhibiting and polluting compound in AD. Despite the limited data available, the SVM-based model outperformed the analytical method for the TAN prediction, achieving a relative average error of 15.2% against 43% for the analytical method. Moreover, SVM showed higher prediction accuracy in comparison with Artificial Neural Networks. This result reveals the future promise of SVM for prediction in non-linear and dynamic AD processes. Graphical abstract ᅟ.


    Directory of Open Access Journals (Sweden)

    Raphael Silveira Amaro


    Full Text Available In the last decades, a remarkable number of models, variants from the Autoregressive Conditional Heteroscedastic family, have been developed and empirically tested, making extremely complex the process of choosing a particular model. This research aim to compare the predictive capacity, using the Model Confidence Set procedure, than five conditional heteroskedasticity models, considering eight different statistical probability distributions. The financial series which were used refers to the log-return series of the Bovespa index and the Dow Jones Industrial Index in the period between 27 October 2008 and 30 December 2014. The empirical evidences showed that, in general, competing models have a great homogeneity to make predictions, either for a stock market of a developed country or for a stock market of a developing country. An equivalent result can be inferred for the statistical probability distributions that were used.

  9. Alcator C-Mod predictive modeling

    International Nuclear Information System (INIS)

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


    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

  10. Modeling thrombin generation: plasma composition based approach. (United States)

    Brummel-Ziedins, Kathleen E; Everse, Stephen J; Mann, Kenneth G; Orfeo, Thomas


    Thrombin has multiple functions in blood coagulation and its regulation is central to maintaining the balance between hemorrhage and thrombosis. Empirical and computational methods that capture thrombin generation can provide advancements to current clinical screening of the hemostatic balance at the level of the individual. In any individual, procoagulant and anticoagulant factor levels together act to generate a unique coagulation phenotype (net balance) that is reflective of the sum of its developmental, environmental, genetic, nutritional and pharmacological influences. Defining such thrombin phenotypes may provide a means to track disease progression pre-crisis. In this review we briefly describe thrombin function, methods for assessing thrombin dynamics as a phenotypic marker, computationally derived thrombin phenotypes versus determined clinical phenotypes, the boundaries of normal range thrombin generation using plasma composition based approaches and the feasibility of these approaches for predicting risk.

  11. Modelling the predictive performance of credit scoring

    Directory of Open Access Journals (Sweden)

    Shi-Wei Shen


    Research purpose: The purpose of this empirical paper was to examine the predictive performance of credit scoring systems in Taiwan. Motivation for the study: Corporate lending remains a major business line for financial institutions. However, in light of the recent global financial crises, it has become extremely important for financial institutions to implement rigorous means of assessing clients seeking access to credit facilities. Research design, approach and method: Using a data sample of 10 349 observations drawn between 1992 and 2010, logistic regression models were utilised to examine the predictive performance of credit scoring systems. Main findings: A test of Goodness of fit demonstrated that credit scoring models that incorporated the Taiwan Corporate Credit Risk Index (TCRI, micro- and also macroeconomic variables possessed greater predictive power. This suggests that macroeconomic variables do have explanatory power for default credit risk. Practical/managerial implications: The originality in the study was that three models were developed to predict corporate firms’ defaults based on different microeconomic and macroeconomic factors such as the TCRI, asset growth rates, stock index and gross domestic product. Contribution/value-add: The study utilises different goodness of fits and receiver operator characteristics during the examination of the robustness of the predictive power of these factors.

  12. Predictions and Experimental Microstructural Characterization of High Strain Rate Failure Modes in Layered Aluminum Composites (United States)

    Khanikar, Prasenjit

    Different aluminum alloys can be combined, as composites, for tailored dynamic applications. Most investigations pertaining to metallic alloy layered composites, however, have been based on quasi-static approaches. The dynamic failure of layered metallic composites, therefore, needs to be characterized in terms of strength, toughness, and fracture response. A dislocation-density based crystalline plasticity formulation, finite-element techniques, rational crystallographic orientation relations and a new fracture methodology were used to predict the failure modes associated with the high strain rate behavior of aluminum layered composites. Two alloy layers, a high strength alloy, aluminum 2195, and an aluminum alloy 2139, with high toughness, were modeled with representative microstructures that included precipitates, dispersed particles, and different grain boundary (GB) distributions. The new fracture methodology, based on an overlap method and phantom nodes, is used with a fracture criteria specialized for fracture on different cleavage planes. One of the objectives of this investigation, therefore, was to determine the optimal arrangements of the 2139 and 2195 aluminum alloys for a metallic layered composite that would combine strength, toughness and fracture resistance for high strain-rate applications. Different layer arrangements were investigated for high strain-rate applications, and the optimal arrangement was with the high toughness 2139 layer on the bottom, which provided extensive shear strain localization, and the high strength 2195 layer on the top for high strength resistance. The layer thickness of the bottom high toughness layer also affected the bending behavior of the roll-boned interface and the potential delamination of the layers. Shear strain localization, dynamic cracking and delamination were the mutually competing failure mechanisms for the layered metallic composite, and control of these failure modes can be optimized for high strain

  13. Validation of Material Models For Automotive Carbon Fiber Composite Structures Via Physical And Crash Testing (VMM Composites Project)

    Energy Technology Data Exchange (ETDEWEB)

    Coppola, Anthony [General Motors Company, Flint, MI (United States); Faruque, Omar [Ford Motor Company, Dearborn, MI (United States); Truskin, James F [FCA US LLC, Auburn Hills, MI (United States); Board, Derek [Ford Motor Company, Dearborn, MI (United States); Jones, Martin [Ford Motor Company, Dearborn, MI (United States); Tao, Jian [FCA US LLC, Auburn Hills, MI (United States); Chen, Yijung [Ford Motor Company, Dearborn, MI (United States); Mehta, Manish [M-Tech International LLC, Dubai (United Arab Emirates)


    As automotive fuel economy requirements increase, the push for reducing overall vehicle weight will likely include the consideration of materials that have not previously been part of mainstream vehicle design and manufacturing, including carbon fiber composites. Vehicle manufacturers currently rely on computer-aided engineering (CAE) methods as part of the design and development process, so going forward, the ability to accurately and predictably model carbon fiber composites will be necessary. If composites are to be used for structural components, this need applies to both, crash and quasi-static modeling. This final report covers the results of a five-year, $6.89M, 50% cost-shared research project between Department of Energy (DOE) and the US Advanced Materials Partnership (USAMP) under Cooperative Agreement DE-EE-0005661 known as “Validation of Material Models for Automotive Carbon Fiber Composite Structures Via Physical and Crash Testing (VMM).” The objective of the VMM Composites Project was to validate and assess the ability of physics-based material models to predict crash performance of automotive primary load-carrying carbon fiber composite structures. Simulation material models that were evaluated included micro-mechanics based meso-scale models developed by the University of Michigan (UM) and micro-plane models by Northwestern University (NWU) under previous collaborations with the DOE and Automotive Composites Consortium/USAMP, as well as five commercial crash codes: LS-DYNA, RADIOSS, VPS/PAM-CRASH, Abaqus, and GENOA-MCQ. CAE predictions obtained from seven organizations were compared with experimental results from quasi-static testing and dynamic crash testing of a thermoset carbon fiber composite front-bumper and crush-can (FBCC) system gathered under multiple loading conditions. This FBCC design was developed to demonstrate progressive crush, virtual simulation, tooling, fabrication, assembly, non-destructive evaluation and crash testing

  14. Analytical design model for a piezo-composite unimorph actuator and its verification using lightweight piezo-composite curved actuators (United States)

    Yoon, K. J.; Park, K. H.; Lee, S. K.; Goo, N. S.; Park, H. C.


    This paper describes an analytical design model for a layered piezo-composite unimorph actuator and its numerical and experimental verification using a LIPCA (lightweight piezo-composite curved actuator) that is lighter than other conventional piezo-composite type actuators. The LIPCA is composed of top fiber composite layers with high modulus and low CTE (coefficient of thermal expansion), a middle PZT ceramic wafer, and base layers with low modulus and high CTE. The advantages of the LIPCA design are to replace the heavy metal layer of THUNDER by lightweight fiber-reinforced plastic layers without compromising the generation of high force and large displacement and to have design flexibility by selecting the fiber direction and the number of prepreg layers. In addition to the lightweight advantage and design flexibility, the proposed device can be manufactured without adhesive layers when we use a resin prepreg system. A piezo-actuation model for a laminate with piezo-electric material layers and fiber composite layers is proposed to predict the curvature and residual stress of the LIPCA. To predict the actuation displacement of the LIPCA with curvature, a finite element analysis method using the proposed piezo-actuation model is introduced. The predicted deformations are in good agreement with the experimental ones.

  15. Protein and oil composition predictions of single soybeans by transmission Raman spectroscopy. (United States)

    Schulmerich, Matthew V; Walsh, Michael J; Gelber, Matthew K; Kong, Rong; Kole, Matthew R; Harrison, Sandra K; McKinney, John; Thompson, Dennis; Kull, Linda S; Bhargava, Rohit


    The soybean industry requires rapid, accurate, and precise technologies for the analyses of seed/grain constituents. While the current gold standard for nondestructive quantification of economically and nutritionally important soybean components is near-infrared spectroscopy (NIRS), emerging technology may provide viable alternatives and lead to next generation instrumentation for grain compositional analysis. In principle, Raman spectroscopy provides the necessary chemical information to generate models for predicting the concentration of soybean constituents. In this communication, we explore the use of transmission Raman spectroscopy (TRS) for nondestructive soybean measurements. We show that TRS uses the light scattering properties of soybeans to effectively homogenize the heterogeneous bulk of a soybean for representative sampling. Working with over 1000 individual intact soybean seeds, we developed a simple partial least-squares model for predicting oil and protein content nondestructively. We find TRS to have a root-mean-standard error of prediction (RMSEP) of 0.89% for oil measurements and 0.92% for protein measurements. In both calibration and validation sets, the predicative capabilities of the model were similar to the error in the reference methods.

  16. A new approach for modeling composite materials (United States)

    Alcaraz de la Osa, R.; Moreno, F.; Saiz, J. M.


    The increasing use of composite materials is due to their ability to tailor materials for special purposes, with applications evolving day by day. This is why predicting the properties of these systems from their constituents, or phases, has become so important. However, assigning macroscopical optical properties for these materials from the bulk properties of their constituents is not a straightforward task. In this research, we present a spectral analysis of three-dimensional random composite typical nanostructures using an Extension of the Discrete Dipole Approximation (E-DDA code), comparing different approaches and emphasizing the influences of optical properties of constituents and their concentration. In particular, we hypothesize a new approach that preserves the individual nature of the constituents introducing at the same time a variation in the optical properties of each discrete element that is driven by the surrounding medium. The results obtained with this new approach compare more favorably with the experiment than previous ones. We have also applied it to a non-conventional material composed of a metamaterial embedded in a dielectric matrix. Our version of the Discrete Dipole Approximation code, the EDDA code, has been formulated specifically to tackle this kind of problem, including materials with either magnetic and tensor properties.

  17. Comparison of two ordinal prediction models

    DEFF Research Database (Denmark)

    Kattan, Michael W; Gerds, Thomas A


    system (i.e. old or new), such as the level of evidence for one or more factors included in the system or the general opinions of expert clinicians. However, given the major objective of estimating prognosis on an ordinal scale, we argue that the rival staging system candidates should be compared...... on their ability to predict outcome. We sought to outline an algorithm that would compare two rival ordinal systems on their predictive ability. RESULTS: We devised an algorithm based largely on the concordance index, which is appropriate for comparing two models in their ability to rank observations. We...... demonstrate our algorithm with a prostate cancer staging system example. CONCLUSION: We have provided an algorithm for selecting the preferred staging system based on prognostic accuracy. It appears to be useful for the purpose of selecting between two ordinal prediction models....

  18. Predictions of the product compositions for combustion or gasification of biomass and others hydrocarbons

    Energy Technology Data Exchange (ETDEWEB)

    Rocha, Hendrick Maxil Zarate; Itai, Yuu; Nogueira, Manoel Fernandes Martins; Moraes, Sinfronio Brito; Rocha, Brigida Ramati Pereira da [Universidade Federal do Para (UFPA), Belem, PA (Brazil). Faculdade de Engenharia Mecanica]. E-mails:;;;;


    Processes involving combustion and gasification are object of study of many researchers. To simulate these processes in a detailed way, it is necessary to solve equations for chemical kinetics whose resolution many times is difficult due lack of information in the literature a simples way to bypass tis problem is due the chemical equilibrium. Prediction of the flu gases composition through chemical equilibrium is an important step in the mathematical modelling for gasification and combustion processes. Some free programs exists to solve problems that involve the chemical equilibrium, such as STANJAN, CEA, GASEQ, CANTERA and others.These programs have difficulty for cases involving fuel such as: biomass, vegetable oils, biodiesel, natural gas, etc., because they do not have database with the fuel composition and is hard to supply their HHV and their elementary analysis. In this work, using numeric methods, a program was developed to predict the gases composition on equilibrium after combustion and gasification processes with the for constant pressure or volume. In the program the chemical formula of the fuel is defined as C{sub x}H{sub y}O{sub z}N{sub w}S{sub v}A{sub u} that reacts with an gaseous oxidizer composed by O{sub 2}, N{sub 2}, Ar, He, CO{sub 2} e H{sub 2}O to have as final result the composition of the products CO{sub 2}, CO, H{sub 2}O, H{sub 2}, H, OH, O{sub 2}, O, N{sub 2}, NO, SO{sub 2}, CH{sub 4}, Ar, He, and ash. To verify the accuracy of the calculated values, it was compared with the program CEA (developed by NASA) and with experimental data obtained from literature. (author)

  19. Blast Testing and Modelling of Composite Structures

    DEFF Research Database (Denmark)

    Giversen, Søren

    The motivation for this work is based on a desire for finding light weight alternatives to high strength steel as the material to use for armouring in military vehicles. With the use of high strength steel, an increase in the level of armouring has a significant impact on the vehicle weight......, affecting for example the manoeuvrability and top speed negatively, which ultimately affects the safety of the personal in the vehicle. Strong and light materials, such as fibre reinforced composites, could therefore act as substitutes for the high strength steel, and minimize the impact on the vehicle...... work this set-up should be improved such that the modelled pressure can be validated. For tests performed with a 250g charge load comparisons with model data showed poor agreement. This was found to be due to improper design of the modelled laminate panels, where the layer interface delamination...

  20. Predictive analytics can support the ACO model. (United States)

    Bradley, Paul


    Predictive analytics can be used to rapidly spot hard-to-identify opportunities to better manage care--a key tool in accountable care. When considering analytics models, healthcare providers should: Make value-based care a priority and act on information from analytics models. Create a road map that includes achievable steps, rather than major endeavors. Set long-term expectations and recognize that the effectiveness of an analytics program takes time, unlike revenue cycle initiatives that may show a quick return.

  1. Predictive performance models and multiple task performance (United States)

    Wickens, Christopher D.; Larish, Inge; Contorer, Aaron


    Five models that predict how performance of multiple tasks will interact in complex task scenarios are discussed. The models are shown in terms of the assumptions they make about human operator divided attention. The different assumptions about attention are then empirically validated in a multitask helicopter flight simulation. It is concluded from this simulation that the most important assumption relates to the coding of demand level of different component tasks.

  2. Model Predictive Control of Sewer Networks

    DEFF Research Database (Denmark)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik


    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....

  3. Distributed Model Predictive Control via Dual Decomposition

    DEFF Research Database (Denmark)

    Biegel, Benjamin; Stoustrup, Jakob; Andersen, Palle


    This chapter presents dual decomposition as a means to coordinate a number of subsystems coupled by state and input constraints. Each subsystem is equipped with a local model predictive controller while a centralized entity manages the subsystems via prices associated with the coupling constraints...

  4. Model Predictive Control of Mineral Column Flotation Process

    Directory of Open Access Journals (Sweden)

    Yahui Tian


    Full Text Available Column flotation is an efficient method commonly used in the mineral industry to separate useful minerals from ores of low grade and complex mineral composition. Its main purpose is to achieve maximum recovery while ensuring desired product grade. This work addresses a model predictive control design for a mineral column flotation process modeled by a set of nonlinear coupled heterodirectional hyperbolic partial differential equations (PDEs and ordinary differential equations (ODEs, which accounts for the interconnection of well-stirred regions represented by continuous stirred tank reactors (CSTRs and transport systems given by heterodirectional hyperbolic PDEs, with these two regions combined through the PDEs’ boundaries. The model predictive control considers both optimality of the process operations and naturally present input and state/output constraints. For the discrete controller design, spatially varying steady-state profiles are obtained by linearizing the coupled ODE–PDE model, and then the discrete system is obtained by using the Cayley–Tustin time discretization transformation without any spatial discretization and/or without model reduction. The model predictive controller is designed by solving an optimization problem with input and state/output constraints as well as input disturbance to minimize the objective function, which leads to an online-solvable finite constrained quadratic regulator problem. Finally, the controller performance to keep the output at the steady state within the constraint range is demonstrated by simulation studies, and it is concluded that the optimal control scheme presented in this work makes this flotation process more efficient.

  5. Life Modeling and Design Analysis for Ceramic Matrix Composite Materials (United States)


    The primary research efforts focused on characterizing and modeling static failure, environmental durability, and creep-rupture behavior of two classes of ceramic matrix composites (CMC), silicon carbide fibers in a silicon carbide matrix (SiC/SiC) and carbon fibers in a silicon carbide matrix (C/SiC). An engineering life prediction model (Probabilistic Residual Strength model) has been developed specifically for CMCs. The model uses residual strength as the damage metric for evaluating remaining life and is posed probabilistically in order to account for the stochastic nature of the material s response. In support of the modeling effort, extensive testing of C/SiC in partial pressures of oxygen has been performed. This includes creep testing, tensile testing, half life and residual tensile strength testing. C/SiC is proposed for airframe and propulsion applications in advanced reusable launch vehicles. Figures 1 and 2 illustrate the models predictive capabilities as well as the manner in which experimental tests are being selected in such a manner as to ensure sufficient data is available to aid in model validation.

  6. A stepwise model to predict monthly streamflow (United States)

    Mahmood Al-Juboori, Anas; Guven, Aytac


    In this study, a stepwise model empowered with genetic programming is developed to predict the monthly flows of Hurman River in Turkey and Diyalah and Lesser Zab Rivers in Iraq. The model divides the monthly flow data to twelve intervals representing the number of months in a year. The flow of a month, t is considered as a function of the antecedent month's flow (t - 1) and it is predicted by multiplying the antecedent monthly flow by a constant value called K. The optimum value of K is obtained by a stepwise procedure which employs Gene Expression Programming (GEP) and Nonlinear Generalized Reduced Gradient Optimization (NGRGO) as alternative to traditional nonlinear regression technique. The degree of determination and root mean squared error are used to evaluate the performance of the proposed models. The results of the proposed model are compared with the conventional Markovian and Auto Regressive Integrated Moving Average (ARIMA) models based on observed monthly flow data. The comparison results based on five different statistic measures show that the proposed stepwise model performed better than Markovian model and ARIMA model. The R2 values of the proposed model range between 0.81 and 0.92 for the three rivers in this study.

  7. Nonlinear analysis of AS4/PEEK thermoplastic composite laminate using a one parameter plasticity model (United States)

    Sun, C. T.; Yoon, K. J.


    A one-parameter plasticity model was shown to adequately describe the orthotropic plastic deformation of AS4/PEEK (APC-2) unidirectional thermoplastic composite. This model was verified further for unidirectional and laminated composite panels with and without a hole. The nonlinear stress-strain relations were measured and compared with those predicted by the finite element analysis using the one-parameter elastic-plastic constitutive model. The results show that the one-parameter orthotropic plasticity model is suitable for the analysis of elastic-plastic deformation of AS4/PEEK composite laminates.

  8. Predicting refinery effluent toxicity on the basis of hydrocarbon composition determined by GCxGC analysis

    Energy Technology Data Exchange (ETDEWEB)

    Whale, G. [and others


    A high resolution analytical method for determining hydrocarbon blocks in petroleum products by comprehensive two-dimensional gas chromatography (GCxGC) was used for the analysis of petroleum hydrocarbons extracted from refinery effluents. From 105 CONCAWE refineries in Europe 111 refinery effluents were collected in the period June 2008 to March 2009 (CONCAWE, 2010). The effluents were analysed for metals, standard effluent parameters (including Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), oil in water (OiW), GCxGC speciated hydrocarbons, BTEX (Benzene, Toluene, Ethylbenzene, and Xylenes) and volatile organic compounds. This report describes the subsequent analysis of the GCxGC data, as described in hydrocarbon blocks, and uses the PETROTOX model, to predict the environmental toxicity (i.e. ecotoxicity) of the discharged effluents. A further analysis was undertaken to address the potential environmental impact of these predicted effects initially using default dilution factors and then,when necessary site specific factors. The report describes all the methods used to arrive at the predictions, and shows that for the majority of refinery effluents direct toxicity effects in the effluents are not anticipated. Furthermore, when applying either the EU Risk Assessment Technical Guidance Document (TGD) default dilution factors or site specific dilution factors, none of the refineries are predicted to exerting either acute or chronic toxicity to organisms in the receiving aquatic environment, based on their hydrocarbon composition present in the effluent samples.

  9. 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)


    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.)

  10. An Intelligent Model for Stock Market Prediction

    Directory of Open Access Journals (Sweden)

    IbrahimM. Hamed


    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.

  11. Predictive Models, How good are they?

    DEFF Research Database (Denmark)

    Kasch, Helge

    The WAD grading system has been used for more than 20 years by now. It has shown long-term viability, but with strengths and limitations. New bio-psychosocial assessment of the acute whiplash injured subject may provide better prediction of long-term disability and pain. Furthermore, the emerging......-up. It is important to obtain prospective identification of the relevant risk underreported disability could, if we were able to expose these hidden “risk-factors” during our consultations, provide us with better predictive models. New data from large clinical studies will present exciting new genetic risk markers...

  12. Sludge pipe flow pressure drop prediction using composite power ...

    African Journals Online (AJOL)


    Sep 30, 2011 ... 3Department of Chemical Engineering, IIT Kanpur, India. Abstract. When predicting pressure gradients for the flow of sludges in pipes, the rheology of the fluid ..... implicit in the stability analysis of Ryan and Johnson (1959).

