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Sample records for composite predictive model

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

    International Nuclear Information System (INIS)

    Lowe, K.A.

    1985-01-01

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

  2. 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.

    2004-01-01

    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

  3. Mathematical model predicts the elastic behavior of composite materials

    Directory of Open Access Journals (Sweden)

    Zoroastro de Miranda Boari

    2005-03-01

    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.

  4. 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)

    2017-04-07

    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.

  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.

    2004-01-01

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

    International Nuclear Information System (INIS)

    Colin, X.

    2009-01-01

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

  7. Composition-Based Prediction of Temperature-Dependent Thermophysical Food Properties: Reevaluating Component Groups and Prediction Models.

    Science.gov (United States)

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

    2017-01-01

    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®.

  8. Predicting Jakarta composite index using hybrid of fuzzy time series and support vector regression models

    Science.gov (United States)

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

    2018-03-01

    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.

  9. Predicting community composition from pairwise interactions

    Science.gov (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.

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

    Directory of Open Access Journals (Sweden)

    Volnei Tita

    2012-02-01

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

  11. 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

    2017-01-01

    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...

  12. Micromechanics model for predicting anisotropic electrical conductivity of carbon fiber composite materials

    Science.gov (United States)

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

    2016-07-01

    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.

  13. Fatigue life prediction in composites

    CSIR Research Space (South Africa)

    Huston, RJ

    1994-01-01

    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...

  14. 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.

    1991-01-01

    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

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

    Energy Technology Data Exchange (ETDEWEB)

    Das, N., E-mail: nirupamd@barc.gov.in [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)

    2013-02-15

    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.

  16. 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

    1996-01-01

    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...

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-03-29

    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.

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

    International Nuclear Information System (INIS)

    Ab Ghani, A.F.; Rivai, Ahmad

    2016-01-01

    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.

  19. A multivariate model for predicting segmental body composition.

    Science.gov (United States)

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

    2013-12-01

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

  20. Modified creep and shrinkage prediction model B3 for serviceability limit state analysis of composite slabs

    Science.gov (United States)

    Gholamhoseini, Alireza

    2016-03-01

    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.

  1. Survey of composite particle models of electroweak interaction

    International Nuclear Information System (INIS)

    Suzuki, Mahiko.

    1992-05-01

    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

  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)

    2016-03-11

    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

    2016-01-01

    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. 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)

    2010-08-15

    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)

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

    Science.gov (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.

    2014-01-01

    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

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

    Science.gov (United States)

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

    2018-06-01

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

    International Nuclear Information System (INIS)

    Lietzke, M.H.

    1977-01-01

    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

  8. Empirical Model Development for Predicting Shock Response on Composite Materials Subjected to Pyroshock Loading

    Science.gov (United States)

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

    2015-01-01

    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.

  9. 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

    2017-06-01

    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.

  10. NOAA's National Air Quality Predictions and Development of Aerosol and Atmospheric Composition Prediction Components for the Next Generation Global Prediction System

    Science.gov (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.

    2016-12-01

    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 http://airquality.weather.gov. 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

  11. Biochemical methane potential prediction of plant biomasses: Comparing chemical composition versus near infrared methods and linear versus non-linear models.

    Science.gov (United States)

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

    2015-01-01

    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.

  12. Prediction of frozen food properties during freezing using product composition.

    Science.gov (United States)

    Boonsupthip, W; Heldman, D R

    2007-06-01

    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.

  13. A Progressive Damage Model for Predicting Permanent Indentation and Impact Damage in Composite Laminates

    Science.gov (United States)

    Ji, Zhaojie; Guan, Zhidong; Li, Zengshan

    2017-10-01

    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.

  14. 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.

  15. 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

    2017-01-01

    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)

  16. Durability and life prediction modeling in polyimide composites

    Science.gov (United States)

    Binienda, Wieslaw K.

    1995-01-01

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

  17. Prediction of mechanical properties of composites of HDPE/HA/EAA.

    Science.gov (United States)

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

    2011-04-01

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

  18. NUMERICAL PREDICTION OF COMPOSITE BEAM SUBJECTED TO COMBINED NEGATIVE BENDING AND AXIAL TENSION

    Directory of Open Access Journals (Sweden)

    MAHESAN BAVAN

    2013-08-01

    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.

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

    OpenAIRE

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

    2015-01-01

    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.

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

    International Nuclear Information System (INIS)

    Kongsro, Jørgen

    2008-01-01

    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

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

    Science.gov (United States)

    Pickett, Leon, Jr.

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

  2. Resin flow/fiber deformation model for composites

    International Nuclear Information System (INIS)

    Gutowski, T.G.

    1985-01-01

    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

  3. 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.

    2017-01-01

    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.

  4. Prediction of aged red wine aroma properties from aroma chemical composition. Partial least squares regression models.

    Science.gov (United States)

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

    2003-04-23

    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.

  5. 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....

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

    Directory of Open Access Journals (Sweden)

    Chi-Hua Tung

    2016-01-01

    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.

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

    Science