  13. Predicting milk yield and composition in lactating sows

    DEFF Research Database (Denmark)

    Hansen, A V; Strathe, A B; Kebreab, E


    The objective of this study was to develop a framework describing the milk production curve in sows as affected by parity, method of milk yield (MY) determination, litter size (LS), and litter gain (LG). A database containing data on LS, LG, dietary protein and fat content, MY, and composition...

  14. Surface roughness prediction of particulate composites using artificial neural networks in turning operation

    Directory of Open Access Journals (Sweden)

    Mohammad Ramezani


    Full Text Available A number of factors, e.g. cutting speed and feed rate, affect the surface roughness in machining process. In this paper, an Artificial Neural Network model was used to forecast surface roughness with related inputs, including cutting speed and feed rate. The output of the ANN model input parameters related to the machined surface roughness parameters. In this research, twelve samples of experimental data were used to train the network. Moreover, four other experimental tests were implemented to test the network. The study concludes that ANN was a reliable and accurate method for predicting machining parameters in CNC turning operation of Particulate Reinforced Aluminum Matrix Composites (PAMCs specimens with 0%, 5%, 10% and 15% filler. The aim of this work is to decrease the production cost and consequently increase the production rate of these materials for industry without any trial and error method procedure.


    Directory of Open Access Journals (Sweden)

    SILVA R. G.


    Full Text Available A new algorithm for model predictive control is presented. The algorithm utilizes a simultaneous solution and optimization strategy to solve the model's differential equations. The equations are discretized by equidistant collocation, and along with the algebraic model equations are included as constraints in a nonlinear programming (NLP problem. This algorithm is compared with the algorithm that uses orthogonal collocation on finite elements. The equidistant collocation algorithm results in simpler equations, providing a decrease in computation time for the control moves. Simulation results are presented and show a satisfactory performance of this algorithm.

  16. A statistical model for predicting muscle performance (United States)

    Byerly, Diane Leslie De Caix

    The objective of these studies was to develop a capability for predicting muscle performance and fatigue to be utilized for both space- and ground-based applications. To develop this predictive model, healthy test subjects performed a defined, repetitive dynamic exercise to failure using a Lordex spinal machine. Throughout the exercise, surface electromyography (SEMG) data were collected from the erector spinae using a Mega Electronics ME3000 muscle tester and surface electrodes placed on both sides of the back muscle. These data were analyzed using a 5th order Autoregressive (AR) model and statistical regression analysis. It was determined that an AR derived parameter, the mean average magnitude of AR poles, significantly correlated with the maximum number of repetitions (designated Rmax) that a test subject was able to perform. Using the mean average magnitude of AR poles, a test subject's performance to failure could be predicted as early as the sixth repetition of the exercise. This predictive model has the potential to provide a basis for improving post-space flight recovery, monitoring muscle atrophy in astronauts and assessing the effectiveness of countermeasures, monitoring astronaut performance and fatigue during Extravehicular Activity (EVA) operations, providing pre-flight assessment of the ability of an EVA crewmember to perform a given task, improving the design of training protocols and simulations for strenuous International Space Station assembly EVA, and enabling EVA work task sequences to be planned enhancing astronaut performance and safety. Potential ground-based, medical applications of the predictive model include monitoring muscle deterioration and performance resulting from illness, establishing safety guidelines in the industry for repetitive tasks, monitoring the stages of rehabilitation for muscle-related injuries sustained in sports and accidents, and enhancing athletic performance through improved training protocols while reducing

  17. Prediction models : the right tool for the right problem

    NARCIS (Netherlands)

    Kappen, Teus H.; Peelen, Linda M.


    PURPOSE OF REVIEW: Perioperative prediction models can help to improve personalized patient care by providing individual risk predictions to both patients and providers. However, the scientific literature on prediction model development and validation can be quite technical and challenging to

  18. Neuro-fuzzy modeling in bankruptcy prediction

    Directory of Open Access Journals (Sweden)

    Vlachos D.


    Full Text Available For the past 30 years the problem of bankruptcy prediction had been thoroughly studied. From the paper of Altman in 1968 to the recent papers in the '90s, the progress of prediction accuracy was not satisfactory. This paper investigates an alternative modeling of the system (firm, combining neural networks and fuzzy controllers, i.e. using neuro-fuzzy models. Classical modeling is based on mathematical models that describe the behavior of the firm under consideration. The main idea of fuzzy control, on the other hand, is to build a model of a human control expert who is capable of controlling the process without thinking in a mathematical model. This control expert specifies his control action in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus, which can stimulate the behavior of the control expert and enhance its performance. The accuracy of the model is studied using datasets from previous research papers.

  19. Predictive Models for Carcinogenicity and Mutagenicity ... (United States)

    Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include VitotoxTM, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-t

  20. Validation of an Acoustic Impedance Prediction Model for Skewed Resonators (United States)

    Howerton, Brian M.; Parrott, Tony L.


    An impedance prediction model was validated experimentally to determine the composite impedance of a series of high-aspect ratio slot resonators incorporating channel skew and sharp bends. Such structures are useful for packaging acoustic liners into constrained spaces for turbofan noise control applications. A formulation of the Zwikker-Kosten Transmission Line (ZKTL) model, incorporating the Richards correction for rectangular channels, is used to calculate the composite normalized impedance of a series of six multi-slot resonator arrays with constant channel length. Experimentally, acoustic data was acquired in the NASA Langley Normal Incidence Tube over the frequency range of 500 to 3500 Hz at 120 and 140 dB OASPL. Normalized impedance was reduced using the Two-Microphone Method for the various combinations of channel skew and sharp 90o and 180o bends. Results show that the presence of skew and/or sharp bends does not significantly alter the impedance of a slot resonator as compared to a straight resonator of the same total channel length. ZKTL predicts the impedance of such resonators very well over the frequency range of interest. The model can be used to design arrays of slot resonators that can be packaged into complex geometries heretofore unsuitable for effective acoustic treatment.

  1. Validated predictive modelling of the environmental resistome. (United States)

    Amos, Gregory C A; Gozzard, Emma; Carter, Charlotte E; Mead, Andrew; Bowes, Mike J; Hawkey, Peter M; Zhang, Lihong; Singer, Andrew C; Gaze, William H; Wellington, Elizabeth M H


    Multi-drug-resistant bacteria pose a significant threat to public health. The role of the environment in the overall rise in antibiotic-resistant infections and risk to humans is largely unknown. This study aimed to evaluate drivers of antibiotic-resistance levels across the River Thames catchment, model key biotic, spatial and chemical variables and produce predictive models for future risk assessment. Sediment samples from 13 sites across the River Thames basin were taken at four time points across 2011 and 2012. Samples were analysed for class 1 integron prevalence and enumeration of third-generation cephalosporin-resistant bacteria. Class 1 integron prevalence was validated as a molecular marker of antibiotic resistance; levels of resistance showed significant geospatial and temporal variation. The main explanatory variables of resistance levels at each sample site were the number, proximity, size and type of surrounding wastewater-treatment plants. Model 1 revealed treatment plants accounted for 49.5% of the variance in resistance levels. Other contributing factors were extent of different surrounding land cover types (for example, Neutral Grassland), temporal patterns and prior rainfall; when modelling all variables the resulting model (Model 2) could explain 82.9% of variations in resistance levels in the whole catchment. Chemical analyses correlated with key indicators of treatment plant effluent and a model (Model 3) was generated based on water quality parameters (contaminant and macro- and micro-nutrient levels). Model 2 was beta tested on independent sites and explained over 78% of the variation in integron prevalence showing a significant predictive ability. We believe all models in this study are highly useful tools for informing and prioritising mitigation strategies to reduce the environmental resistome.

  2. Nonlinear model predictive control theory and algorithms

    CERN Document Server

    Grüne, Lars


    This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from, together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...

  3. Baryogenesis model predicting antimatter in the Universe

    International Nuclear Information System (INIS)

    Kirilova, D.


    Cosmic ray and gamma-ray data do not rule out antimatter domains in the Universe, separated at distances bigger than 10 Mpc from us. Hence, it is interesting to analyze the possible generation of vast antimatter structures during the early Universe evolution. We discuss a SUSY-condensate baryogenesis model, predicting large separated regions of matter and antimatter. The model provides generation of the small locally observed baryon asymmetry for a natural initial conditions, it predicts vast antimatter domains, separated from the matter ones by baryonically empty voids. The characteristic scale of antimatter regions and their distance from the matter ones is in accordance with observational constraints from cosmic ray, gamma-ray and cosmic microwave background anisotropy data

  4. Finding Furfural Hydrogenation Catalysts via Predictive Modelling


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


    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 t...

  5. Predictive Modeling in Actinide Chemistry and Catalysis

    Energy Technology Data Exchange (ETDEWEB)

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


    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.

  6. Composite vector mesons and string models

    International Nuclear Information System (INIS)

    Mandelstam, S.


    The author discusses the general question of gauge mesons in extended supergravities, and whether such theories can produce the gauge mesons corresponding to a group at least as large as SU(3) x SU(2) x U(1). An exciting conjecture in this direction was made a few years ago by previous authors, who suggested that there might be composite SU(8) gauge mesons in a supergravity model known as the N=8 model. Until we have a consistent, renormalizable theory of supergravity we cannot really obtain any indication of the truth or falseness of that conjecture. One form of the Neveu-Schwarz string model has been shown to be a theory of supergravity; it is finite at the one-loop level and probably in any order of perturbation theory. The discussion is within the framework of this model. The author questions whether massive vector mesons can possibly lose their mass due to interactions. Arguments have been given on both sides of this question, and the author believes that this can occur under certain circumstances. Our conclusions is that the FNNS mechanism will create a gauge symmetry in addition to the rigid symmetry

  7. Tectonic predictions with mantle convection models (United States)

    Coltice, Nicolas; Shephard, Grace E.


    Over the past 15 yr, numerical models of convection in Earth's mantle have made a leap forward: they can now produce self-consistent plate-like behaviour at the surface together with deep mantle circulation. These digital tools provide a new window into the intimate connections between plate tectonics and mantle dynamics, and can therefore be used for tectonic predictions, in principle. This contribution explores this assumption. First, initial conditions at 30, 20, 10 and 0 Ma are generated by driving a convective flow with imposed plate velocities at the surface. We then compute instantaneous mantle flows in response to the guessed temperature fields without imposing any boundary conditions. Plate boundaries self-consistently emerge at correct locations with respect to reconstructions, except for small plates close to subduction zones. As already observed for other types of instantaneous flow calculations, the structure of the top boundary layer and upper-mantle slab is the dominant character that leads to accurate predictions of surface velocities. Perturbations of the rheological parameters have little impact on the resulting surface velocities. We then compute fully dynamic model evolution from 30 and 10 to 0 Ma, without imposing plate boundaries or plate velocities. Contrary to instantaneous calculations, errors in kinematic predictions are substantial, although the plate layout and kinematics in several areas remain consistent with the expectations for the Earth. For these calculations, varying the rheological parameters makes a difference for plate boundary evolution. Also, identified errors in initial conditions contribute to first-order kinematic errors. This experiment shows that the tectonic predictions of dynamic models over 10 My are highly sensitive to uncertainties of rheological parameters and initial temperature field in comparison to instantaneous flow calculations. Indeed, the initial conditions and the rheological parameters can be good enough

  8. Prediction of the chemical composition and in vitro dry matter ...

    African Journals Online (AJOL)

    ammoniated maize residue to replace maize meal in fattening diets for ... Optimal feeding is essential for economical animal production. ... linear regression. The wavelengths were incorporated into a prediction equation for each forage quality. The equations were validated by simple linear regression of the laboratory.

  9. Prediction of chemical composition of South African Medicago sativa ...

    African Journals Online (AJOL)

    The near infrared reflectance spectroscopy (NIRS) to predict chemical and digestibility parameters was investigated. Samples (n = 168) representing the spectral characteristics of the South African. Medicago sativa L. hay population were chemically analysed for the development of calibration equations. Values for r² and ...

  10. a rational approach for predicting the minimum composition of anti ...

    African Journals Online (AJOL)

    of Cv may deviate from what is predicted by the formula given above are discussed. Extensive literature search on malaria, onchocerciasis and schistosomiasis sub-unit vaccine development ... provides a rational framework for constituting a sub-unit anti-parasite vaccine. ..... vaccine formulation produced greater protection.

  11. Breast cancer risks and risk prediction models. (United States)

    Engel, Christoph; Fischer, Christine


    BRCA1/2 mutation carriers have a considerably increased risk to develop breast and ovarian cancer. The personalized clinical management of carriers and other at-risk individuals depends on precise knowledge of the cancer risks. In this report, we give an overview of the present literature on empirical cancer risks, and we describe risk prediction models that are currently used for individual risk assessment in clinical practice. Cancer risks show large variability between studies. Breast cancer risks are at 40-87% for BRCA1 mutation carriers and 18-88% for BRCA2 mutation carriers. For ovarian cancer, the risk estimates are in the range of 22-65% for BRCA1 and 10-35% for BRCA2. The contralateral breast cancer risk is high (10-year risk after first cancer 27% for BRCA1 and 19% for BRCA2). Risk prediction models have been proposed to provide more individualized risk prediction, using additional knowledge on family history, mode of inheritance of major genes, and other genetic and non-genetic risk factors. User-friendly software tools have been developed that serve as basis for decision-making in family counseling units. In conclusion, further assessment of cancer risks and model validation is needed, ideally based on prospective cohort studies. To obtain such data, clinical management of carriers and other at-risk individuals should always be accompanied by standardized scientific documentation.

  12. A predictive model for dimensional errors in fused deposition modeling

    DEFF Research Database (Denmark)

    Stolfi, A.


    This work concerns the effect of deposition angle (a) and layer thickness (L) on the dimensional performance of FDM parts using a predictive model based on the geometrical description of the FDM filament profile. An experimental validation over the whole a range from 0° to 177° at 3° steps and two...... values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....

  13. Prediction of foal carcass composition and wholesale cut yields by using video image analysis. (United States)

    Lorenzo, J M; Guedes, C M; Agregán, R; Sarriés, M V; Franco, D; Silva, S R


    This work represents the first contribution for the application of the video image analysis (VIA) technology in predicting lean meat and fat composition in the equine species. Images of left sides of the carcass (n=42) were captured from the dorsal, lateral and medial views using a high-resolution digital camera. A total of 41 measurements (angles, lengths, widths and areas) were obtained by VIA. The variation of percentage of lean meat obtained from the forequarter (FQ) and hindquarter (HQ) carcass ranged between 5.86% and 7.83%. However, the percentage of fat (FAT) obtained from the FQ and HQ carcass presented a higher variation (CV between 41.34% and 44.58%). By combining different measurements and using prediction models with cold carcass weight (CCW) and VIA measurement the coefficient of determination (k-fold-R 2) were 0.458 and 0.532 for FQ and HQ, respectively. On the other hand, employing the most comprehensive model (CCW plus all VIA measurements), the k-fold-R 2 increased from 0.494 to 0.887 and 0.513 to 0.878 with respect to the simplest model (only with CCW), while precision increased with the reduction in the root mean square error (2.958 to 0.947 and 1.841 to 0.787) for the hindquarter fat and lean percentage, respectively. With CCW plus VIA measurements is possible to explain the wholesale value cuts yield variation (k-fold-R 2 between 0.533 and 0.889). Overall, the VIA technology performed in the present study could be considered as an accurate method to assess the horse carcass composition which could have a role in breeding programmes and research studies to assist in the development of a value-based marketing system for horse carcass.

  14. Two stage neural network modelling for robust model predictive control. (United States)

    Patan, Krzysztof


    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.

  15. Predictive modeling of reactive wetting and metal joining.

    Energy Technology Data Exchange (ETDEWEB)

    van Swol, Frank B.


    The performance, reproducibility and reliability of metal joints are complex functions of the detailed history of physical processes involved in their creation. Prediction and control of these processes constitutes an intrinsically challenging multi-physics problem involving heating and melting a metal alloy and reactive wetting. Understanding this process requires coupling strong molecularscale chemistry at the interface with microscopic (diffusion) and macroscopic mass transport (flow) inside the liquid followed by subsequent cooling and solidification of the new metal mixture. The final joint displays compositional heterogeneity and its resulting microstructure largely determines the success or failure of the entire component. At present there exists no computational tool at Sandia that can predict the formation and success of a braze joint, as current capabilities lack the ability to capture surface/interface reactions and their effect on interface properties. This situation precludes us from implementing a proactive strategy to deal with joining problems. Here, we describe what is needed to arrive at a predictive modeling and simulation capability for multicomponent metals with complicated phase diagrams for melting and solidification, incorporating dissolutive and composition-dependent wetting.

  16. Predicting extinction rates in stochastic epidemic models

    International Nuclear Information System (INIS)

    Schwartz, Ira B; Billings, Lora; Dykman, Mark; Landsman, Alexandra


    We investigate the stochastic extinction processes in a class of epidemic models. Motivated by the process of natural disease extinction in epidemics, we examine the rate of extinction as a function of disease spread. We show that the effective entropic barrier for extinction in a susceptible–infected–susceptible epidemic model displays scaling with the distance to the bifurcation point, with an unusual critical exponent. We make a direct comparison between predictions and numerical simulations. We also consider the effect of non-Gaussian vaccine schedules, and show numerically how the extinction process may be enhanced when the vaccine schedules are Poisson distributed

  17. Predictive Modeling of the CDRA 4BMS (United States)

    Coker, Robert F.; Knox, James C.


    As part of NASA's Advanced Exploration Systems (AES) program and the Life Support Systems Project (LSSP), fully predictive models of the Four Bed Molecular Sieve (4BMS) of the Carbon Dioxide Removal Assembly (CDRA) on the International Space Station (ISS) are being developed. This virtual laboratory will be used to help reduce mass, power, and volume requirements for future missions. In this paper we describe current and planned modeling developments in the area of carbon dioxide removal to support future crewed Mars missions as well as the resolution of anomalies observed in the ISS CDRA.

  18. Constraining composite Higgs models using LHC data (United States)

    Banerjee, Avik; Bhattacharyya, Gautam; Kumar, Nilanjana; Ray, Tirtha Sankar


    We systematically study the modifications in the couplings of the Higgs boson, when identified as a pseudo Nambu-Goldstone boson of a strong sector, in the light of LHC Run 1 and Run 2 data. For the minimal coset SO(5)/SO(4) of the strong sector, we focus on scenarios where the standard model left- and right-handed fermions (specifically, the top and bottom quarks) are either in 5 or in the symmetric 14 representation of SO(5). Going beyond the minimal 5 L - 5 R representation, to what we call here the `extended' models, we observe that it is possible to construct more than one invariant in the Yukawa sector. In such models, the Yukawa couplings of the 125 GeV Higgs boson undergo nontrivial modifications. The pattern of such modifications can be encoded in a generic phenomenological Lagrangian which applies to a wide class of such models. We show that the presence of more than one Yukawa invariant allows the gauge and Yukawa coupling modifiers to be decorrelated in the `extended' models, and this decorrelation leads to a relaxation of the bound on the compositeness scale ( f ≥ 640 GeV at 95% CL, as compared to f ≥ 1 TeV for the minimal 5 L - 5 R representation model). We also study the Yukawa coupling modifications in the context of the next-to-minimal strong sector coset SO(6)/SO(5) for fermion-embedding up to representations of dimension 20. While quantifying our observations, we have performed a detailed χ 2 fit using the ATLAS and CMS combined Run 1 and available Run 2 data.

  19. Coherent nonlinear quantum model for composite fermions

    Energy Technology Data Exchange (ETDEWEB)

    Reinisch, Gilbert [Science Institute, University of Iceland, Dunhaga 3, IS-107 Reykjavik (Iceland); Gudmundsson, Vidar, E-mail: [Science Institute, University of Iceland, Dunhaga 3, IS-107 Reykjavik (Iceland); Manolescu, Andrei [School of Science and Engineering, Reykjavik University, Menntavegur 1, IS-101 Reykjavik (Iceland)


    Originally proposed by Read [1] and Jain [2], the so-called “composite-fermion” is a phenomenological quasi-particle resulting from the attachment of two local flux quanta, seen as nonlocal vortices, to electrons situated on a two-dimensional (2D) surface embedded in a strong orthogonal magnetic field. In this Letter this phenomenon is described as a highly-nonlinear and coherent mean-field quantum process of the soliton type by use of a 2D stationary Schrödinger–Poisson differential model with only two Coulomb-interacting electrons. At filling factor ν=1/3 of the lowest Landau level the solution agrees with both the exact two-electron antisymmetric Schrödinger wavefunction and with Laughlin's Jastrow-type guess for the fractional quantum Hall effect, hence providing this latter with a tentative physical justification deduced from the experimental results and based on first principles.

  20. Top partners searches and composite Higgs models

    International Nuclear Information System (INIS)

    Matsedonskyi, Oleksii; Panico, Giuliano; Wulzer, Andrea


    Colored fermionic partners of the top quark are well-known signatures of the Composite Higgs scenario and for this reason they have been and will be subject of an intensive experimental study at the LHC. Performing an assessment of the theoretical implications of this experimental effort is the goal of the present paper. We proceed by analyzing a set of simple benchmark models, characterized by simple two-dimensional parameter spaces where the results of the searches are conveniently visualized and their impact quantified. We only draw exclusion contours, in the hypothesis of no signal, but of course our formalism could equally well be used to report discoveries in a theoretically useful format.

  1. Data Driven Economic Model Predictive Control

    Directory of Open Access Journals (Sweden)

    Masoud Kheradmandi


    Full Text Available This manuscript addresses the problem of data driven model based economic model predictive control (MPC design. To this end, first, a data-driven Lyapunov-based MPC is designed, and shown to be capable of stabilizing a system at an unstable equilibrium point. The data driven Lyapunov-based MPC utilizes a linear time invariant (LTI model cognizant of the fact that the training data, owing to the unstable nature of the equilibrium point, has to be obtained from closed-loop operation or experiments. Simulation results are first presented demonstrating closed-loop stability under the proposed data-driven Lyapunov-based MPC. The underlying data-driven model is then utilized as the basis to design an economic MPC. The economic improvements yielded by the proposed method are illustrated through simulations on a nonlinear chemical process system example.

  2. Modelling and analysing interoperability in service compositions using COSMO

    NARCIS (Netherlands)

    Quartel, Dick; van Sinderen, Marten J.


    A service composition process typically involves multiple service models. These models may represent the composite and composed services from distinct perspectives, e.g. to model the role of some system that is involved in a service, and at distinct abstraction levels, e.g. to model the goal,

  3. Predicting the initial freezing point and water activity of meat products from composition data

    NARCIS (Netherlands)

    Sman, van der R.G.M.; Boer, E.P.J.


    In this paper we predict the water activity and initial freezing point of food products (meat and fish) based on their composition. The prediction is based on thermodynamics (the Clausius-Clapeyron equation, the Ross equation and an approximation of the Pitzer equation). Furthermore, we have taken

  4. Verification and Validation of a Three-Dimensional Generalized Composite Material Model (United States)

    Hoffarth, Canio; Harrington, Joseph; Rajan, Subramaniam D.; Goldberg, Robert K.; Carney, Kelly S.; DuBois, Paul; Blankenhorn, Gunther


    A general purpose orthotropic elasto-plastic computational constitutive material model has been developed to improve predictions of the response of composites subjected to high velocity impact. The three-dimensional orthotropic elasto-plastic composite material model is being implemented initially for solid elements in LS-DYNA as MAT213. In order to accurately represent the response of a composite, experimental stress-strain curves are utilized as input, allowing for a more general material model that can be used on a variety of composite applications. The theoretical details are discussed in a companion paper. This paper documents the implementation, verification and qualitative validation of the material model using the T800-F3900 fiber/resin composite material


    Directory of Open Access Journals (Sweden)

    Nataša Šarlija


    Full Text Available The purpose of this paper is to design three separate financial distress prediction models that will track the changes in a relative importance of financial ratios throughout three consecutive years. The models were based on the financial data from 2000 privately-owned small and medium-sized enterprises in Croatia from 2006 to 2009, and developed by means of logistic regression. Macroeconomic conditions as well as market dynamic have been changed over the mentioned period. Financial ratios that were less important in one period become more important in the next period. Composition of model starting in 2006 has been changed in the next years. It tells us what financial ratios are more important during the time of economic downturn. Besides, it helps us to understand behavior of small and medium-sized enterprises in the period of prerecession and in the period of recession.

  6. COBRA: A Computational Brewing Application for Predicting the Molecular Composition of Organic Aerosols

    Energy Technology Data Exchange (ETDEWEB)

    Fooshee, David R.; Nguyen, Tran B.; Nizkorodov, Sergey A.; Laskin, Julia; Laskin, Alexander; Baldi, Pierre


    Atmospheric organic aerosols (OA) represent a significant fraction of airborne particulate matter and can impact climate, visibility, and human health. These mixtures are difficult to characterize experimentally due to the enormous complexity and dynamic nature of their chemical composition. We introduce a novel Computational Brewing Application (COBRA) and apply it to modeling oligomerization chemistry stemming from condensation and addition reactions of monomers pertinent to secondary organic aerosol (SOA) formed by photooxidation of isoprene. COBRA uses two lists as input: a list of chemical structures comprising the molecular starting pool, and a list of rules defining potential reactions between molecules. Reactions are performed iteratively, with products of all previous iterations serving as reactants for the next one. The simulation generated thousands of molecular structures in the mass range of 120-500 Da, and correctly predicted ~70% of the individual SOA constituents observed by high-resolution mass spectrometry (HR-MS). Selected predicted structures were confirmed with tandem mass spectrometry. Esterification and hemiacetal formation reactions were shown to play the most significant role in oligomer formation, whereas aldol condensation was shown to be insignificant. COBRA is not limited to atmospheric aerosol chemistry, but is broadly applicable to the prediction of reaction products in other complex mixtures for which reasonable reaction mechanisms and seed molecules can be supplied by experimental or theoretical methods.

  7. Plant control using embedded predictive models

    International Nuclear Information System (INIS)

    Godbole, S.S.; Gabler, W.E.; Eschbach, S.L.


    B and W recently undertook the design of an advanced light water reactor control system. A concept new to nuclear steam system (NSS) control was developed. The concept, which is called the Predictor-Corrector, uses mathematical models of portions of the controlled NSS to calculate, at various levels within the system, demand and control element position signals necessary to satisfy electrical demand. The models give the control system the ability to reduce overcooling and undercooling of the reactor coolant system during transients and upsets. Two types of mathematical models were developed for use in designing and testing the control system. One model was a conventional, comprehensive NSS model that responds to control system outputs and calculates the resultant changes in plant variables that are then used as inputs to the control system. Two other models, embedded in the control system, were less conventional, inverse models. These models accept as inputs plant variables, equipment states, and demand signals and predict plant operating conditions and control element states that will satisfy the demands. This paper reports preliminary results of closed-loop Reactor Coolant (RC) pump trip and normal load reduction testing of the advanced concept. Results of additional transient testing, and of open and closed loop stability analyses will be reported as they are available

  8. Image Reconstruction Based Modeling of 3D Textile Composite (Postprint)

    National Research Council Canada - National Science Library

    Zhou, Eric; Mollenhauer, David; Iarve, Endel


    ... joints, near-net shape processing, etc. To fully understand the mechanical behavior of 3-D textile composites, it is essential to perform analyses to predict effective material properties and damage initiation and growth...

  9. Versatile Micromechanics Model for Multiscale Analysis of Composite Structures (United States)

    Kwon, Y. W.; Park, M. S.


    A general-purpose micromechanics model was developed so that the model could be applied to various composite materials such as reinforced by particles, long fibers and short fibers as well as those containing micro voids. Additionally, the model can be used with hierarchical composite materials. The micromechanics model can be used to compute effective material properties like elastic moduli, shear moduli, Poisson's ratios, and coefficients of thermal expansion for the various composite materials. The model can also calculate the strains and stresses at the constituent material level such as fibers, particles, and whiskers from the composite level stresses and strains. The model was implemented into ABAQUS using the UMAT option for multiscale analysis. An extensive set of examples are presented to demonstrate the reliability and accuracy of the developed micromechanics model for different kinds of composite materials. Another set of examples is provided to study the multiscale analysis of composite structures.

  10. Predicting the mineral composition of dust aerosols: Insights from elemental composition measured at the Izaña Observatory (United States)

    Pérez García-Pando, Carlos; Miller, Ron L.; Perlwitz, Jan P.; Rodríguez, Sergio; Prospero, Joseph M.


    Regional variations of dust mineral composition are fundamental to climate impacts but generally neglected in climate models. A challenge for models is that atlases of soil composition are derived from measurements following wet sieving, which destroys the aggregates potentially emitted from the soil. Aggregates are crucial to simulating the observed size distribution of emitted soil particles. We use an extension of brittle fragmentation theory in a global dust model to account for these aggregates. Our method reproduces the size-resolved dust concentration along with the approximately size-invariant fractional abundance of elements like Fe and Al in the decade-long aerosol record from the Izaña Observatory, off the coast of West Africa. By distinguishing between Fe in structural and free forms, we can attribute improved model behavior to aggregation of Fe and Al-rich clay particles. We also demonstrate the importance of size-resolved measurements along with elemental composition analysis to constrain models.

  11. Ground Motion Prediction Models for Caucasus Region (United States)

    Jorjiashvili, Nato; Godoladze, Tea; Tvaradze, Nino; Tumanova, Nino


    Ground motion prediction models (GMPMs) relate ground motion intensity measures to variables describing earthquake source, path, and site effects. Estimation of expected ground motion is a fundamental earthquake hazard assessment. The most commonly used parameter for attenuation relation is peak ground acceleration or spectral acceleration because this parameter gives useful information for Seismic Hazard Assessment. Since 2003 development of Georgian Digital Seismic Network has started. In this study new GMP models are obtained based on new data from Georgian seismic network and also from neighboring countries. Estimation of models is obtained by classical, statistical way, regression analysis. In this study site ground conditions are additionally considered because the same earthquake recorded at the same distance may cause different damage according to ground conditions. Empirical ground-motion prediction models (GMPMs) require adjustment to make them appropriate for site-specific scenarios. However, the process of making such adjustments remains a challenge. This work presents a holistic framework for the development of a peak ground acceleration (PGA) or spectral acceleration (SA) GMPE that is easily adjustable to different seismological conditions and does not suffer from the practical problems associated with adjustments in the response spectral domain.

  12. Modeling and Prediction of Krueger Device Noise (United States)

    Guo, Yueping; Burley, Casey L.; Thomas, Russell H.


    This paper presents the development of a noise prediction model for aircraft Krueger flap devices that are considered as alternatives to leading edge slotted slats. The prediction model decomposes the total Krueger noise into four components, generated by the unsteady flows, respectively, in the cove under the pressure side surface of the Krueger, in the gap between the Krueger trailing edge and the main wing, around the brackets supporting the Krueger device, and around the cavity on the lower side of the main wing. For each noise component, the modeling follows a physics-based approach that aims at capturing the dominant noise-generating features in the flow and developing correlations between the noise and the flow parameters that control the noise generation processes. The far field noise is modeled using each of the four noise component's respective spectral functions, far field directivities, Mach number dependencies, component amplitudes, and other parametric trends. Preliminary validations are carried out by using small scale experimental data, and two applications are discussed; one for conventional aircraft and the other for advanced configurations. The former focuses on the parametric trends of Krueger noise on design parameters, while the latter reveals its importance in relation to other airframe noise components.

  13. Prediction of Chemical Function: Model Development and ... (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

  14. Evaluating Predictive Models of Software Quality (United States)

    Ciaschini, V.; Canaparo, M.; Ronchieri, E.; Salomoni, D.


    Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

  15. Predicting FLDs Using a Multiscale Modeling Scheme (United States)

    Wu, Z.; Loy, C.; Wang, E.; Hegadekatte, V.


    The measurement of a single forming limit diagram (FLD) requires significant resources and is time consuming. We have developed a multiscale modeling scheme to predict FLDs using a combination of limited laboratory testing, crystal plasticity (VPSC) modeling, and dual sequential-stage finite element (ABAQUS/Explicit) modeling with the Marciniak-Kuczynski (M-K) criterion to determine the limit strain. We have established a means to work around existing limitations in ABAQUS/Explicit by using an anisotropic yield locus (e.g., BBC2008) in combination with the M-K criterion. We further apply a VPSC model to reduce the number of laboratory tests required to characterize the anisotropic yield locus. In the present work, we show that the predicted FLD is in excellent agreement with the measured FLD for AA5182 in the O temper. Instead of 13 different tests as for a traditional FLD determination within Novelis, our technique uses just four measurements: tensile properties in three orientations; plane strain tension; biaxial bulge; and the sheet crystallographic texture. The turnaround time is consequently far less than for the traditional laboratory measurement of the FLD.


    Directory of Open Access Journals (Sweden)

    Narciso Ysac Avila Serrano


    Full Text Available With the objective to characterize the grain yield of five cowpea cultivars and to find linear regression models to predict it, a study was developed in La Paz, Baja California Sur, Mexico. A complete randomized blocks design was used. Simple and multivariate analyses of variance were carried out using the canonical variables to characterize the cultivars. The variables cluster per plant, pods per plant, pods per cluster, seeds weight per plant, seeds hectoliter weight, 100-seed weight, seeds length, seeds wide, seeds thickness, pods length, pods wide, pods weight, seeds per pods, and seeds weight per pods, showed significant differences (P≤ 0.05 among cultivars. Paceño and IT90K-277-2 cultivars showed the higher seeds weight per plant. The linear regression models showed correlation coefficients ≥0.92. In these models, the seeds weight per plant, pods per cluster, pods per plant, cluster per plant and pods length showed significant correlations (P≤ 0.05. In conclusion, the results showed that grain yield differ among cultivars and for its estimation, the prediction models showed determination coefficients highly dependable.

  17. Evaluating predictive models of software quality

    International Nuclear Information System (INIS)

    Ciaschini, V; Canaparo, M; Ronchieri, E; Salomoni, D


    Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

  18. Gamma-Ray Pulsars Models and Predictions

    CERN Document Server

    Harding, A K


    Pulsed emission from gamma-ray pulsars originates inside the magnetosphere, from radiation by charged particles accelerated near the magnetic poles or in the outer gaps. In polar cap models, the high energy spectrum is cut off by magnetic pair production above an energy that is dependent on the local magnetic field strength. While most young pulsars with surface fields in the range B = 10^{12} - 10^{13} G are expected to have high energy cutoffs around several GeV, the gamma-ray spectra of old pulsars having lower surface fields may extend to 50 GeV. Although the gamma-ray emission of older pulsars is weaker, detecting pulsed emission at high energies from nearby sources would be an important confirmation of polar cap models. Outer gap models predict more gradual high-energy turnovers at around 10 GeV, but also predict an inverse Compton component extending to TeV energies. Detection of pulsed TeV emission, which would not survive attenuation at the polar caps, is thus an important test of outer gap models. N...

  19. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif


    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  20. Clinical Predictive Modeling Development and Deployment through FHIR Web Services. (United States)

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


    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.

  1. Comparison of various tool wear prediction methods during end milling of metal matrix composite (United States)

    Wiciak, Martyna; Twardowski, Paweł; Wojciechowski, Szymon


    In this paper, the problem of tool wear prediction during milling of hard-to-cut metal matrix composite Duralcan™ was presented. The conducted research involved the measurements of acceleration of vibrations during milling with constant cutting conditions, and evaluation of the flank wear. Subsequently, the analysis of vibrations in time and frequency domain, as well as the correlation of the obtained measures with the tool wear values were conducted. The validation of tool wear diagnosis in relation to selected diagnostic measures was carried out with the use of one variable and two variables regression models, as well as with the application of artificial neural networks (ANN). The comparative analysis of the obtained results enable.

  2. Probabilistic Modelling of Fatigue Life of Composite Laminates Using Bayesian Inference

    DEFF Research Database (Denmark)

    Dimitrov, Nikolay Krasimirov; Kiureghian, Armen Der


    A probabilistic model for estimating the fatigue life of laminated composite plates subjected to constant-amplitude or variable-amplitude loading is developed. The model is based on lamina-level input data, making it possible to predict fatigue properties for a wide range of laminate configuratio...

  3. Simulated small-angle scattering patterns for a plastically deformed model composite material

    NARCIS (Netherlands)

    Shenoy, V.B.; Cleveringa, H.H.M.; Phillips, R.; Giessen, E. van der; Needleman, A.


    The small-angle scattering patterns predicted by discrete dislocation plasticity versus local and non-local continuum plasticity theory are compared in a model problem. The problem considered is a two-dimensional model composite with elastic reinforcements in a crystalline matrix subject to

  4. On the elastic properties of carbon nanotube-based composites: modelling and characterization

    CERN Document Server

    Thostenson, E T


    The exceptional mechanical and physical properties observed for carbon nanotubes has stimulated the development of nanotube-based composite materials, but critical challenges exist before we can exploit these extraordinary nanoscale properties in a macroscopic composite. At the nanoscale, the structure of the carbon nanotube strongly influences the overall properties of the composite. The focus of this research is to develop a fundamental understanding of the structure/size influence of carbon nanotubes on the elastic properties of nanotube-based composites. Towards this end, the nanoscale structure and elastic properties of a model composite system of aligned multi-walled carbon nanotubes embedded in a polystyrene matrix were characterized, and a micromechanical approach for modelling of short fibre composites was modified to account for the structure of the nanotube reinforcement to predict the elastic modulus of the nanocomposite as a function of the constituent properties, reinforcement geometry and nanot...

  5. Modeling compositional dynamics based on GC and purine contents of protein-coding sequences

    KAUST Repository

    Zhang, Zhang; Yu, Jun


    Background: Understanding the compositional dynamics of genomes and their coding sequences is of great significance in gaining clues into molecular evolution and a large number of publically-available genome sequences have allowed us to quantitatively predict deviations of empirical data from their theoretical counterparts. However, the quantification of theoretical compositional variations for a wide diversity of genomes remains a major challenge.Results: To model the compositional dynamics of protein-coding sequences, we propose two simple models that take into account both mutation and selection effects, which act differently at the three codon positions, and use both GC and purine contents as compositional parameters. The two models concern the theoretical composition of nucleotides, codons, and amino acids, with no prerequisite of homologous sequences or their alignments. We evaluated the two models by quantifying theoretical compositions of a large collection of protein-coding sequences (including 46 of Archaea, 686 of Bacteria, and 826 of Eukarya), yielding consistent theoretical compositions across all the collected sequences.Conclusions: We show that the compositions of nucleotides, codons, and amino acids are largely determined by both GC and purine contents and suggest that deviations of the observed from the expected compositions may reflect compositional signatures that arise from a complex interplay between mutation and selection via DNA replication and repair mechanisms.Reviewers: This article was reviewed by Zhaolei Zhang (nominated by Mark Gerstein), Guruprasad Ananda (nominated by Kateryna Makova), and Daniel Haft. 2010 Zhang and Yu; licensee BioMed Central Ltd.

  6. Modeling compositional dynamics based on GC and purine contents of protein-coding sequences

    KAUST Repository

    Zhang, Zhang


    Background: Understanding the compositional dynamics of genomes and their coding sequences is of great significance in gaining clues into molecular evolution and a large number of publically-available genome sequences have allowed us to quantitatively predict deviations of empirical data from their theoretical counterparts. However, the quantification of theoretical compositional variations for a wide diversity of genomes remains a major challenge.Results: To model the compositional dynamics of protein-coding sequences, we propose two simple models that take into account both mutation and selection effects, which act differently at the three codon positions, and use both GC and purine contents as compositional parameters. The two models concern the theoretical composition of nucleotides, codons, and amino acids, with no prerequisite of homologous sequences or their alignments. We evaluated the two models by quantifying theoretical compositions of a large collection of protein-coding sequences (including 46 of Archaea, 686 of Bacteria, and 826 of Eukarya), yielding consistent theoretical compositions across all the collected sequences.Conclusions: We show that the compositions of nucleotides, codons, and amino acids are largely determined by both GC and purine contents and suggest that deviations of the observed from the expected compositions may reflect compositional signatures that arise from a complex interplay between mutation and selection via DNA replication and repair mechanisms.Reviewers: This article was reviewed by Zhaolei Zhang (nominated by Mark Gerstein), Guruprasad Ananda (nominated by Kateryna Makova), and Daniel Haft. 2010 Zhang and Yu; licensee BioMed Central Ltd.

  7. Direct and indirect signals of natural composite Higgs models (United States)

    Niehoff, Christoph; Stangl, Peter; Straub, David M.


    We present a comprehensive numerical analysis of a four-dimensional model with the Higgs as a composite pseudo-Nambu-Goldstone boson that features a calculable Higgs potential and protective custodial and flavour symmetries to reduce electroweak fine-tuning. We employ a novel numerical technique that allows us for the first time to study constraints from radiative electroweak symmetry breaking, Higgs physics, electroweak precision tests, flavour physics, and direct LHC bounds on fermion and vector boson resonances in a single framework. We consider four different flavour symmetries in the composite sector, one of which we show to not be viable anymore in view of strong precision constraints. In the other cases, all constraints can be passed with a sub-percent electroweak fine-tuning. The models can explain the excesses recently observed in WW, WZ, Wh and ℓ + ℓ - resonance searches by ATLAS and CMS and the anomalies in angular observables and branching ratios of rare semi-leptonic B decays observed by LHCb. Solving the B physics anomalies predicts the presence of a dijet or toverline{t} resonance around 1 TeV just below the sensitivity of LHC run 1. We discuss the prospects to probe the models at run 2 of the LHC. As a side product, we identify several gaps in the searches for vector-like quarks at hadron colliders, that could be closed by reanalyzing existing LHC data.

  8. Utilizing the non-bridge oxygen model to predict the glass viscosity

    International Nuclear Information System (INIS)

    Choi, Kwansik; Sheng, Jiawei; Maeng, Sung Jun; Song, Myung Jae


    Viscosity is the most important process property of waste glass. Viscosity measurement is difficult and costs much. Non-bridging Oxygen (NBO) model which relates glass composition to viscosity had been developed for high level waste at the Savannah River Site (SRS). This research utilized this NBO model to predict the viscosity of KEPRI's 55 glasses. It was found that there was a linear relationship between the measured viscosity and the predicted viscosity. The NBO model could be used to predict glass viscosity in glass formulation development. However the precision of predicted viscosity is out of satisfaction because the composition ranges are very different between the SRS and KEPRI glasses. The modification of NBO calculation, which included modification of alkaline earth elements and TiO 2 , could not strikingly improve the precision of predicted values

  9. Modelling Behaviour of a Carbon Epoxy Composite Exposed to Fire: Part II-Comparison with Experimental Results. (United States)

    Tranchard, Pauline; Samyn, Fabienne; Duquesne, Sophie; Estèbe, Bruno; Bourbigot, Serge


    Based on a phenomenological methodology, a three dimensional (3D) thermochemical model was developed to predict the temperature profile, the mass loss and the decomposition front of a carbon-reinforced epoxy composite laminate (T700/M21 composite) exposed to fire conditions. This 3D model takes into account the energy accumulation by the solid material, the anisotropic heat conduction, the thermal decomposition of the material, the gas mass flow into the composite, and the internal pressure. Thermophysical properties defined as temperature dependant properties were characterised using existing as well as innovative methodologies in order to use them as inputs into our physical model. The 3D thermochemical model accurately predicts the measured mass loss and observed decomposition front when the carbon fibre/epoxy composite is directly impacted by a propane flame. In short, the model shows its capability to predict the fire behaviour of a carbon fibre reinforced composite for fire safety engineering.

  10. Prediction of Material Properties of Nanostructured Polymer Composites Using Atomistic Simulations (United States)

    Hinkley, J.A.; Clancy, T.C.; Frankland, S.J.V.


    Atomistic models of epoxy polymers were built in order to assess the effect of structure at the nanometer scale on the resulting bulk properties such as elastic modulus and thermal conductivity. Atomistic models of both bulk polymer and carbon nanotube polymer composites were built. For the bulk models, the effect of moisture content and temperature on the resulting elastic constants was calculated. A relatively consistent decrease in modulus was seen with increasing temperature. The dependence of modulus on moisture content was less consistent. This behavior was seen for two different epoxy systems, one containing a difunctional epoxy molecule and the other a tetrafunctional epoxy molecule. Both epoxy structures were crosslinked with diamine curing agents. Multifunctional properties were calculated with the nanocomposite models. Molecular dynamics simulation was used to estimate the interfacial thermal (Kapitza) resistance between the carbon nanotube and the surrounding epoxy matrix. These estimated values were used in a multiscale model in order to predict the thermal conductivity of a nanocomposite as a function of the nanometer scaled molecular structure.

  11. 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.


    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.

  12. An analytical model for climatic predictions

    International Nuclear Information System (INIS)

    Njau, E.C.


    A climatic model based upon analytical expressions is presented. This model is capable of making long-range predictions of heat energy variations on regional or global scales. These variations can then be transformed into corresponding variations of some other key climatic parameters since weather and climatic changes are basically driven by differential heating and cooling around the earth. On the basis of the mathematical expressions upon which the model is based, it is shown that the global heat energy structure (and hence the associated climatic system) are characterized by zonally as well as latitudinally propagating fluctuations at frequencies downward of 0.5 day -1 . We have calculated the propagation speeds for those particular frequencies that are well documented in the literature. The calculated speeds are in excellent agreement with the measured speeds. (author). 13 refs

  13. An Anisotropic Hardening Model for Springback Prediction (United States)

    Zeng, Danielle; Xia, Z. Cedric


    As more Advanced High-Strength Steels (AHSS) are heavily used for automotive body structures and closures panels, accurate springback prediction for these components becomes more challenging because of their rapid hardening characteristics and ability to sustain even higher stresses. In this paper, a modified Mroz hardening model is proposed to capture realistic Bauschinger effect at reverse loading, such as when material passes through die radii or drawbead during sheet metal forming process. This model accounts for material anisotropic yield surface and nonlinear isotropic/kinematic hardening behavior. Material tension/compression test data are used to accurately represent Bauschinger effect. The effectiveness of the model is demonstrated by comparison of numerical and experimental springback results for a DP600 straight U-channel test.

  14. An Anisotropic Hardening Model for Springback Prediction

    International Nuclear Information System (INIS)

    Zeng, Danielle; Xia, Z. Cedric


    As more Advanced High-Strength Steels (AHSS) are heavily used for automotive body structures and closures panels, accurate springback prediction for these components becomes more challenging because of their rapid hardening characteristics and ability to sustain even higher stresses. In this paper, a modified Mroz hardening model is proposed to capture realistic Bauschinger effect at reverse loading, such as when material passes through die radii or drawbead during sheet metal forming process. This model accounts for material anisotropic yield surface and nonlinear isotropic/kinematic hardening behavior. Material tension/compression test data are used to accurately represent Bauschinger effect. The effectiveness of the model is demonstrated by comparison of numerical and experimental springback results for a DP600 straight U-channel test

  15. Predicting Madura cattle growth curve using non-linear model (United States)

    Widyas, N.; Prastowo, S.; Widi, T. S. M.; Baliarti, E.


    Madura cattle is Indonesian native. It is a composite breed that has undergone hundreds of years of selection and domestication to reach nowadays remarkable uniformity. Crossbreeding has reached the isle of Madura and the Madrasin, a cross between Madura cows and Limousine semen emerged. This paper aimed to compare the growth curve between Madrasin and one type of pure Madura cows, the common Madura cattle (Madura) using non-linear models. Madura cattles are kept traditionally thus reliable records are hardly available. Data were collected from small holder farmers in Madura. Cows from different age classes (5years) were observed, and body measurements (chest girth, body length and wither height) were taken. In total 63 Madura and 120 Madrasin records obtained. Linear model was built with cattle sub-populations and age as explanatory variables. Body weights were estimated based on the chest girth. Growth curves were built using logistic regression. Results showed that within the same age, Madrasin has significantly larger body compared to Madura (plogistic models fit better for Madura and Madrasin cattle data; with the estimated MSE for these models were 39.09 and 759.28 with prediction accuracy of 99 and 92% for Madura and Madrasin, respectively. Prediction of growth curve using logistic regression model performed well in both types of Madura cattle. However, attempts to administer accurate data on Madura cattle are necessary to better characterize and study these cattle.

  16. Predictive Engineering Tools for Injection-Molded Long-Carbon-Thermoplastic Composites: Weight and Cost Analyses

    Energy Technology Data Exchange (ETDEWEB)

    Nguyen, Ba Nghiep [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Fifield, Leonard S. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Gandhi, Umesh N. [Toyota Research Inst. North America, Ann Arbor, MI (United States); Mori, Steven [MAGNA Exteriors and Interiors Corporation, Aurora, ON (Canada); Wollan, Eric J. [PlastiComp, Inc., Winona, MN (United States)


    This project proposed to integrate, optimize and validate the fiber orientation and length distribution models previously developed and implemented in the Autodesk Simulation Moldflow Insight (ASMI) package for injection-molded long-carbon-fiber thermoplastic composites into a cohesive prediction capability. The current effort focused on rendering the developed models more robust and efficient for automotive industry part design to enable weight savings and cost reduction. The project goal has been achieved by optimizing the developed models, improving and integrating their implementations in ASMI, and validating them for a complex 3D LCF thermoplastic automotive part (Figure 1). Both PP and PA66 were used as resin matrices. After validating ASMI predictions for fiber orientation and fiber length for this complex part against the corresponding measured data, in collaborations with Toyota and Magna PNNL developed a method using the predictive engineering tool to assess LCF/PA66 complex part design in terms of stiffness performance. Structural three-point bending analyses of the complex part and similar parts in steel were then performed for this purpose, and the team has then demonstrated the use of stiffness-based complex part design assessment to evaluate weight savings relative to the body system target (≥ 35%) set in Table 2 of DE-FOA-0000648 (AOI #1). In addition, starting from the part-to-part analysis, the PE tools enabled an estimated weight reduction for the vehicle body system using 50 wt% LCF/PA66 parts relative to the current steel system. Also, from this analysis an estimate of the manufacturing cost including the material cost for making the equivalent part in steel has been determined and compared to the costs for making the LCF/PA66 part to determine the cost per “saved” pound.

  17. Extreme events and predictability of catastrophic failure in composite materials and in the Earth (United States)

    Main, I.; Naylor, M.


    Despite all attempts to isolate and predict extreme earthquakes, these nearly always occur without obvious warning in real time: fully deterministic earthquake prediction is very much a `black swan'. On the other hand engineering-scale samples of rocks and other composite materials often show clear precursors to dynamic failure under controlled conditions in the laboratory, and successful evacuations have occurred before several volcanic eruptions. This may be because extreme earthquakes are not statistically special, being an emergent property of the process of dynamic rupture. Nevertheless, probabilistic forecasting of event rate above a given size, based on the tendency of earthquakes to cluster in space and time, can have significant skill compared to say random failure, even in real-time mode. We address several questions in this debate, using examples from the Earth (earthquakes, volcanoes) and the laboratory, including the following. How can we identify `characteristic' events, i.e. beyond the power law, in model selection (do dragon-kings exist)? How do we discriminate quantitatively between stationary and non-stationary hazard models (is a dragon likely to come soon)? Does the system size (the size of the dragon's domain) matter? Are there localising signals of imminent catastrophic failure we may not be able to access (is the dragon effectively invisible on approach)? We focus on the effect of sampling effects and statistical uncertainty in the identification of extreme events and their predictability, and highlight the strong influence of scaling in space and time as an outstanding issue to be addressed by quantitative studies, experimentation and models.

  18. Model Lung Surfactant Films: Why Composition Matters

    Energy Technology Data Exchange (ETDEWEB)

    Selladurai, Sahana L.; Miclette Lamarche, Renaud; Schmidt, Rolf; DeWolf, Christine E.


    Lung surfactant replacement therapies, Survanta and Infasurf, and two lipid-only systems both containing saturated and unsaturated phospholipids and one containing additional palmitic acid were used to study the impact of buffered saline on the surface activity, morphology, rheology, and structure of Langmuir monolayer model membranes. Isotherms and Brewster angle microscopy show that buffered saline subphases induce a film expansion, except when the cationic protein, SP-B, is present in sufficient quantities to already screen electrostatic repulsion, thus limiting the effect of changing pH and adding counterions. Grazing incidence X-ray diffraction results indicate an expansion not only of the liquid expanded phase but also an expansion of the lattice of the condensed phase. The film expansion corresponded in all cases with a significant reduction in the viscosity and elasticity of the films. The viscoelastic parameters are dominated by liquid expanded phase properties and do not appear to be dependent on the structure of the condensed phase domains in a phase separated film. The results highlight that the choice of subphase and film composition is important for meaningful interpretations of measurements using model systems.

  19. Multi-physics modeling of multifunctional composite materials for damage detection (United States)

    Sujidkul, Thanyawalai

    This study presents a modeling of multifunction composite materials for damage detection with its verification and validation to mechanical behavior predictions of Carbon Fibre Reinforced Polymer composites (CFRPs), CFRPs laminated composites, and woven SiC/SiC matrix composites that are subjected to fracture damage. Advantages of those materials are low cost, low density, high strength-to-weight ratio, and comparable specific tensile properties, the special of SiC/SiC is good environmental stability at high temperature. Resulting in, the composite has been used for many important structures such as helicopter rotors, aerojet engines, gas turbines, hot control surfaces, sporting goods, and windmill blades. Damage or material defect detection in a mechanical component can provide vital information for the prediction of remaining useful life, which will result in the prevention of catastrophic failures. Thus the understanding of the mechanical behavior have been challenge to the prevent damage and failure of composites in different scales. The damage detection methods in composites have been investigated widely in recent years. Non-destructive techniques are the traditional methods to detect the damage such as X-ray, acoustic emission and thermography. However, due to the invisible damage in composite can be occurred, to prevent the failure in composites. The developments of damage detection methods have been considered. Due to carbon fibers are conductive materials, in resulting CFRPs can be self-sensing to detect damage. As is well known, the electrical resistance has been shown to be a sensitive measure of internal damage, and also this work study in thermal resistance can detect damage in composites. However, there is a few number of different micromechanical modeling schemes has been proposed in the published literature for various types of composites. This works will provide with a numerical, analytical, and theoretical failure models in different damages to

  20. Web tools for predictive toxicology model building. (United States)

    Jeliazkova, Nina


    The development and use of web tools in chemistry has accumulated more than 15 years of history already. Powered by the advances in the Internet technologies, the current generation of web systems are starting to expand into areas, traditional for desktop applications. The web platforms integrate data storage, cheminformatics and data analysis tools. The ease of use and the collaborative potential of the web is compelling, despite the challenges. The topic of this review is a set of recently published web tools that facilitate predictive toxicology model building. The focus is on software platforms, offering web access to chemical structure-based methods, although some of the frameworks could also provide bioinformatics or hybrid data analysis functionalities. A number of historical and current developments are cited. In order to provide comparable assessment, the following characteristics are considered: support for workflows, descriptor calculations, visualization, modeling algorithms, data management and data sharing capabilities, availability of GUI or programmatic access and implementation details. The success of the Web is largely due to its highly decentralized, yet sufficiently interoperable model for information access. The expected future convergence between cheminformatics and bioinformatics databases provides new challenges toward management and analysis of large data sets. The web tools in predictive toxicology will likely continue to evolve toward the right mix of flexibility, performance, scalability, interoperability, sets of unique features offered, friendly user interfaces, programmatic access for advanced users, platform independence, results reproducibility, curation and crowdsourcing utilities, collaborative sharing and secure access.

  1. Predictive Engineering Tools for Injection-Molded Long-Carbon-Fiber Thermoplastic Composites. Topical Report

    Energy Technology Data Exchange (ETDEWEB)

    Nguyen, Ba Nghiep [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Fifield, Leonard S. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Wang, Jin [Autodesk, Inc., Ithaca, NY (United States); Costa, Franco [Autodesk, Inc., Ithaca, NY (United States); Lambert, Gregory [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Baird, Donald G. [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Sharma, Bhisham A. [Purdue Univ., West Lafayette, IN (United States); Kijewski, Seth A. [Purdue Univ., West Lafayette, IN (United States); Sangid, Michael D. [Purdue Univ., West Lafayette, IN (United States); Gandhi, Umesh N. [Toyota Research Inst. North America, Ann Arbor, MI (United States); Wollan, Eric J. [PlastiComp, Inc., Winona, MN (United States); Roland, Dale [PlastiComp, Inc., Winona, MN (United States); Mori, Steven [Magna Exteriors and Interiors Corporation, Aurora, ON (Canada); Tucker, III, Charles L. [Univ. of Illinois, Urbana-Champaign, IL (United States)


    This project aimed to integrate, optimize, and validate the fiber orientation and length distribution models previously developed and implemented in the Autodesk® Simulation Moldflow® Insight (ASMI) software package for injection-molded long-carbon-fiber (LCF) thermoplastic composite structures. The project was organized into two phases. Phase 1 demonstrated the ability of the advanced ASMI package to predict fiber orientation and length distributions in LCF/polypropylene (PP) and LCF/polyamide-6, 6 (PA66) plaques within 15% of experimental results. Phase 2 validated the advanced ASMI package by predicting fiber orientation and length distributions within 15% of experimental results for a complex three-dimensional (3D) Toyota automotive part injection-molded from LCF/PP and LCF/PA66 materials. Work under Phase 2 also included estimate of weight savings and cost impacts for a vehicle system using ASMI and structural analyses of the complex part. The present report summarizes the completion of Phases 1 and 2 work activities and accomplishments achieved by the team comprising Pacific Northwest National Laboratory (PNNL); Purdue University (Purdue); Virginia Polytechnic Institute and State University (Virginia Tech); Autodesk, Inc. (Autodesk); PlastiComp, Inc. (PlastiComp); Toyota Research Institute North America (Toyota); Magna Exteriors and Interiors Corp. (Magna); and University of Illinois. Figure 1 illustrates the technical approach adopted in this project that progressed from compounding LCF/PP and LCF/PA66 materials, to process model improvement and implementation, to molding and modeling LCF/PP and LCF/PA66 plaques. The lessons learned from the plaque study and the successful validation of improved process models for fiber orientation and length distributions for these plaques enabled the project to go to Phase 2 to mold, model, and optimize the 3D complex part.

  2. Predictions of models for environmental radiological assessment

    International Nuclear Information System (INIS)

    Peres, Sueli da Silva; Lauria, Dejanira da Costa; Mahler, Claudio Fernando


    In the field of environmental impact assessment, models are used for estimating source term, environmental dispersion and transfer of radionuclides, exposure pathway, radiation dose and the risk for human beings Although it is recognized that the specific information of local data are important to improve the quality of the dose assessment results, in fact obtaining it can be very difficult and expensive. Sources of uncertainties are numerous, among which we can cite: the subjectivity of modelers, exposure scenarios and pathways, used codes and general parameters. The various models available utilize different mathematical approaches with different complexities that can result in different predictions. Thus, for the same inputs different models can produce very different outputs. This paper presents briefly the main advances in the field of environmental radiological assessment that aim to improve the reliability of the models used in the assessment of environmental radiological impact. The intercomparison exercise of model supplied incompatible results for 137 Cs and 60 Co, enhancing the need for developing reference methodologies for environmental radiological assessment that allow to confront dose estimations in a common comparison base. The results of the intercomparison exercise are present briefly. (author)

  3. Modeling the curing process of thermosetting resin matrix composites (United States)

    Loos, A. C.


    A model is presented for simulating the curing process of a thermosetting resin matrix composite. The model relates the cure temperature, the cure pressure, and the properties of the prepreg to the thermal, chemical, and rheological processes occurring in the composite during cure. The results calculated with the computer code developed on the basis of the model were compared with the experimental data obtained from autoclave-curved composite laminates. Good agreement between the two sets of results was obtained.

  4. A Predictive Maintenance Model for Railway Tracks

    DEFF Research Database (Denmark)

    Li, Rui; Wen, Min; Salling, Kim Bang


    presents a mathematical model based on Mixed Integer Programming (MIP) which is designed to optimize the predictive railway tamping activities for ballasted track for the time horizon up to four years. The objective function is setup to minimize the actual costs for the tamping machine (measured by time......). Five technical and economic aspects are taken into account to schedule tamping: (1) track degradation of the standard deviation of the longitudinal level over time; (2) track geometrical alignment; (3) track quality thresholds based on the train speed limits; (4) the dependency of the track quality...

  5. School Processes Mediate School Compositional Effects: Model Specification and Estimation (United States)

    Liu, Hongqiang; Van Damme, Jan; Gielen, Sarah; Van Den Noortgate, Wim


    School composition effects have been consistently verified, but few studies ever attempted to study how school composition affects school achievement. Based on prior research findings, we employed multilevel mediation modeling to examine whether school processes mediate the effect of school composition upon school outcomes based on the data of 28…

  6. Predictive Capability Maturity Model for computational modeling and simulation.

    Energy Technology Data Exchange (ETDEWEB)

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


    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.

  7. Effective modelling for predictive analytics in data science ...

    African Journals Online (AJOL)

    Effective modelling for predictive analytics in data science. ... the nearabsence of empirical or factual predictive analytics in the mainstream research going on ... Keywords: Predictive Analytics, Big Data, Business Intelligence, Project Planning.

  8. Artificial Neural Networks for the Prediction of Wear Properties of Al6061-TiO2 Composites (United States)

    Veeresh Kumar, G. B.; Pramod, R.; Shivakumar Gouda, P. S.; Rao, C. S. P.


    The exceptional performance of composite materials in comparison with the monolithic materials have been extensively studied by researchers. Among the metal matrix composites Aluminium matrix based composites have displayed superior mechanical properties. The aluminium 6061 alloy has been used in aeronautical and automotive components, but their resistance against the wear is poor. To enhance the wear properties, Titanium dioxide (TiO2) particulates have been used as reinforcements. In the present investigation Back propagation (BP) technique has been adopted for Artificial Neural Network [ANN] modelling. The wear experimentations were carried out on a pin-on-disc wear monitoring apparatus. For conduction of wear tests ASTM G99 was adopted. Experimental design was carried out using Taguchi L27 orthogonal array. The sliding distance, weight percentage of the reinforcement material and applied load have a substantial influence on the height damage due to wear of the Al6061 and Al6061-TiO2 filled composites. The Al6061 with 3 wt% TiO2 composite displayed an excellent wear resistance in comparison with other composites investigated. A non-linear relationship between density, applied load, weight percentage of reinforcement, sliding distance and height decrease due to wear has been established using an artificial neural network. A good agreement has been observed between experimental and ANN model predicted results.

  9. A nonlinear model for ionic polymer metal composites as actuators (United States)

    Bonomo, C.; Fortuna, L.; Giannone, P.; Graziani, S.; Strazzeri, S.


    This paper introduces a comprehensive nonlinear dynamic model of motion actuators based on ionic polymer metal composites (IPMCs) working in air. Significant quantities ruling the acting properties of IPMC-based actuators are taken into account. The model is organized as follows. As a first step, the dependence of the IPMC absorbed current on the voltage applied across its thickness is taken into account; a nonlinear circuit model is proposed to describe this relationship. In a second step the transduction of the absorbed current into the IPMC mechanical reaction is modelled. The model resulting from the cascade of both the electrical and the electromechanical stages represents a novel contribution in the field of IPMCs, capable of describing the electromechanical behaviour of these materials and predicting relevant quantities in a large range of applied signals. The effect of actuator scaling is also investigated, giving interesting support to the activities involved in the design of actuating devices based on these novel materials. Evidence of the excellent agreement between the estimations obtained by using the proposed model and experimental signals is given.

  10. Combining GPS measurements and IRI model predictions

    International Nuclear Information System (INIS)

    Hernandez-Pajares, M.; Juan, J.M.; Sanz, J.; Bilitza, D.


    The free electrons distributed in the ionosphere (between one hundred and thousands of km in height) produce a frequency-dependent effect on Global Positioning System (GPS) signals: a delay in the pseudo-orange and an advance in the carrier phase. These effects are proportional to the columnar electron density between the satellite and receiver, i.e. the integrated electron density along the ray path. Global ionospheric TEC (total electron content) maps can be obtained with GPS data from a network of ground IGS (international GPS service) reference stations with an accuracy of few TEC units. The comparison with the TOPEX TEC, mainly measured over the oceans far from the IGS stations, shows a mean bias and standard deviation of about 2 and 5 TECUs respectively. The discrepancies between the STEC predictions and the observed values show an RMS typically below 5 TECUs (which also includes the alignment code noise). he existence of a growing database 2-hourly global TEC maps and with resolution of 5x2.5 degrees in longitude and latitude can be used to improve the IRI prediction capability of the TEC. When the IRI predictions and the GPS estimations are compared for a three month period around the Solar Maximum, they are in good agreement for middle latitudes. An over-determination of IRI TEC has been found at the extreme latitudes, the IRI predictions being, typically two times higher than the GPS estimations. Finally, local fits of the IRI model can be done by tuning the SSN from STEC GPS observations

  11. Predicting placebo response in adolescents with major depressive disorder: The Adolescent Placebo Impact Composite Score (APICS). (United States)

    Nakonezny, Paul A; Mayes, Taryn L; Byerly, Matthew J; Emslie, Graham J


    The aim of this study was to construct a composite scoring system to predict the probability of placebo response in adolescents with Major Depressive Disorder (MDD). Participants of the current study were 151 adolescents (aged 12-17 years) who were randomized to the placebo arm (placebo transdermal patches) of a randomized controlled trial (RCT) comparing the selegiline transdermal patch with placebo (DelBello et al., 2014). The primary outcome of response was defined as a CGI-I score of 1 or 2 (very much or much improved) at week 12 (study-end) or exit. As a first step, a multiple logistic mixed model was used to estimate the odds of placebo response from each predictor in the model, including age, CDRS-R total at baseline (depressive symptom severity), history of recurrent depression (yes vs. no), sex (female vs. male), and race (non-Caucasian vs. Caucasian). On the basis of the initial logistic mixed model analysis, we then constructed an Adolescent Placebo Impact Composite Score (APICS) that became the sole predictor in a re-specified Bayesian logistic regression model to estimate the probability of placebo response. Finally, the AUC for the APICS was tested against a nominal area of 0.50 to evaluate how well the APICS discriminated placebo response status. Among the 151 adolescents, with a mean age of 14.6 years (SD = 1.6) and a mean baseline CDRS-R total of 60.6 (SD = 12.1), 68.2% were females, 50.3% was Caucasian, and 39.7% had a history of recurrent depression. Placebo response rate was 58.3%. Based on the logistic mixed model, the re-specified equation with the highest discriminatory ability to estimate the probability of placebo response was APICS = age + (0.32 × CDRS-R Total at baseline) + (-2.85 × if female) + (-5.50 × if history of recurrent depression) + (-5.85 × if non-Caucasian). The AUC for this model was 0.59 (p = .049). Within a Bayesian decision-theoretic framework, in 95.5% of the time, the 10,000 posterior Monte Carlo samples suggested

  12. Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models. (United States)

    Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F


    In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.

  13. Post-impact performance of composites; Predicting Compression after Impact (CAI) in composite laminates

    NARCIS (Netherlands)

    Esrall, F.


    Impact damage has been known to seriously limit the performance of composite aircraft structures. In the preliminary design phase, tens of thousands of subparts need to be analyzed for impact. Over the years, many approaches have been proposed to study the creation of impact damage and to determine

  14. Mathematical models for indoor radon prediction

    International Nuclear Information System (INIS)

    Malanca, A.; Pessina, V.; Dallara, G.


    It is known that the indoor radon (Rn) concentration can be predicted by means of mathematical models. The simplest model relies on two variables only: the Rn source strength and the air exchange rate. In the Lawrence Berkeley Laboratory (LBL) model several environmental parameters are combined into a complex equation; besides, a correlation between the ventilation rate and the Rn entry rate from the soil is admitted. The measurements were carried out using activated carbon canisters. Seventy-five measurements of Rn concentrations were made inside two rooms placed on the second floor of a building block. One of the rooms had a single-glazed window whereas the other room had a double pane window. During three different experimental protocols, the mean Rn concentration was always higher into the room with a double-glazed window. That behavior can be accounted for by the simplest model. A further set of 450 Rn measurements was collected inside a ground-floor room with a grounding well in it. This trend maybe accounted for by the LBL model

  15. Towards predictive models for transitionally rough surfaces (United States)

    Abderrahaman-Elena, Nabil; Garcia-Mayoral, Ricardo


    We analyze and model the previously presented decomposition for flow variables in DNS of turbulence over transitionally rough surfaces. The flow is decomposed into two contributions: one produced by the overlying turbulence, which has no footprint of the surface texture, and one induced by the roughness, which is essentially the time-averaged flow around the surface obstacles, but modulated in amplitude by the first component. The roughness-induced component closely resembles the laminar steady flow around the roughness elements at the same non-dimensional roughness size. For small - yet transitionally rough - textures, the roughness-free component is essentially the same as over a smooth wall. Based on these findings, we propose predictive models for the onset of the transitionally rough regime. Project supported by the Engineering and Physical Sciences Research Council (EPSRC).

  16. Long Fibre Composite Modelling Using Cohesive User's Element

    International Nuclear Information System (INIS)

    Kozak, Vladislav; Chlup, Zdenek


    The development glass matrix composites reinforced by unidirectional long ceramic fibre has resulted in a family of very perspective structural materials. The only disadvantage of such materials is relatively high brittleness at room temperature. The main micromechanisms acting as toughening mechanism are the pull out, crack bridging, matrix cracking. There are other mechanisms as crack deflection etc. but the primer mechanism is mentioned pull out which is governed by interface between fibre and matrix. The contribution shows a way how to predict and/or optimise mechanical behaviour of composite by application of cohesive zone method and write user's cohesive element into the FEM numerical package Abaqus. The presented results from numerical calculations are compared with experimental data. Crack extension is simulated by means of element extinction algorithms. The principal effort is concentrated on the application of the cohesive zone model with the special traction separation (bridging) law and on the cohesive zone modelling. Determination of micro-mechanical parameters is based on the combination of static tests, microscopic observations and numerical calibration procedures.

  17. Resource-estimation models and predicted discovery

    International Nuclear Information System (INIS)

    Hill, G.W.


    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)

  18. Modelling Spatial Compositional Data: Reconstructions of past land cover and uncertainties

    DEFF Research Database (Denmark)

    Pirzamanbein, Behnaz; Lindström, Johan; Poska, Anneli


    In this paper, we construct a hierarchical model for spatial compositional data, which is used to reconstruct past land-cover compositions (in terms of coniferous forest, broadleaved forest, and unforested/open land) for five time periods during the past $6\\,000$ years over Europe. The model...... to a fast MCMC algorithm. Reconstructions are obtained by combining pollen-based estimates of vegetation cover at a limited number of locations with scenarios of past deforestation and output from a dynamic vegetation model. To evaluate uncertainties in the predictions a novel way of constructing joint...... confidence regions for the entire composition at each prediction location is proposed. The hierarchical model's ability to reconstruct past land cover is evaluated through cross validation for all time periods, and by comparing reconstructions for the recent past to a present day European forest map...

  19. 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)


    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

  20. Prediction of pipeline corrosion rate based on grey Markov models

    International Nuclear Information System (INIS)

    Chen Yonghong; Zhang Dafa; Peng Guichu; Wang Yuemin


    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)

  1. An Operational Model for the Prediction of Jet Blast (United States)


    This paper presents an operational model for the prediction of jet blast. The model was : developed based upon three modules including a jet exhaust model, jet centerline decay : model and aircraft motion model. The final analysis was compared with d...

  2. Predicting ecosystem vulnerability to biodiversity loss from community composition. (United States)

    Heilpern, Sebastian A; Weeks, Brian C; Naeem, Shahid


    Ecosystems vary widely in their responses to biodiversity change, with some losing function dramatically while others are highly resilient. However, generalizations about how species- and community-level properties determine these divergent ecosystem responses have been elusive because potential sources of variation (e.g., trophic structure, compensation, functional trait diversity) are rarely evaluated in conjunction. Ecosystem vulnerability, or the likely change in ecosystem function following biodiversity change, is influenced by two types of species traits: response traits that determine species' individual sensitivities to environmental change, and effect traits that determine a species' contribution to ecosystem function. Here we extend the response-effect trait framework to quantify ecosystem vulnerability and show how trophic structure, within-trait variance, and among-trait covariance affect ecosystem vulnerability by linking extinction order and functional compensation. Using in silico trait-based simulations we found that ecosystem vulnerability increased when response and effect traits positively covaried, but this increase was attenuated by decreasing trait variance. Contrary to expectations, in these communities, both functional diversity and trophic structure increased ecosystem vulnerability. In contrast, ecosystem functions were resilient when response and effect traits covaried negatively, and variance had a positive effect on resiliency. Our results suggest that although biodiversity loss is often associated with decreases in ecosystem functions, such effects are conditional on trophic structure, and the variation within and covariation among response and effect traits. Taken together, these three factors can predict when ecosystems are poised to lose or gain function with ongoing biodiversity change. © 2018 by the Ecological Society of America.

  3. Data driven propulsion system weight prediction model (United States)

    Gerth, Richard J.


    The objective of the research was to develop a method to predict the weight of paper engines, i.e., engines that are in the early stages of development. The impetus for the project was the Single Stage To Orbit (SSTO) project, where engineers need to evaluate alternative engine designs. Since the SSTO is a performance driven project the performance models for alternative designs were well understood. The next tradeoff is weight. Since it is known that engine weight varies with thrust levels, a model is required that would allow discrimination between engines that produce the same thrust. Above all, the model had to be rooted in data with assumptions that could be justified based on the data. The general approach was to collect data on as many existing engines as possible and build a statistical model of the engines weight as a function of various component performance parameters. This was considered a reasonable level to begin the project because the data would be readily available, and it would be at the level of most paper engines, prior to detailed component design.

  4. Predictive modeling of emergency cesarean delivery.

    Directory of Open Access Journals (Sweden)

    Carlos Campillo-Artero

    Full Text Available To increase discriminatory accuracy (DA for emergency cesarean sections (ECSs.We prospectively collected data on and studied all 6,157 births occurring in 2014 at four public hospitals located in three different autonomous communities of Spain. To identify risk factors (RFs for ECS, we used likelihood ratios and logistic regression, fitted a classification tree (CTREE, and analyzed a random forest model (RFM. We used the areas under the receiver-operating-characteristic (ROC curves (AUCs to assess their DA.The magnitude of the LR+ for all putative individual RFs and ORs in the logistic regression models was low to moderate. Except for parity, all putative RFs were positively associated with ECS, including hospital fixed-effects and night-shift delivery. The DA of all logistic models ranged from 0.74 to 0.81. The most relevant RFs (pH, induction, and previous C-section in the CTREEs showed the highest ORs in the logistic models. The DA of the RFM and its most relevant interaction terms was even higher (AUC = 0.94; 95% CI: 0.93-0.95.Putative fetal, maternal, and contextual RFs alone fail to achieve reasonable DA for ECS. It is the combination of these RFs and the interactions between them at each hospital that make it possible to improve the DA for the type of delivery and tailor interventions through prediction to improve the appropriateness of ECS indications.

  5. Predicting glass-forming compositions in the Al-La and Al-La-Ni systems

    International Nuclear Information System (INIS)

    Gargarella, P.; de Oliveira, M.F.; Kiminami, C.S.; Pauly, S.; Kuehn, U.; Bolfarini, C.; Botta, W.J.; Eckert, J.


    Research highlights: → The glass-forming ability of the Al-La and Al-La-Ni systems was studied using the λ* and the λ.Δe criteria. → Both criteria predicted with just 1% at. of error the best glass-former verified so far in the Al-La system. → Four new glass-former compositions could be predicted in the Al-La-Ni system using the λ.Δe criterion. → The best glass-former reported so far in the Al-La-Ni system was found. - Abstract: In this work, a criterion considering the topological instability (λ) and the differences in the electronegativity of the constituent elements (Δe) was applied to the Al-La and Al-Ni-La systems in order to predict the best glass-forming compositions. The results were compared with literature data and with our own experimental data for the Al-La-Ni system. The alloy described in the literature as the best glass former in the Al-La system is located near the point with local maximum for the λ.Δe criterion. A good agreement was found between the predictions of the λ.Δe criterion and literature data in the Al-La-Ni system, with the region of the best glass-forming ability (GFA) and largest supercooled liquid region (ΔT x ) coinciding with the best compositional region for amorphization indicated by the λ.Δe criterion. Four new glassy compositions were found in the Al-La-Ni system, with the best predicted composition presenting the best glass-forming ability observed so far for this system. Although the λ.Δe criterion needs further refinements for completely describe the glass-forming ability in the Al-La and Al-La-Ni systems, the results demonstrated that this criterion is a good tool to predict new glass-forming compositions.

  6. Model Predictive Control based on Finite Impulse Response Models

    DEFF Research Database (Denmark)

    Prasath, Guru; Jørgensen, John Bagterp


    We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations...... and related to the uncertainty of the impulse response coefficients. The simulations can be used to benchmark l2 MPC against FIR based robust MPC as well as to estimate the maximum performance improvements by robust MPC....

  7. Loading Analysis of Composite Wind Turbine Blade for Fatigue Life Prediction of Adhesively Bonded Root Joint (United States)

    Salimi-Majd, Davood; Azimzadeh, Vahid; Mohammadi, Bijan


    Nowadays wind energy is widely used as a non-polluting cost-effective renewable energy resource. During the lifetime of a composite wind turbine which is about 20 years, the rotor blades are subjected to different cyclic loads such as aerodynamics, centrifugal and gravitational forces. These loading conditions, cause to fatigue failure of the blade at the adhesively bonded root joint, where the highest bending moments will occur and consequently, is the most critical zone of the blade. So it is important to estimate the fatigue life of the root joint. The cohesive zone model is one of the best methods for prediction of initiation and propagation of debonding at the root joint. The advantage of this method is the possibility of modeling the debonding without any requirement to the remeshing. However in order to use this approach, it is necessary to analyze the cyclic loading condition at the root joint. For this purpose after implementing a cohesive interface element in the Ansys finite element software, one blade of a horizontal axis wind turbine with 46 m rotor diameter was modelled in full scale. Then after applying loads on the blade under different condition of the blade in a full rotation, the critical condition of the blade is obtained based on the delamination index and also the load ratio on the root joint in fatigue cycles is calculated. These data are the inputs for fatigue damage growth analysis of the root joint by using CZM approach that will be investigated in future work.

  8. Prediction of Creep Behaviour of the Hybrid Composite Material Using the Accelerated Characterisation Method

    International Nuclear Information System (INIS)

    Larbi, S.; Berradj, M.; Djebbar, A.; Bilek, A.


    We present in this study a creep behavior in flexure of a hybrid composite consisting of a polyester matrix containing methyl methacrylate reinforced by two bidirectional fabrics. The first one is made with E-glass fibers and the second one is made of a knitted polyamide 66. The mass fractions are 13% for the glass fabric and 9% for the polyamide fabric. The specimens, of dimensions (L = 60, l = 15 and h = 2.3 mm) containing 06 alternating layers (2P/2V/2P) were fabricated by using the vacuum bag molding method. Bending tests performed at different temperatures allowed us first to determine the load levels for the creep tests. Creep tests at different loads (5 to 43 MPa) and different temperatures (23'deg' to 80'deg' C) show a noticeable increase of creep deformation for both tests under the same load and different temperatures just as those carried out at different loads under the same temperature. The initial deformation varies significantly with the load but very little with temperature. The application of the Findley model shows good correlation with experimental results. Model parameters were identified. Creep deformation satisfies the principle of superposition time-temperature-stress (TTSSP). Findley's model has subsequently been coupled with the principle of superposition of time-temperature-stress to plot master curves at different stresses and temperatures; this enables prediction of creep deformation in the long term. (author)

  9. Case study on prediction of remaining methane potential of landfilled municipal solid waste by statistical analysis of waste composition data. (United States)

    Sel, İlker; Çakmakcı, Mehmet; Özkaya, Bestamin; Suphi Altan, H


    Main objective of this study was to develop a statistical model for easier and faster Biochemical Methane Potential (BMP) prediction of landfilled municipal solid waste by analyzing waste composition of excavated samples from 12 sampling points and three waste depths representing different landfilling ages of closed and active sections of a sanitary landfill site located in İstanbul, Turkey. Results of Principal Component Analysis (PCA) were used as a decision support tool to evaluation and describe the waste composition variables. Four principal component were extracted describing 76% of data set variance. The most effective components were determined as PCB, PO, T, D, W, FM, moisture and BMP for the data set. Multiple Linear Regression (MLR) models were built by original compositional data and transformed data to determine differences. It was observed that even residual plots were better for transformed data the R(2) and Adjusted R(2) values were not improved significantly. The best preliminary BMP prediction models consisted of D, W, T and FM waste fractions for both versions of regressions. Adjusted R(2) values of the raw and transformed models were determined as 0.69 and 0.57, respectively. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. Modelling and simulation of the consolidation behavior during thermoplastic prepreg composites forming process (United States)

    Xiong, H.; Hamila, N.; Boisse, P.


    Pre-impregnated thermoplastic composites have recently attached increasing interest in the automotive industry for their excellent mechanical properties and their rapid cycle manufacturing process, modelling and numerical simulations of forming processes for composites parts with complex geometry is necessary to predict and optimize manufacturing practices, especially for the consolidation effects. A viscoelastic relaxation model is proposed to characterize the consolidation behavior of thermoplastic prepregs based on compaction tests with a range of temperatures. The intimate contact model is employed to predict the evolution of the consolidation which permits the microstructure prediction of void presented through the prepreg. Within a hyperelastic framework, several simulation tests are launched by combining a new developed solid shell finite element and the consolidation models.

  11. Constitutive modeling of fiber-reinforced cement composites (United States)

    Boulfiza, Mohamed

    The role of fibers in the enhancement of the inherently low tensile stress and strain capacities of fiber reinforced cementitious composites (FRC) has been addressed through both the phenomenological, using concepts of continuum damage mechanics, and micro-mechanical approaches leading to the development of a closing pressure that could be used in a cohesive crack analysis. The observed enhancements in the matrix behavior is assumed to be related to the ability of the material to transfer stress across cracks. In the micromechanics approach, this is modeled by the introduction of a nonlinear closing pressure at the crack lips. Due to the different nature of cracking in the pre-peak and post peak regimes, two different micro-mechanical models of the cohesive pressure have been proposed, one for the strain hardening stage and another for the strain softening regime. This cohesive pressure is subsequently incorporated into a finite element code so that a nonlinear fracture analysis can be carried out. On top of the fact that a direct fracture analysis has been performed to predict the response of some FRC structural elements, a numerical procedure for the homogenization of FRC materials has been proposed. In this latter approach, a link is established between the cracking taking place at the meso-scale and its mechanical characteristics as represented by the Young's modulus. A parametric study has been carried out to investigate the effect of crack patterning and fiber volume fractions on the overall Young's modulus and the thermodynamic force associated with the tensorial damage variable. After showing the usefulness and power of phenomenological continuum damage mechanics (PCDM) in the prediction of ERC materials' response to a stimuli (loading), a combined PCDM-NLFMsp1 approach is proposed to model (predict, forecast) the complete response of the composite up to failure. Based on experimental observations, this approach assumes that damage mechanics which predicts

  12. Methodology for Designing Models Predicting Success of Infertility Treatment


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


    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...


    Directory of Open Access Journals (Sweden)

    A. I. Hinojosa

    Full Text Available Abstract In the process industry, advanced controllers usually aim at an economic objective, which usually requires closed-loop stability and constraints satisfaction. In this paper, the application of a MPC in the optimization structure of an industrial Propylene/Propane (PP splitter is tested with a controller based on a state space model, which is suitable for heavily disturbed environments. The simulation platform is based on the integration of the commercial dynamic simulator Dynsim® and the rigorous steady-state optimizer ROMeo® with the real-time facilities of Matlab. The predictive controller is the Infinite Horizon Model Predictive Control (IHMPC, based on a state-space model that that does not require the use of a state observer because the non-minimum state is built with the past inputs and outputs. The controller considers the existence of zone control of the outputs and optimizing targets for the inputs. We verify that the controller is efficient to control the propylene distillation system in a disturbed scenario when compared with a conventional controller based on a state observer. The simulation results show a good performance in terms of stability of the controller and rejection of large disturbances in the composition of the feed of the propylene distillation column.

  14. Prediction of the creep properties of discontinuous fibre composites from the matrix creep law

    International Nuclear Information System (INIS)

    Bilde-Soerensen, J.B.; Boecker Pedersen, O.; Lilholt, H.


    Existing theories for predicting the creep properties of discontinuous fibre composites with non-creeping fibres from matrix creep properties, originally based on a power law, are extended to include an exponential law, and in principle a general matrixlaw. An analysis shows that the composite creep curve can be obtained by a simple displacement of the matrix creep curve in a log sigma vs. log epsilon diagram. This principle, that each point on the matrix curve has a corresponding point on the composite curve,is given a physical interpretation. The direction of displacement is such that the transition from a power law toan exponential law occurs at a lower strain rate for the composite than for the unreinforced matrix. This emphasizes the importance of the exponential creep range in the creep of fibre composites. The combined use of matrix and composite data may allow the creep phenomenon to be studied over a larger range of strain rates than otherwise possible. A method for constructing generalized composite creep diagrams is suggested. Creep properties predicted from matrix data by the present analysis are compared with experimental data from the literature. (author)

  15. Inverse modeling with RZWQM2 to predict water quality (United States)

    Nolan, Bernard T.; Malone, Robert W.; Ma, Liwang; Green, Christopher T.; Fienen, Michael N.; Jaynes, Dan B.


    This chapter presents guidelines for autocalibration of the Root Zone Water Quality Model (RZWQM2) by inverse modeling using PEST parameter estimation software (Doherty, 2010). Two sites with diverse climate and management were considered for simulation of N losses by leaching and in drain flow: an almond [Prunus dulcis (Mill.) D.A. Webb] orchard in the San Joaquin Valley, California and the Walnut Creek watershed in central Iowa, which is predominantly in corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] rotation. Inverse modeling provides an objective statistical basis for calibration that involves simultaneous adjustment of model parameters and yields parameter confidence intervals and sensitivities. We describe operation of PEST in both parameter estimation and predictive analysis modes. The goal of parameter estimation is to identify a unique set of parameters that minimize a weighted least squares objective function, and the goal of predictive analysis is to construct a nonlinear confidence interval for a prediction of interest by finding a set of parameters that maximizes or minimizes the prediction while maintaining the model in a calibrated state. We also describe PEST utilities (PAR2PAR, TSPROC) for maintaining ordered relations among model parameters (e.g., soil root growth factor) and for post-processing of RZWQM2 outputs representing different cropping practices at the Iowa site. Inverse modeling provided reasonable fits to observed water and N fluxes and directly benefitted the modeling through: (i) simultaneous adjustment of multiple parameters versus one-at-a-time adjustment in manual approaches; (ii) clear indication by convergence criteria of when calibration is complete; (iii) straightforward detection of nonunique and insensitive parameters, which can affect the stability of PEST and RZWQM2; and (iv) generation of confidence intervals for uncertainty analysis of parameters and model predictions. Composite scaled sensitivities, which

  16. Finite Unification: Theory, Models and Predictions

    CERN Document Server

    Heinemeyer, S; Zoupanos, G


    All-loop Finite Unified Theories (FUTs) are very interesting N=1 supersymmetric Grand Unified Theories (GUTs) realising an old field theory dream, and moreover have a remarkable predictive power due to the required reduction of couplings. The reduction of the dimensionless couplings in N=1 GUTs is achieved by searching for renormalization group invariant (RGI) relations among them holding beyond the unification scale. Finiteness results from the fact that there exist RGI relations among dimensional couplings that guarantee the vanishing of all beta-functions in certain N=1 GUTs even to all orders. Furthermore developments in the soft supersymmetry breaking sector of N=1 GUTs and FUTs lead to exact RGI relations, i.e. reduction of couplings, in this dimensionful sector of the theory, too. Based on the above theoretical framework phenomenologically consistent FUTs have been constructed. Here we review FUT models based on the SU(5) and SU(3)^3 gauge groups and their predictions. Of particular interest is the Hig...

  17. Revised predictive equations for salt intrusion modelling in estuaries

    NARCIS (Netherlands)

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


    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

  18. TIPPtool: Compositional Specification and Analysis of Markovian Performance Models

    NARCIS (Netherlands)

    Hermanns, H.; Halbwachs, N.; Peled, D.; Mertsiotakis, V.; Siegle, M.


    In this short paper we briefly describe a tool which is based on a Markovian stochastic process algebra. The tool offers both model specification and quantitative model analysis in a compositional fashion, wrapped in a userfriendly graphical front-end.

  19. Advanced moisture modeling of polymer composites. (United States)


    Long term moisture exposure has been shown to affect the mechanical performance of polymeric composite structures. This reduction : in mechanical performance must be considered during product design in order to ensure long term structure survival. In...

  20. Neutrino nucleosynthesis in supernovae: Shell model predictions

    International Nuclear Information System (INIS)

    Haxton, W.C.


    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

  1. Hierarchical Model Predictive Control for Resource Distribution

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Trangbæk, K; Stoustrup, Jakob


    units. The approach is inspired by smart-grid electric power production and consumption systems, where the flexibility of a large number of power producing and/or power consuming units can be exploited in a smart-grid solution. The objective is to accommodate the load variation on the grid, arising......This paper deals with hierarchichal model predictive control (MPC) of distributed systems. A three level hierachical approach is proposed, consisting of a high level MPC controller, a second level of so-called aggregators, controlled by an online MPC-like algorithm, and a lower level of autonomous...... on one hand from varying consumption, on the other hand by natural variations in power production e.g. from wind turbines. The approach presented is based on quadratic optimization and possess the properties of low algorithmic complexity and of scalability. In particular, the proposed design methodology...

  2. Distributed model predictive control made easy

    CERN Document Server

    Negenborn, Rudy


    The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems.   This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those ...

  3. Model predictive control of a wind turbine modelled in Simpack

    International Nuclear Information System (INIS)

    Jassmann, U; Matzke, D; Reiter, M; Abel, D; Berroth, J; Schelenz, R; Jacobs, G


    Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine

  4. Model predictive control of a wind turbine modelled in Simpack (United States)

    Jassmann, U.; Berroth, J.; Matzke, D.; Schelenz, R.; Reiter, M.; Jacobs, G.; Abel, D.


    Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine to

  5. Poisson Mixture Regression Models for Heart Disease Prediction. (United States)

    Mufudza, Chipo; Erol, Hamza


    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.

  6. Prediction of stock markets by the evolutionary mix-game model (United States)

    Chen, Fang; Gou, Chengling; Guo, Xiaoqian; Gao, Jieping


    This paper presents the efforts of using the evolutionary mix-game model, which is a modified form of the agent-based mix-game model, to predict financial time series. Here, we have carried out three methods to improve the original mix-game model by adding the abilities of strategy evolution to agents, and then applying the new model referred to as the evolutionary mix-game model to forecast the Shanghai Stock Exchange Composite Index. The results show that these modifications can improve the accuracy of prediction greatly when proper parameters are chosen.

  7. Mathematical micro-model of a solid oxide fuel cell composite cathode

    International Nuclear Information System (INIS)

    Kenney, B.; Karan, K.


    In a solid oxide fuel cell (SOFC), the cathode processes account for a majority of the overall electrochemical losses. A composite cathode comprising a mixture of ion-conducting electrolyte and electron-conducting electro-catalyst can help minimize cathode losses provided microstructural parameters such as particle-size, composition, and porosity are optimized. The cost of composite cathode research can be greatly reduced by incorporating mathematical models into the development cycle. Incorporated with reliable experimental data, it is possible to conduct a parametric study using a model and the predicted results can be used as guides for component design. Many electrode models treat the cathode process simplistically by considering only the charge-transfer reaction for low overpotentials or the gas-diffusion at high overpotentials. Further, in these models an average property of the cathode internal microstructure is assumed. This paper will outline the development of a 1-dimensional SOFC composite cathode micro-model and the experimental procedures for obtaining accurate parameter estimates. The micro-model considers the details of the cathode microstructure such as porosity, composition and particle-size of the ionic and electronic phases, and their interrelationship to the charge-transfer reaction and mass transport processes. The micro-model will be validated against experimental data to determine its usefulness for performance prediction. (author)

  8. Effect of electrical conductivity on the polarization behaviour and pyroelectric, piezoelectric property prediction of 0-3 ferroelectric composites

    International Nuclear Information System (INIS)

    Wei Nian; Zhang Duanming; Yang Fengxia; Han Xiangyun; Zhong Zhicheng; Zheng Keyu


    We have investigated the effect of electrical conductivity of the constituents on the poling behaviour of the ceramic inclusions in 0-3 ferroelectric composites which comprise a dilute suspension of spherical particles uniformly distributed in the matrix material. A new model for the pyroelectric and piezoelectric properties in terms of the poling conditions (poling field and poling time) has been developed to include electrical conductivity. Simulated results show that conductivity plays an important role in the poling process. Properly increasing the conductivity of the matrix σ m can enhance the polarization in the ceramic inclusion of the composite P i , thereby making the poling of the composite more efficient. In contrast, higher conductivity of the ceramic inclusion σ i results in lower polarization P i , which is unfavourable to the poling of the composite. These results provide insights into the observed behaviour of 0-3 composites. The model predicts the pyroelectric and piezoelectric properties under different poling conditions, which agree well with the corresponding experimental data

  9. Modeling of robotic fish propelled by an ionic polymer-metal composite caudal fin (United States)

    Chen, Zheng; Shatara, Stephan; Tan, Xiaobo


    In this paper, a model is proposed for a biomimetic robotic fish propelled by an ionic polymer metal composite (IPMC) actuator with a rigid passive fin at the end. The model incorporates both IPMC actuation dynamics and the hydrodynamics, and predicts the steady-state speed of the robot under a periodic actuation voltage. Experimental results have shown that the proposed model can predict the fish motion for different tail dimensions. Since its parameters are expressed in terms of physical properties and geometric dimensions, the model is expected to be instrumental in optimal design of the robotic fish.

  10. Novel Predictive Model of the Debonding Strength for Masonry Members Retrofitted with FRP

    Directory of Open Access Journals (Sweden)

    Iman Mansouri


    Full Text Available Strengthening of masonry members using externally bonded (EB fiber-reinforced polymer (FRP composites has become a famous structural strengthening method over the past decade due to the popular advantages of FRP composites, including their high strength-to-weight ratio and excellent corrosion resistance. In this study, gene expression programming (GEP, as a novel tool, has been used to predict the debonding strength of retrofitted masonry members. The predictions of the new debonding resistance model, as well as several other models, are evaluated by comparing their estimates with experimental results of a large test database. The results indicate that the new model has the best efficiency among the models examined and represents an improvement to other models. The root mean square errors (RMSE of the best empirical Kashyap model in training and test data were, respectively, reduced by 51.7% and 41.3% using the GEP model in estimating debonding strength.

  11. Strategies to predict and improve eating quality of cooked beef using carcass and meat composition traits in Angus cattle. (United States)

    Mateescu, R G; Oltenacu, P A; Garmyn, A J; Mafi, G G; VanOverbeke, D L


    Product quality is a high priority for the beef industry because of its importance as a major driver of consumer demand for beef and the ability of the industry to improve it. A 2-prong approach based on implementation of a genetic program to improve eating quality and a system to communicate eating quality and increase the probability that consumers' eating quality expectations are met is outlined. The objectives of this study were 1) to identify the best carcass and meat composition traits to be used in a selection program to improve eating quality and 2) to develop a relatively small number of classes that reflect real and perceptible differences in eating quality that can be communicated to consumers and identify a subset of carcass and meat composition traits with the highest predictive accuracy across all eating quality classes. Carcass traits, meat composition, including Warner-Bratzler shear force (WBSF), intramuscular fat content (IMFC), trained sensory panel scores, and mineral composition traits of 1,666 Angus cattle were used in this study. Three eating quality indexes, EATQ1, EATQ2, and EATQ3, were generated by using different weights for the sensory traits (emphasis on tenderness, flavor, and juiciness, respectively). The best model for predicting eating quality explained 37%, 9%, and 19% of the variability of EATQ1, EATQ2, and EATQ3, and 2 traits, WBSF and IMFC, accounted for most of the variability explained by the best models. EATQ1 combines tenderness, juiciness, and flavor assessed by trained panels with 0.60, 0.15, and 0.25 weights, best describes North American consumers, and has a moderate heritability (0.18 ± 0.06). A selection index (I= -0.5[WBSF] + 0.3[IMFC]) based on phenotypic and genetic variances and covariances can be used to improve eating quality as a correlated trait. The 3 indexes (EATQ1, EATQ2, and EATQ3) were used to generate 3 equal (33.3%) low, medium, and high eating quality classes, and linear combinations of traits that

  12. Predictive integrated modelling for ITER scenarios

    International Nuclear Information System (INIS)

    Artaud, J.F.; Imbeaux, F.; Aniel, T.; Basiuk, V.; Eriksson, L.G.; Giruzzi, G.; Hoang, G.T.; Huysmans, G.; Joffrin, E.; Peysson, Y.; Schneider, M.; Thomas, P.


    The uncertainty on the prediction of ITER scenarios is evaluated. 2 transport models which have been extensively validated against the multi-machine database are used for the computation of the transport coefficients. The first model is GLF23, the second called Kiauto is a model in which the profile of dilution coefficient is a gyro Bohm-like analytical function, renormalized in order to get profiles consistent with a given global energy confinement scaling. The package of codes CRONOS is used, it gives access to the dynamics of the discharge and allows the study of interplay between heat transport, current diffusion and sources. The main motivation of this work is to study the influence of parameters such plasma current, heat, density, impurities and toroidal moment transport. We can draw the following conclusions: 1) the target Q = 10 can be obtained in ITER hybrid scenario at I p = 13 MA, using either the DS03 two terms scaling or the GLF23 model based on the same pedestal; 2) I p = 11.3 MA, Q = 10 can be reached only assuming a very peaked pressure profile and a low pedestal; 3) at fixed Greenwald fraction, Q increases with density peaking; 4) achieving a stationary q-profile with q > 1 requires a large non-inductive current fraction (80%) that could be provided by 20 to 40 MW of LHCD; and 5) owing to the high temperature the q-profile penetration is delayed and q = 1 is reached about 600 s in ITER hybrid scenario at I p = 13 MA, in the absence of active q-profile control. (A.C.)

  13. Prediction of endoplasmic reticulum resident proteins using fragmented amino acid composition and support vector machine

    Directory of Open Access Journals (Sweden)

    Ravindra Kumar


    Full Text Available Background The endoplasmic reticulum plays an important role in many cellular processes, which includes protein synthesis, folding and post-translational processing of newly synthesized proteins. It is also the site for quality control of misfolded proteins and entry point of extracellular proteins to the secretory pathway. Hence at any given point of time, endoplasmic reticulum contains two different cohorts of proteins, (i proteins involved in endoplasmic reticulum-specific function, which reside in the lumen of the endoplasmic reticulum, called as endoplasmic reticulum resident proteins and (ii proteins which are in process of moving to the extracellular space. Thus, endoplasmic reticulum resident proteins must somehow be distinguished from newly synthesized secretory proteins, which pass through the endoplasmic reticulum on their way out of the cell. Approximately only 50% of the proteins used in this study as training data had endoplasmic reticulum retention signal, which shows that these signals are not essentially present in all endoplasmic reticulum resident proteins. This also strongly indicates the role of additional factors in retention of endoplasmic reticulum-specific proteins inside the endoplasmic reticulum. Methods This is a support vector machine based method, where we had used different forms of protein features as inputs for support vector machine to develop the prediction models. During training leave-one-out approach of cross-validation was used. Maximum performance was obtained with a combination of amino acid compositions of different part of proteins. Results In this study, we have reported a novel support vector machine based method for predicting endoplasmic reticulum resident proteins, named as ERPred. During training we achieved a maximum accuracy of 81.42% with leave-one-out approach of cross-validation. When evaluated on independent dataset, ERPred did prediction with sensitivity of 72.31% and specificity of 83

  14. Predicting stone composition before treatment – can it really drive clinical decisions? (United States)

    Bres–Niewada, Ewa; Radziszewski, Piotr


    Introduction Determination of stone composition is considered to be crucial for the choice of an optimal treatment algorithm. It is especially important for uric acid stones, which can be dissolved by oral chemolysis and for renal stones smaller than 2 cm, which can be treated with extracorporeal shockwave lithotripsy (ESWL). Material and methods This short review identifies the latest papers on radiological assessment of stone composition and presents a comprehensive evaluation of current scientific findings. Results Stone chemical composition is difficult to predict using standard CT imaging, however, attenuation index measured in Hounsfield units (HU) is related to ESWL outcome. Stone density >1000 HU can be considered predictive for ESWL failure. It seems that stone composition is meaningless in determining the outcome of ureterolithotripsy and percutaneous surgery. Alternative imaging techniques such as Dual–Energy CT or analysis of shape, density and homogeneity of stones on plain X–rays are used as promising methods of predicting stone composition and ESWL outcome. Conclusions New imaging techniques facilitate the identification of uric acid stones and ESWL–resistant stones. Therefore, they may help in selecting the best therapeutic option. PMID:25667761

  15. High-Fidelity Microstructural Characterization and Performance Modeling of Aluminized Composite Propellant

    International Nuclear Information System (INIS)

    Kosiba, Graham D.; Wixom, Ryan R.; Oehlschlaeger, Matthew A.


    Image processing and stereological techniques were used to characterize the heterogeneity of composite propellant and inform a predictive burn rate model. Composite propellant samples made up of ammonium perchlorate (AP), hydroxyl-terminated polybutadiene (HTPB), and aluminum (Al) were faced with an ion mill and imaged with a scanning electron microscope (SEM) and x-ray tomography (micro-CT). Properties of both the bulk and individual components of the composite propellant were determined from a variety of image processing tools. An algebraic model, based on the improved Beckstead-Derr-Price model developed by Cohen and Strand, was used to predict the steady-state burning of the aluminized composite propellant. In the presented model the presence of aluminum particles within the propellant was introduced. The thermal effects of aluminum particles are accounted for at the solid-gas propellant surface interface and aluminum combustion is considered in the gas phase using a single global reaction. In conclusion, properties derived from image processing were used directly as model inputs, leading to a sample-specific predictive combustion model.

  16. Creep-rupture strength prediction of an epoxy composite under tension

    Czech Academy of Sciences Publication Activity Database

    Krastev, R.K.; Zachariev, G.; Hristova, J.; Minster, Jiří


    Roč. 13, č. 2 (2009), s. 207-214 ISSN 1385-2000 Institutional research plan: CEZ:AV0Z20710524 Keywords : materials testing * creep * strength prediction Subject RIV: JI - Composite Materials Impact factor: 1.051, year: 2009

  17. Prediction of process induced shape distortions and residual stresses in large fibre reinforced composite laminates

    DEFF Research Database (Denmark)

    Nielsen, Michael Wenani

    to their accuracy in predicting process induced strain and stress development in thick section laminates during curing, and more precisely regarding the evolution of the composite thermoset polymer matrix mechanical behaviour during the phase transitions experienced during curing. The different constitutive...

  18. In vivo ultrasound and biometric measurements predict the empty body chemical composition in Nellore cattle. (United States)

    Castilhos, A M; Francisco, C L; Branco, R H; Bonilha, S F M; Mercadante, M E Z; Meirelles, P R L; Pariz, C M; Jorge, A M


    Evaluation of the body chemical composition of beef cattle can only be measured postmortem and those data cannot be used in real production scenarios to adjust nutritional plans. The objective of this study was to develop multiple linear regression equations from in vivo measurements, such as ultrasound parameters [backfat thickness (uBFT, mm), rump fat thickness (uRF, mm), and ribeye area (uLMA, cm2)], shrunk body weight (SBW, kg), age (AG, d), hip height (HH, m), as well as from postmortem measurements (composition of the 9th to 11th rib section) to predict the empty body and carcass chemical composition for Nellore cattle. Thirty-three young bulls were used (339 ± 36.15 kg and 448 ± 17.78 d for initial weight and age, respectively). Empty body chemical composition (protein, fat, water, and ash in kg) was obtained by combining noncarcass and carcass components. Data were analyzed using the PROC REG procedure of SAS software. Mallows' Cp values were close to the ideal value of number of independent variables in the prediction equations plus one. Equations to predict chemical components of both empty body and carcass using in vivo measurements presented higher R2 values than those determined by postmortem measurements. Chemical composition of the empty body using in vivo measurements was predicted with R2 > 0.73. Equations to predict chemical composition of the carcass from in vivo measurements showed R2 lower (R2Chemical compounds from components of the empty body of Nellore cattle can be calculated by the following equations: protein (kg) = 47.92 + 0.18 × SBW - 1.46 × uRF - 30.72 × HH (R2 = 0.94, RMSPE = 1.79); fat (kg) = 11.33 + 0.16 × SBW + 2.09 × uRF - 0.06 × AG (R2 = 0.74, RMSPE = 4.18); water (kg) = - 34.00 + 0.55 × SBW + 0.10 × AG - 2.34 × uRF (R2 = 0.96, RMSPE = 5.47). In conclusion, the coefficients of determination (for determining the chemical composition of the empty body) of the equations derived from in vivo measures were higher than those

  19. A modeling approach for compounds affecting body composition. (United States)

    Gennemark, Peter; Jansson-Löfmark, Rasmus; Hyberg, Gina; Wigstrand, Maria; Kakol-Palm, Dorota; Håkansson, Pernilla; Hovdal, Daniel; Brodin, Peter; Fritsch-Fredin, Maria; Antonsson, Madeleine; Ploj, Karolina; Gabrielsson, Johan


    Body composition and body mass are pivotal clinical endpoints in studies of welfare diseases. We present a combined effort of established and new mathematical models based on rigorous monitoring of energy intake (EI) and body mass in mice. Specifically, we parameterize a mechanistic turnover model based on the law of energy conservation coupled to a drug mechanism model. Key model variables are fat-free mass (FFM) and fat mass (FM), governed by EI and energy expenditure (EE). An empirical Forbes curve relating FFM to FM was derived experimentally for female C57BL/6 mice. The Forbes curve differs from a previously reported curve for male C57BL/6 mice, and we thoroughly analyse how the choice of Forbes curve impacts model predictions. The drug mechanism function acts on EI or EE, or both. Drug mechanism parameters (two to three parameters) and system parameters (up to six free parameters) could be estimated with good precision (coefficients of variation typically mass and FM changes at different drug provocations using a similar model for man. Surprisingly, model simulations indicate that an increase in EI (e.g. 10 %) was more efficient than an equal lowering of EI. Also, the relative change in body mass and FM is greater in man than in mouse at the same relative change in either EI or EE. We acknowledge that this assumes the same drug mechanism impact across the two species. A set of recommendations regarding the Forbes curve, vehicle control groups, dual action on EI and loss, and translational aspects are discussed. This quantitative approach significantly improves data interpretation, disease system understanding, safety assessment and translation across species.

  20. Maximum Likelihood Method for Predicting Environmental Conditions from Assemblage Composition: The R Package bio.infer

    Directory of Open Access Journals (Sweden)

    Lester L. Yuan


    Full Text Available This paper provides a brief introduction to the R package bio.infer, a set of scripts that facilitates the use of maximum likelihood (ML methods for predicting environmental conditions from assemblage composition. Environmental conditions can often be inferred from only biological data, and these inferences are useful when other sources of data are unavailable. ML prediction methods are statistically rigorous and applicable to a broader set of problems than more commonly used weighted averaging techniques. However, ML methods require a substantially greater investment of time to program algorithms and to perform computations. This package is designed to reduce the effort required to apply ML prediction methods.

  1. Model for the resistive critical current transition in composite superconductors

    International Nuclear Information System (INIS)

    Warnes, W.H.


    Much of the research investigating technological type-II superconducting composites relies on the measurement of the resistive critical current transition. We have developed a model for the resistive transition which improves on older models by allowing for the very different nature of monofilamentary and multifilamentary composite structures. The monofilamentary model allows for axial current flow around critical current weak links in the superconducting filament. The multifilamentary model incorporates an additional radial current transfer between neighboring filaments. The development of both models is presented. It is shown that the models are useful for extracting more information from the experimental data than was formerly possible. Specific information obtainable from the experimental voltage-current characteristic includes the distribution of critical currents in the composite, the average critical current of the distribution, the range of critical currents in the composite, the field and temperature dependence of the distribution, and the fraction of the composite dissipating energy in flux flow at any current. This additional information about the distribution of critical currents may be helpful in leading toward a better understanding of flux pinning in technological superconductors. Comparison of the models with several experiments is given and shown to be in reasonable agreement. Implications of the models for the measurement of critical currents in technological composites is presented and discussed with reference to basic flux pinning studies in such composites

  2. Prediction on flexural strength of encased composite beam with cold-formed steel section (United States)

    Khadavi, Tahir, M. M.


    A flexural strength of composite beam designed as boxed shaped section comprised of lipped C-channel of cold-formed steel (CFS) facing each other with reinforcement bars is proposed in this paper. The boxed shaped is kept restrained in position by a profiled metal decking installed on top of the beam to form a slab system. This profiled decking slab is cast by using self-compacting concrete where the concrete is in compression when load is applied to the beam. Reinforcement bars are used as shear connector between slab and CFS as beam. A numerical analysis method proposed by EC4 is used to predict the flexural strength of the proposed composite beam. It was assumed that elasto-plastic behaviour is developed in the cross -sectional of the proposed beam. The calculated predicted flexural strength of the proposed beam shows reasonable flexural strength for cold-formed composite beam.

  3. Application of bioelectrical impedance analysis in prediction of light kid carcass and muscle chemical composition. (United States)

    Silva, S R; Afonso, J; Monteiro, A; Morais, R; Cabo, A; Batista, A C; Guedes, C M; Teixeira, A


    Carcass data were collected from 24 kids (average live weight of 12.5±5.5 kg; range 4.5 to 22.4 kg) of Jarmelista Portuguese native breed, to evaluate bioelectrical impedance analysis (BIA) as a technique for prediction of light kid carcass and muscle chemical composition. Resistance (Rs, Ω) and reactance (Xc, Ω), were measured in the cold carcasses with a single frequency bioelectrical impedance analyzer and, together with impedance (Z, Ω), two electrical volume measurements (VolA and VolB, cm2/Ω), carcass cold weight (CCW), carcass compactness and several carcass linear measurements were fitted as independent variables to predict carcass composition by stepwise regression analysis. The amount of variation explained by VolA and VolB only reached a significant level (Pcarcass fat weight (0.814⩽R 2⩽0.862; Pcarcass fat weight (combined with carcass length, CL; R 2=0.943; Pcarcass composition.

  4. Modeling and additive manufacturing of bio-inspired composites with tunable fracture mechanical properties. (United States)

    Dimas, Leon S; Buehler, Markus J


    Flaws, imperfections and cracks are ubiquitous in material systems and are commonly the catalysts of catastrophic material failure. As stresses and strains tend to concentrate around cracks and imperfections, structures tend to fail far before large regions of material have ever been subjected to significant loading. Therefore, a major challenge in material design is to engineer systems that perform on par with pristine structures despite the presence of imperfections. In this work we integrate knowledge of biological systems with computational modeling and state of the art additive manufacturing to synthesize advanced composites with tunable fracture mechanical properties. Supported by extensive mesoscale computer simulations, we demonstrate the design and manufacturing of composites that exhibit deformation mechanisms characteristic of pristine systems, featuring flaw-tolerant properties. We analyze the results by directly comparing strain fields for the synthesized composites, obtained through digital image correlation (DIC), and the computationally tested composites. Moreover, we plot Ashby diagrams for the range of simulated and experimental composites. Our findings show good agreement between simulation and experiment, confirming that the proposed mechanisms have a significant potential for vastly improving the fracture response of composite materials. We elucidate the role of stiffness ratio variations of composite constituents as an important feature in determining the composite properties. Moreover, our work validates the predictive ability of our models, presenting them as useful tools for guiding further material design. This work enables the tailored design and manufacturing of composites assembled from inferior building blocks, that obtain optimal combinations of stiffness and toughness.

  5. Design prediction for long term stress rupture service of composite pressure vessels (United States)

    Robinson, Ernest Y.


    Extensive stress rupture studies on glass composites and Kevlar composites were conducted by the Lawrence Radiation Laboratory beginning in the late 1960's and extending to about 8 years in some cases. Some of the data from these studies published over the years were incomplete or were tainted by spurious failures, such as grip slippage. Updated data sets were defined for both fiberglass and Kevlar composite stand test specimens. These updated data are analyzed in this report by a convenient form of the bivariate Weibull distribution, to establish a consistent set of design prediction charts that may be used as a conservative basis for predicting the stress rupture life of composite pressure vessels. The updated glass composite data exhibit an invariant Weibull modulus with lifetime. The data are analyzed in terms of homologous service load (referenced to the observed median strength). The equations relating life, homologous load, and probability are given, and corresponding design prediction charts are presented. A similar approach is taken for Kevlar composites, where the updated stand data do show a turndown tendency at long life accompanied by a corresponding change (increase) of the Weibull modulus. The turndown characteristic is not present in stress rupture test data of Kevlar pressure vessels. A modification of the stress rupture equations is presented to incorporate a latent, but limited, strength drop, and design prediction charts are presented that incorporate such behavior. The methods presented utilize Cartesian plots of the probability distributions (which are a more natural display for the design engineer), based on median normalized data that are independent of statistical parameters and are readily defined for any set of test data.

  6. 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...

  7. Analytical method for predicting plastic flow in notched fiber composite materials

    International Nuclear Information System (INIS)

    Flynn, P.L.; Ebert, L.J.


    An analytical system was developed for prediction of the onset and progress of plastic flow of oriented fiber composite materials in which both externally applied complex stress states and stress raisers were present. The predictive system was a unique combination of two numerical systems, the ''SAAS II'' finite element analysis system and a micromechanics finite element program. The SAAS II system was used to generate the three-dimensional stress distributions, which were used as the input into the finite element micromechanics program. Appropriate yielding criteria were then applied to this latter program. The accuracy of the analytical system was demonstrated by the agreement between the analytically predicted and the experimentally measured flow values of externally notched tungsten wire reinforced copper oriented fiber composites, in which the fiber fraction was 50 vol pct

  8. Micromechanical modeling of strength and damage of fiber reinforced composites

    Energy Technology Data Exchange (ETDEWEB)

    Mishnaevsky, L. Jr.; Broendsted, P.


    The report for the first year of the EU UpWind project includes three parts: overview of concepts and methods of modelling of mechanical behavior, deformation and damage of unidirectional fiber reinforced composites, development of computational tools for the automatic generation of 3D micromechanical models of fiber reinforced composites, and micromechanical modelling of damage in FRC, and phenomenological analysis of the effect of frequency of cyclic loading on the lifetime and damage evolution in materials. (au)

  9. Structural Acoustic Physics Based Modeling of Curved Composite Shells (United States)


    NUWC-NPT Technical Report 12,236 19 September 2017 Structural Acoustic Physics -Based Modeling of Curved Composite Shells Rachel E. Hesse...SUBTITLE Structural Acoustic Physics -Based Modeling of Curved Composite Shells 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM was to use physics -based modeling (PBM) to investigate wave propagations through curved shells that are subjected to acoustic excitation. An

  10. Modeling Bistable Composite Laminates for Piezoelectric Morphing Structures


    Darryl V. Murray; Oliver J. Myers


    A sequential modeling effort for bistable composite laminates for piezoelectric morphing structures is presented. Thin unsymmetric carbon fiber composite laminates are examined for use of morphing structures using piezoelectric actuation. When cooling from the elevated cure temperature to room temperature, these unsymmetric composite laminates will deform. These postcure room temperature deformation shapes can be used as morphing structures. Applying a force to these deformed laminates will c...

  11. Thermodynamic modeling to analyse composition of carbonaceous ...

    Indian Academy of Sciences (India)

    Equilibrium thermodynamic analysis has been applied to the low-pressure MOCVD process using manganese acetylacetonate as the precursor. ``CVD phase stability diagrams” have been constructed separately for the processes carried out in argon and oxygen ambient, depicting the compositions of the resulting films as ...

  12. DC modeling of composite MOS transistors

    NARCIS (Netherlands)

    de Haan, P.; de Haan, P.E.; Klumperink, Eric A.M.; van Leeuwen, M.G.; Wallinga, Hans


    Mixed-signal circuit design on sea-of-gates arrays requires the use of composite MOSTs, combinations of in-series and in-parallel connected unit MOSTs. To avoid an increase in circuit simulation complexity these are in general replaced by artificial single MOSTs. The analysis in this paper shows

  13. Partially composite two-Higgs doublet model

    Indian Academy of Sciences (India)

    Abstract. In the extra dimensional scenarios with gauge fields in the bulk, the Kaluza–. Klein (KK) gauge bosons can induce Nambu–Jona–Lasinio (NJL) type attractive four- fermion interactions, which can break electroweak symmetry dynamically with accompa- nying composite Higgs fields. We consider a possibility that ...

  14. Simplified Predictive Models for CO2 Sequestration Performance Assessment (United States)

    Mishra, Srikanta; RaviGanesh, Priya; Schuetter, Jared; Mooney, Douglas; He, Jincong; Durlofsky, Louis


    We present results from an ongoing research project that seeks to develop and validate a portfolio of simplified modeling approaches that will enable rapid feasibility and risk assessment for CO2 sequestration in deep saline formation. The overall research goal is to provide tools for predicting: (a) injection well and formation pressure buildup, and (b) lateral and vertical CO2 plume migration. Simplified modeling approaches that are being developed in this research fall under three categories: (1) Simplified physics-based modeling (SPM), where only the most relevant physical processes are modeled, (2) Statistical-learning based modeling (SLM), where the simulator is replaced with a "response surface", and (3) Reduced-order method based modeling (RMM), where mathematical approximations reduce the computational burden. The system of interest is a single vertical well injecting supercritical CO2 into a 2-D layered reservoir-caprock system with variable layer permeabilities. In the first category (SPM), we use a set of well-designed full-physics compositional simulations to understand key processes and parameters affecting pressure propagation and buoyant plume migration. Based on these simulations, we have developed correlations for dimensionless injectivity as a function of the slope of fractional-flow curve, variance of layer permeability values, and the nature of vertical permeability arrangement. The same variables, along with a modified gravity number, can be used to develop a correlation for the total storage efficiency within the CO2 plume footprint. In the second category (SLM), we develop statistical "proxy models" using the simulation domain described previously with two different approaches: (a) classical Box-Behnken experimental design with a quadratic response surface fit, and (b) maximin Latin Hypercube sampling (LHS) based design with a Kriging metamodel fit using a quadratic trend and Gaussian correlation structure. For roughly the same number of

  15. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu


    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.

  16. Property-Composition-Temperature Modeling of Waste Glass Melt Data Subject to a Randomization Restriction

    International Nuclear Information System (INIS)

    Piepel, Gregory F.; Heredia-Langner, Alejandro; Cooley, Scott K.


    Properties such as viscosity and electrical conductivity of glass melts are functions of melt temperature as well as glass composition. When measuring such a property for several glasses, the property is typically measured at several temperatures for one glass, then at several temperatures for the next glass, and so on. This data-collection process involves a restriction on randomization, which is referred to as split-plot experiment. The split-plot data structure must be accounted for in developing property-composition-temperature models and the corresponding uncertainty equations for model predictions. Instead of ordinary least squares (OLS) regression methods, generalized least squares (GLS) regression methods using restricted maximum likelihood (REML) estimation must be used. This article describes the methodology for developing property-composition-temperature models and corresponding prediction uncertainty equations using the GLS/REML regression approach. Viscosity data collected on 197 simulated nuclear waste glasses are used to illustrate the GLS/REML methods for developing a viscosity-composition-temperature model and corresponding equations for model prediction uncertainties. The correct results using GLS/REML regression are compared to the incorrect results obtained using OLS regression

  17. Properties predictive modeling through the concept of a hybrid interphase existing between phases in contact (United States)

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


    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.

  18. Finite element modelling and analysis of composites toecaps

    International Nuclear Information System (INIS)

    Yang, C C; Duhovic, M; Lin, R J T; Bhattacharyya, D


    Composite toe-caps have attracted considerable attention due to their advantageous properties over traditional metallic toe-caps. However, the anisotropic properties of composite materials also make the toe-cap performance more complex to analyse. This project aims at developing a Finite Element (FE) model for composite toe-caps with the aid of compression testing data. The geometry of the toe-cap was first scanned and imported into an FEA software package to create a workable FE model. The method was then validated by comparing the FE model with experimental results of steel toe-caps. Manufacturing, modelling and testing of custom-made composite toe-cap samples were then carried out. Modelling outputs of composite toe-caps were compared with compression test data for validation. The stress distributions and deformations of the toe-caps were also analysed. Modelling of the steel and composite toe-caps was realized using LS-DYNA Solver and PrePost (registered) . All FE analyses were modelled with reference to European Standards. The developed FE models can in the future be used to model toe-caps with various materials to determine the effects of fibre orientation relating to structural strength, and to achieve structural optimisation.

  19. Short cellulosic fiber/starch acetate composites — micromechanical modeling of Young’s modulus

    DEFF Research Database (Denmark)

    Madsen, Bo; Joffe, Roberts; Peltola, Heidi


    This study is presented to predict the Young’s modulus of injection-molded short cellulosic fiber/plasticized starch acetate composites with variable fiber and plasticizer content. A modified rule of mixtures model is applied where the effect of porosity is included, and where the fiber weight...... (density and Young’s modulus). The measured Young’s modulus of the composites varies in the range 1.1—8.3 GPa, and this is well predicted by the model calculations. A property diagram is presented to be used for the tailor-making of composites with Young’s modulus in the range 0.2—10 GPa....

  20. Foundation Settlement Prediction Based on a Novel NGM Model

    Directory of Open Access Journals (Sweden)

    Peng-Yu Chen


    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.

  1. From Process Modeling to Elastic Property Prediction for Long-Fiber Injection-Molded Thermoplastics

    International Nuclear Information System (INIS)

    Nguyen, Ba Nghiep; Kunc, Vlastimil; Frame, Barbara J.; Phelps, Jay; Tucker III, Charles L.; Bapanapalli, Satish K.; Holbery, James D.; Smith, Mark T.


    This paper presents an experimental-modeling approach to predict the elastic properties of long-fiber injection-molded thermoplastics (LFTs). The approach accounts for fiber length and orientation distributions in LFTs. LFT samples were injection-molded for the study, and fiber length and orientation distributions were measured at different locations for use in the computation of the composite properties. The current fiber orientation model was assessed to determine its capability to predict fiber orientation in LFTs. Predicted fiber orientations for the studied LFT samples were also used in the calculation of the elastic properties of these samples, and the predicted overall moduli were then compared with the experimental results. The elastic property prediction was based on the Eshelby-Mori-Tanaka method combined with the orientation averaging technique. The predictions reasonably agree with the experimental LFT data

  2. A Plastic Damage Mechanics Model for Engineered Cementitious Composites

    DEFF Research Database (Denmark)

    Dick-Nielsen, Lars; Stang, Henrik; Poulsen, Peter Noe


    This paper discusses the establishment of a plasticity-based damage mechanics model for Engineered Cementitious Composites (ECC). The present model differs from existing models by combining a matrix and fiber description in order to describe the behavior of the ECC material. The model provides...

  3. Predicting growth of the healthy infant using a genome scale metabolic model

    DEFF Research Database (Denmark)

    Nilsson, Avlant; Mardinoglu, Adil; Nielsen, Jens


    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...

  4. Theoretical Development of an Orthotropic Elasto-Plastic Generalized Composite Material Model (United States)

    Goldberg, Robert; Carney, Kelly; DuBois, Paul; Hoffarth, Canio; Harrington, Joseph; Rajan, Subramaniam; Blankenhorn, Gunther


    The need for accurate material models to simulate the deformation, damage and failure of polymer matrix composites is becoming critical as these materials are gaining increased usage in the aerospace and automotive industries. While there are several composite material models currently available within LSDYNA (Livermore Software Technology Corporation), there are several features that have been identified that could improve the predictive capability of a composite model. To address these needs, a combined plasticity and damage model suitable for use with both solid and shell elements is being developed and is being implemented into LS-DYNA as MAT_213. A key feature of the improved material model is the use of tabulated stress-strain data in a variety of coordinate directions to fully define the stress-strain response of the material. To date, the model development efforts have focused on creating the plasticity portion of the model. The Tsai-Wu composite failure model has been generalized and extended to a strain-hardening based orthotropic yield function with a nonassociative flow rule. The coefficients of the yield function, and the stresses to be used in both the yield function and the flow rule, are computed based on the input stress-strain curves using the effective plastic strain as the tracking variable. The coefficients in the flow rule are computed based on the obtained stress-strain data. The developed material model is suitable for implementation within LS-DYNA for use in analyzing the nonlinear response of polymer composites.

  5. Predictions and Verification of an Isotope Marine Boundary Layer Model (United States)

    Feng, X.; Posmentier, E. S.; Sonder, L. J.; Fan, N.


    A one-dimensional (1D), steady state isotope marine boundary layer (IMBL) model is constructed. The model includes meteorologically important features absent in Craig and Gordon type models, namely height-dependent diffusion/mixing and convergence of subsiding external air. Kinetic isotopic fractionation results from this height-dependent diffusion which starts as pure molecular diffusion at the air-water interface and increases linearly with height due to turbulent mixing. The convergence permits dry, isotopically depleted air subsiding adjacent to the model column to mix into ambient air. In δD-δ18O space, the model results fill a quadrilateral, of which three sides represent 1) vapor in equilibrium with various sea surface temperatures (SSTs) (high d18O boundary of quadrilateral); 2) mixture of vapor in equilibrium with seawater and vapor in the subsiding air (lower boundary depleted in both D and 18O); and 3) vapor that has experienced the maximum possible kinetic fractionation (high δD upper boundary). The results can be plotted in d-excess vs. δ18O space, indicating that these processes all cause variations in d-excess of MBL vapor. In particular, due to relatively high d-excess in the descending air, mixing of this air into the MBL causes an increase in d-excess, even without kinetic isotope fractionation. The model is tested by comparison with seven datasets of marine vapor isotopic ratios, with excellent correspondence; >95% of observational data fall within the quadrilateral area predicted by the model. The distribution of observations also highlights the significant influence of vapor from the nearby converging descending air on isotopic variations in the MBL. At least three factors may explain the affect the isotopic composition of precipitation. The model can be applied to modern as well as paleo- climate conditions.

  6. Combinatory Models for Predicting the Effective Thermal Conductivity of Frozen and Unfrozen Food Materials


    K. S. Reddy; P Karthikeyan


    A model to predict the effective thermal conductivity of heterogeneous materials is proposed based on unit cell approach. The model is combined with four fundamental effective thermal conductivity models (Parallel, Series, Maxwell-Eucken-I, and Maxwell-Eucken-II) to evolve a unifying equation for the estimation of effective thermal conductivity of porous and nonporous food materials. The effect of volume fraction (ν) on the structure composition factor (ψ) of the food materials is studied. Th...

  7. Composite Higgs Models and the tt-bar H Channel

    International Nuclear Information System (INIS)

    Carmona, A.; Chala, M.; Santiago, J.


    Despite its suppressed couplings to Standard Model particles, a composite Higgs with mass m H = 125 GeV and a moderate degree of compositeness can be consistent with current Higgs searches, including a sizable enhancement in the H → γγ channel. Heavy resonances common to many composite Higgs models can mediate new Higgs production mechanisms. In particular, the tt-bar H channel can be accessible at the LHC in these models through the exchange of colored vector and fermion resonances. In this case, the tt-bar H channel is not a direct measure of the top Yukawa coupling. (authors)

  8. Nonconvex model predictive control for commercial refrigeration (United States)

    Gybel Hovgaard, Tobias; Boyd, Stephen; Larsen, Lars F. S.; Bagterp Jørgensen, John


    We consider the control of a commercial multi-zone refrigeration system, consisting of several cooling units that share a common compressor, and is used to cool multiple areas or rooms. In each time period we choose cooling capacity to each unit and a common evaporation temperature. The goal is to minimise the total energy cost, using real-time electricity prices, while obeying temperature constraints on the zones. We propose a variation on model predictive control to achieve this goal. When the right variables are used, the dynamics of the system are linear, and the constraints are convex. The cost function, however, is nonconvex due to the temperature dependence of thermodynamic efficiency. To handle this nonconvexity we propose a sequential convex optimisation method, which typically converges in fewer than 5 or so iterations. We employ a fast convex quadratic programming solver to carry out the iterations, which is more than fast enough to run in real time. We demonstrate our method on a realistic model, with a full year simulation and 15-minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost savings, on the order of 30%, compared to a standard thermostat-based control system. Perhaps more important, we see that the method exhibits sophisticated response to real-time variations in electricity prices. This demand response is critical to help balance real-time uncertainties in generation capacity associated with large penetration of intermittent renewable energy sources in a future smart grid.

  9. Predictive Modelling of Heavy Metals in Urban Lakes


    Lindström, Martin


    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 ...

  10. Incorporation of lysosomal sequestration in the mechanistic model for prediction of tissue distribution of basic drugs. (United States)

    Assmus, Frauke; Houston, J Brian; Galetin, Aleksandra


    The prediction of tissue-to-plasma water partition coefficients (Kpu) from in vitro and in silico data using the tissue-composition based model (Rodgers & Rowland, J Pharm Sci. 2005, 94(6):1237-48.) is well established. However, distribution of basic drugs, in particular into lysosome-rich lung tissue, tends to be under-predicted by this approach. The aim of this study was to develop an extended mechanistic model for the prediction of Kpu which accounts for lysosomal sequestration and the contribution of different cell types in the tissue of interest. The extended model is based on compound-specific physicochemical properties and tissue composition data to describe drug ionization, distribution into tissue water and drug binding to neutral lipids, neutral phospholipids and acidic phospholipids in tissues, including lysosomes. Physiological data on the types of cells contributing to lung, kidney and liver, their lysosomal content and lysosomal pH were collated from the literature. The predictive power of the extended mechanistic model was evaluated using a dataset of 28 basic drugs (pK a ≥7.8, 17 β-blockers, 11 structurally diverse drugs) for which experimentally determined Kpu data in rat tissue have been reported. Accounting for the lysosomal sequestration in the extended mechanistic model improved the accuracy of Kpu predictions in lung compared to the original Rodgers model (56% drugs within 2-fold or 88% within 3-fold of observed values). Reduction in the extent of Kpu under-prediction was also evident in liver and kidney. However, consideration of lysosomal sequestration increased the occurrence of over-predictions, yielding overall comparable model performances for kidney and liver, with 68% and 54% of Kpu values within 2-fold error, respectively. High lysosomal concentration ratios relative to cytosol (>1000-fold) were predicted for the drugs investigated; the extent differed depending on the lysosomal pH and concentration of acidic phospholipids among

  11. Magnetic monopoles in a model of a composite photon

    International Nuclear Information System (INIS)

    Iwazaki, Aiichi.


    We show that there are monopole solutions in a composite model where the photon is regarded as a composite of elementary constituents. These monopoles have magnetic charges of the Dirac unit but are essencially different from 't Hooft-Polyakov monopoles since they are boundstates of the constituents. The stability of the monopoles is guaranteed by the conservation of the magnetic charges. (author)

  12. A compositional modelling framework for exploring MPSoC systems

    DEFF Research Database (Denmark)

    Tranberg-Hansen, Anders Sejer; Madsen, Jan


    This paper presents a novel compositional framework for system level performance estimation and exploration of Multi-Processor System On Chip (MPSoC) based systems. The main contributions are the definition of a compositional model which allows quantitative performance estimation to be carried ou...

  13. Heuristic Model Of The Composite Quality Index Of Environmental Assessment (United States)

    Khabarov, A. N.; Knyaginin, A. A.; Bondarenko, D. V.; Shepet, I. P.; Korolkova, L. N.


    The goal of the paper is to present the heuristic model of the composite environmental quality index based on the integrated application of the elements of utility theory, multidimensional scaling, expert evaluation and decision-making. The composite index is synthesized in linear-quadratic form, it provides higher adequacy of the results of the assessment preferences of experts and decision-makers.

  14. Seasonal predictability of Kiremt rainfall in coupled general circulation models (United States)

    Gleixner, Stephanie; Keenlyside, Noel S.; Demissie, Teferi D.; Counillon, François; Wang, Yiguo; Viste, Ellen


    The Ethiopian economy and population is strongly dependent on rainfall. Operational seasonal predictions for the main rainy season (Kiremt, June-September) are based on statistical approaches with Pacific sea surface temperatures (SST) as the main predictor. Here we analyse dynamical predictions from 11 coupled general circulation models for the Kiremt seasons from 1985-2005 with the forecasts starting from the beginning of May. We find skillful predictions from three of the 11 models, but no model beats a simple linear prediction model based on the predicted Niño3.4 indices. The skill of the individual models for dynamically predicting Kiremt rainfall depends on the strength of the teleconnection between Kiremt rainfall and concurrent Pacific SST in the models. Models that do not simulate this teleconnection fail to capture the observed relationship between Kiremt rainfall and the large-scale Walker circulation.


    Directory of Open Access Journals (Sweden)

    Andrey Borisovich Nikolaev


    Full Text Available The article considers the issues of traffic management using intelligent system “Car-Road” (IVHS, which consist of interacting intelligent vehicles (IV and intelligent roadside controllers. Vehicles are organized in convoy with small distances between them. All vehicles are assumed to be fully automated (throttle control, braking, steering. Proposed approaches for determining speed limits for traffic cars on the motorway using a model predictive control (MPC. The article proposes an approach to dynamic speed limit to minimize the downtime of vehicles in traffic.

  16. On the Effect of Unit-Cell Parameters in Predicting the Elastic Response of Wood-Plastic Composites

    Directory of Open Access Journals (Sweden)

    Fatemeh Alavi


    Full Text Available This paper presents a study on the effect of unit-cell geometrical parameters in predicting elastic properties of a typical wood plastic composite (WPC. The ultimate goal was obtaining the optimal values of representative volume element (RVE parameters to accurately predict the mechanical behavior of the WPC. For each unit cell, defined by a given combination of the above geometrical parameters, finite element simulation in ABAQUS was carried out, and the corresponding stress-strain curve was obtained. A uniaxial test according to ASTM D638-02a type V was performed on the composite specimen. Modulus of elasticity was determined using hyperbolic tangent function, and the results were compared to the sets of finite element analyses. Main effects of RVE parameters and their interactions were demonstrated and discussed, specially regarding the inclusion of two adjacent wood particles within one unit cell of the material. Regression analysis was performed to mathematically model the RVE parameter effects and their interactions over the modulus of elasticity response. The model was finally employed in an optimization analysis to arrive at an optimal set of RVE parameters that minimizes the difference between the predicted and experimental moduli of elasticity.

  17. Microbial Composition Predicts Genital Tract Inflammation and Persistent Bacterial Vaginosis in South African Adolescent Females. (United States)

    Lennard, Katie; Dabee, Smritee; Barnabas, Shaun L; Havyarimana, Enock; Blakney, Anna; Jaumdally, Shameem Z; Botha, Gerrit; Mkhize, Nonhlanhla N; Bekker, Linda-Gail; Lewis, David A; Gray, Glenda; Mulder, Nicola; Passmore, Jo-Ann S; Jaspan, Heather B


    Young African females are at an increased risk of HIV acquisition, and genital inflammation or the vaginal microbiome may contribute to this risk. We studied these factors in 168 HIV-negative South African adolescent females aged 16 to 22 years. Unsupervised clustering of 16S rRNA gene sequences revealed three clusters (subtypes), one of which was strongly associated with genital inflammation. In a multivariate model, the microbiome compositional subtype and hormonal contraception were significantly associated with genital inflammation. We identified 40 taxa significantly associated with inflammation, including those reported previously ( Prevotella , Sneathia , Aerococcus , Fusobacterium , and Gemella ) as well as several novel taxa (including increased frequencies of bacterial vaginosis-associated bacterium 1 [BVAB1], BVAB2, BVAB3, Prevotella amnii , Prevotella pallens , Parvimonas micra , Megasphaera , Gardnerella vaginalis , and Atopobium vaginae and decreased frequencies of Lactobacillus reuteri , Lactobacillus crispatus , Lactobacillus jensenii , and Lactobacillus iners ). Women with inflammation-associated microbiomes had significantly higher body mass indices and lower levels of endogenous estradiol and luteinizing hormone. Community functional profiling revealed three distinct vaginal microbiome subtypes, one of which was characterized by extreme genital inflammation and persistent bacterial vaginosis (BV); this subtype could be predicted with high specificity and sensitivity based on the Nugent score (≥9) or BVAB1 abundance. We propose that women with this BVAB1-dominated subtype may have chronic genital inflammation due to persistent BV, which may place them at a particularly high risk for HIV infection. Copyright © 2017 American Society for Microbiology.

  18. The association of LUR modeled PM2.5 elemental composition with personal exposure

    International Nuclear Information System (INIS)

    Montagne, Denise; Hoek, Gerard; Nieuwenhuijsen, Mark; Lanki, Timo; Pennanen, Arto; Portella, Meritxell; Meliefste, Kees; Wang, Meng; Eeftens, Marloes; Yli-Tuomi, Tarja; Cirach, Marta; Brunekreef, Bert


    Background and aims: Land use regression (LUR) models predict spatial variation of ambient concentrations, but little is known about the validity in predicting personal exposures. In this study, the association of LUR modeled concentrations of PM 2.5 components with measured personal concentrations was determined. The elements of interest were copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V) and zinc (Zn). Methods: In Helsinki (Finland), Utrecht (the Netherlands) and Barcelona (Spain) five participants from urban background, five from suburban background and five from busy street sites were selected in each city (15 participants per city). Outdoor, indoor and personal 96-hour PM 2.5 samples were collected by the participants over periods of two weeks in three different seasons (winter, summer and spring/autumn) and the overall average was calculated. Elemental composition was measured by ED-XRF spectrometry. The LUR models for the average ambient concentrations of each element were developed by the ESCAPE project. Results: LUR models predicted the within-city variation of average outdoor Cu and Fe concentrations moderately well (range in R 2 27–67% for Cu and 24–54% for Fe). The outdoor concentrations of the other elements were not well predicted. The LUR modeled concentration only significantly correlated with measured personal Fe exposure in Utrecht and Ni and V in Helsinki. The LUR model predictions did not correlate with measured personal Cu exposure. After excluding observations with an indoor/outdoor ratio of > 1.5, modeled Cu outdoor concentrations correlated with indoor concentrations in Helsinki and Utrecht and personal concentrations in Utrecht. The LUR model predictions were associated with measured outdoor, indoor and personal concentrations for all elements when the data for the three cities was pooled. Conclusions: Within-city modeled variation of elemental composition of PM 2.5 did not predict measured

  19. MJO prediction skill of the subseasonal-to-seasonal (S2S) prediction models (United States)

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


    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.

  20. Prediction of composites behavior undergoing an ATP process through data-mining (United States)

    Martin, Clara Argerich; Collado, Angel Leon; Pinillo, Rubén Ibañez; Barasinski, Anaïs; Abisset-Chavanne, Emmanuelle; Chinesta, Francisco


    The need to characterize composite surfaces for distinct mechanical or physical processes leads to different manners of evaluate the state of the surface. During many manufacturing processes deformation occurs, thus hindering composite classification for fabrication processes. In this work we focus on the challenge of a priori identifying the surfaces' behavior in order to optimize manufacturing. We will propose and validate the curvature of the surface as a reliable parameter and we will develop a tool that allows the prediction of the surface behavior.

  1. 3-D cohesive finite element model for application in structural analysis of heavy duty composite pavements

    DEFF Research Database (Denmark)

    Skar, Asmus; Poulsen, Peter Noe


    The problem of stiffness degradation in composite pavement systems from localised fracture damage in the quasibrittle cement bound granular mixture are today taken into account only by empirical formulas. These formulas deals with a limited number of materials in a restricted range of design...... this paper presents a numerical analysis of the fracture behaviour of cement bound granular mixtures in composite concrete block pavement systems applying a cohesive model. The functionality of the proposed model is compared to experimental investigations of beam bending tests. The pavement is modelled......, it can be shown that adequately good prediction of the structural response of composite pavements is obtained for monotonic loading without significant computational cost, making the model applicable for engineering design purpose. It is envisaged that the methodology implemented in this study can...

  2. A new aeroelastic model for composite rotor blades with straight and swept tips (United States)

    Yuan, Kuo-An; Friedmann, Peretz P.; Venkatesan, Comandur


    An analytical model for predicting the aeroelastic behavior of composite rotor blades with straight and swept tips is presented. The blade is modeled by beam type finite elements along the elastic axis. A single finite element is used to model the swept tip. The nonlinear equations of motion for the finite element model are derived using Hamilton's principle and based on a moderate deflection theory and accounts for: arbitrary cross-sectional shape, pretwist, generally anisotropic material behavior, transverse shears and out-of-plane warping. Numerical results illustrating the effects of tip sweep, anhedral and composite ply orientation on blade aeroelastic behavior are presented. Tip sweep can induce aeroelastic instability by flap-twist coupling. Tip anhedral causes lag-torsion and flap-axial couplings, however, its effects on blade stability is less pronounced than the effect due to sweep. Composite ply orientation has a substantial effect on blade stability.

  3. Vector and axial-vector resonances in composite models of the Higgs boson

    Energy Technology Data Exchange (ETDEWEB)

    Franzosi, Diogo Buarque [II. Physikalisches Institut, Universität Göttingen,Friedrich-Hund-Platz 1, 37077 Göttingen (Germany); Cacciapaglia, Giacomo; Cai, Haiying; Deandrea, Aldo [Univ Lyon, Université Lyon 1, CNRS/IN2P3, IPNL,F-69622, Villeurbanne (France); Frandsen, Mads [CP-Origins & Danish Institute for Advanced Study DIAS, University of Southern Denmark,Campusvej 55, DK-5230 Odense M (Denmark)


    We provide a non-linear realisation of composite Higgs models in the context of the SU(4)/Sp(4) symmetry breaking pattern, where the effective Lagrangian of the spin-0 and spin-1 resonances is constructed via the CCWZ prescription using the Hidden Symmetry formalism. We investigate the EWPT constraints by accounting the effects from reduced Higgs couplings and integrating out heavy spin-1 resonances. This theory emerges from an underlying theory of gauge interactions with fermions, thus first principle lattice results predict the massive spectrum in composite Higgs models. This model can be used as a template for the phenomenology of composite Higgs models at the LHC and at future 100 TeV colliders, as well as for other application. In this work, we focus on the formalism for spin-1 resonances and their bounds from di-lepton and di-boson searches at the LHC.

  4. Development of constitutive model for composites exhibiting time dependent properties

    International Nuclear Information System (INIS)

    Pupure, L; Joffe, R; Varna, J; Nyström, B


    Regenerated cellulose fibres and their composites exhibit highly nonlinear behaviour. The mechanical response of these materials can be successfully described by the model developed by Schapery for time-dependent materials. However, this model requires input parameters that are experimentally determined via large number of time-consuming tests on the studied composite material. If, for example, the volume fraction of fibres is changed we have a different material and new series of experiments on this new material are required. Therefore the ultimate objective of our studies is to develop model which determines the composite behaviour based on behaviour of constituents of the composite. This paper gives an overview of problems and difficulties, associated with development, implementation and verification of such model

  5. Unified composite model of all fundamental particles and forces

    International Nuclear Information System (INIS)

    Terazawa, H.


    The unified supersymmetric composite model of all fundamental particles (and forces) including not only the fundamental fermions (quarks and leptons) but also the fundamental bosons (gauge bosons and Higgs scalars) is reviewed in detail

  6. Supersymmetric composite models on intersecting D-branes

    International Nuclear Information System (INIS)

    Kitazawa, Noriaki


    We construct supersymmetric composite models of quarks and leptons from type IIA T6/(Z2xZ2) orientifold with intersecting D6-branes. In case of T6=T2xT2xT2 with no tilted T2, a composite model of supersymmetric SU(5) grand unified theory with four generations is constructed. In case of that one T2 is tilted, a composite model with SU(3)cxSU(2)LxU(1)Y gauge symmetry with three generations of left-handed quarks and leptons is constructed. These models are not realistic, but contain relatively fewer additional exotic particles and U(1) gauge symmetries due to the introduction of the compositeness of quarks and leptons. The masses of some exotic particles are naturally generated through the Yukawa interactions among 'preons'

  7. Multivariate Calibration Models for Sorghum Composition using Near-Infrared Spectroscopy

    Energy Technology Data Exchange (ETDEWEB)

    Wolfrum, E.; Payne, C.; Stefaniak, T.; Rooney, W.; Dighe, N.; Bean, B.; Dahlberg, J.


    NREL developed calibration models based on near-infrared (NIR) spectroscopy coupled with multivariate statistics to predict compositional properties relevant to cellulosic biofuels production for a variety of sorghum cultivars. A robust calibration population was developed in an iterative fashion. The quality of models developed using the same sample geometry on two different types of NIR spectrometers and two different sample geometries on the same spectrometer did not vary greatly.

  8. A reconstruction of Maxwell model for effective thermal conductivity of composite materials

    International Nuclear Information System (INIS)

    Xu, J.Z.; Gao, B.Z.; Kang, F.Y.


    Highlights: • Deficiencies were found in classical Maxwell model for effective thermal conductivity. • Maxwell model was reconstructed based on potential mean-field theory. • Reconstructed Maxwell model was extended with particle–particle contact resistance. • Predictions by reconstructed Maxwell model agree excellently with experimental data. - Abstract: Composite materials consisting of high thermal conductive fillers and polymer matrix are often used as thermal interface materials to dissipate heat generated from mechanical and electronic devices. The prediction of effective thermal conductivity of composites remains as a critical issue due to its dependence on considerably factors. Most models for prediction are based on the analog between electric potential and temperature that satisfy the Laplace equation under steady condition. Maxwell was the first to derive the effective electric resistivity of composites by examining the far-field spherical harmonic solution of Laplace equation perturbed by a sphere of different resistivity, and his model was considered as classical. However, a close review of Maxwell’s derivation reveals that there exist several controversial issues (deficiencies) inherent in his model. In this study, we reconstruct the Maxwell model based on a potential mean-field theory to resolve these issues. For composites made of continuum matrix and particle fillers, the contact resistance among particles was introduced in the reconstruction of Maxwell model. The newly reconstructed Maxwell model with contact resistivity as a fitting parameter is shown to fit excellently to experimental data over wide ranges of particle concentration and mean particle diameter. The scope of applicability of the reconstructed Maxwell model is also discussed using the contact resistivity as a parameter.

  9. Butterfly, Recurrence, and Predictability in Lorenz Models (United States)

    Shen, B. W.


    Over the span of 50 years, the original three-dimensional Lorenz model (3DLM; Lorenz,1963) and its high-dimensional versions (e.g., Shen 2014a and references therein) have been used for improving our understanding of the predictability of weather and climate with a focus on chaotic responses. Although the Lorenz studies focus on nonlinear processes and chaotic dynamics, people often apply a "linear" conceptual model to understand the nonlinear processes in the 3DLM. In this talk, we present examples to illustrate the common misunderstandings regarding butterfly effect and discuss the importance of solutions' recurrence and boundedness in the 3DLM and high-dimensional LMs. The first example is discussed with the following folklore that has been widely used as an analogy of the butterfly effect: "For want of a nail, the shoe was lost.For want of a shoe, the horse was lost.For want of a horse, the rider was lost.For want of a rider, the battle was lost.For want of a battle, the kingdom was lost.And all for the want of a horseshoe nail."However, in 2008, Prof. Lorenz stated that he did not feel that this verse described true chaos but that it better illustrated the simpler phenomenon of instability; and that the verse implicitly suggests that subsequent small events will not reverse the outcome (Lorenz, 2008). Lorenz's comments suggest that the verse neither describes negative (nonlinear) feedback nor indicates recurrence, the latter of which is required for the appearance of a butterfly pattern. The second example is to illustrate that the divergence of two nearby trajectories should be bounded and recurrent, as shown in Figure 1. Furthermore, we will discuss how high-dimensional LMs were derived to illustrate (1) negative nonlinear feedback that stabilizes the system within the five- and seven-dimensional LMs (5D and 7D LMs; Shen 2014a; 2015a; 2016); (2) positive nonlinear feedback that destabilizes the system within the 6D and 8D LMs (Shen 2015b; 2017); and (3

  10. Auditing predictive models : a case study in crop growth

    NARCIS (Netherlands)

    Metselaar, K.


    Methods were developed to assess and quantify the predictive quality of simulation models, with the intent to contribute to evaluation of model studies by non-scientists. In a case study, two models of different complexity, LINTUL and SUCROS87, were used to predict yield of forage maize

  11. Models for predicting compressive strength and water absorption of ...

    African Journals Online (AJOL)

    This work presents a mathematical model for predicting the compressive strength and water absorption of laterite-quarry dust cement block using augmented Scheffe's simplex lattice design. The statistical models developed can predict the mix proportion that will yield the desired property. The models were tested for lack of ...

  12. Foundations of compositional models: structural properties

    Czech Academy of Sciences Publication Activity Database

    Jiroušek, Radim; Kratochvíl, Václav


    Roč. 44, č. 1 (2015), s. 2-25 ISSN 0308-1079 R&D Projects: GA ČR GA13-20012S Grant - others:GA ČR(CZ) GAP403/12/2175 Program:GA Institutional support: RVO:67985556 Keywords : multidimensional distribution * conditional independence * composition * semigraphoid properties * running intersection property Subject RIV: BA - General Mathematics Impact factor: 1.677, year: 2015

  13. Analytical and numerical techniques for predicting the interfacial stresses of wavy carbon nanotube/polymer composites

    NARCIS (Netherlands)

    Yazdchi, K.; Salehi, M.; Shokrieh, M.M.


    By introducing a new simplified 3D representative volume element for wavy carbon nanotubes, an analytical model is developed to study the stress transfer in single-walled carbon nanotube-reinforced polymer composites. Based on the pull-out modeling technique, the effects of waviness, aspect ratio,

  14. A refined model for piezoelectric composite beams

    International Nuclear Information System (INIS)

    Luschi, Luca; Pieri, Francesco


    This work presents and compares few simple one-dimensional models for the piezoelectric actuation and detection of beams. The 1D nature, which allows an easy embedding of the model in the classical Euler-Bernoulli beam equations, is obtained by adopting simplifying assumptions along directions of the cross-sectional plane. By changing such assumptions, different models can be built. Their validity is discussed and compared with results of FEM simulations for varying geometries. We show that commonly adopted models fail in a series of practical cases and propose a new model capable of accurately describing wide beams. (paper)

  15. Statistical and Machine Learning Models to Predict Programming Performance


    Bergin, Susan


    This thesis details a longitudinal study on factors that influence introductory programming success and on the development of machine learning models to predict incoming student performance. Although numerous studies have developed models to predict programming success, the models struggled to achieve high accuracy in predicting the likely performance of incoming students. Our approach overcomes this by providing a machine learning technique, using a set of three significant...

  16. Vector-like bottom quarks in composite Higgs models

    DEFF Research Database (Denmark)

    Gillioz, M.; Grober, R.; Kapuvari, A.


    Like many other models, Composite Higgs Models feature the existence of heavy vector-like quarks. Mixing effects between the Standard Model fields and the heavy states, which can be quite large in case of the top quark, imply deviations from the SM. In this work we investigate the possibility of ...

  17. A combination of compositional index and genetic algorithm for predicting transmembrane helical segments.

    Directory of Open Access Journals (Sweden)

    Nazar Zaki

    Full Text Available Transmembrane helix (TMH topology prediction is becoming a focal problem in bioinformatics because the structure of TM proteins is difficult to determine using experimental methods. Therefore, methods that can computationally predict the topology of helical membrane proteins are highly desirable. In this paper we introduce TMHindex, a method for detecting TMH segments using only the amino acid sequence information. Each amino acid in a protein sequence is represented by a Compositional Index, which is deduced from a combination of the difference in amino acid occurrences in TMH and non-TMH segments in training protein sequences and the amino acid composition information. Furthermore, a genetic algorithm was employed to find the optimal threshold value for the separation of TMH segments from non-TMH segments. The method successfully predicted 376 out of the 378 TMH segments in a dataset consisting of 70 test protein sequences. The sensitivity and specificity for classifying each amino acid in every protein sequence in the dataset was 0.901 and 0.865, respectively. To assess the generality of TMHindex, we also tested the approach on another standard 73-protein 3D helix dataset. TMHindex correctly predicted 91.8% of proteins based on TM segments. The level of the accuracy achieved using TMHindex in comparison to other recent approaches for predicting the topology of TM proteins is a strong argument in favor of our proposed method.The datasets, software together with supplementary materials are available at:

  18. Elastic-plastic analysis of AS4/PEEK composite laminate using a one-parameter plasticity model (United States)

    Sun, C. T.; Yoon, K. J.


    A one-parameter plasticity model was shown to adequately describe the plastic deformation of AS4/PEEK (APC-2) unidirectional thermoplastic composite. This model was verified further for unidirectional and laminated composite panels with and without a hole. The elastic-plastic stress-strain relations of coupon specimens were measured and compared with those predicted by the finite element analysis using the one-parameter plasticity model. The results show that the one-parameter plasticity model is suitable for the analysis of elastic-plastic deformation of AS4/PEEK composite laminates.

  19. Probabilistic Modeling and Visualization for Bankruptcy Prediction

    DEFF Research Database (Denmark)

    Antunes, Francisco; Ribeiro, Bernardete; Pereira, Francisco Camara


    In accounting and finance domains, bankruptcy prediction is of great utility for all of the economic stakeholders. The challenge of accurate assessment of business failure prediction, specially under scenarios of financial crisis, is known to be complicated. Although there have been many successful...... studies on bankruptcy detection, seldom probabilistic approaches were carried out. In this paper we assume a probabilistic point-of-view by applying Gaussian Processes (GP) in the context of bankruptcy prediction, comparing it against the Support Vector Machines (SVM) and the Logistic Regression (LR......). Using real-world bankruptcy data, an in-depth analysis is conducted showing that, in addition to a probabilistic interpretation, the GP can effectively improve the bankruptcy prediction performance with high accuracy when compared to the other approaches. We additionally generate a complete graphical...

  20. Accurate and dynamic predictive model for better prediction in medicine and healthcare. (United States)

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


    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.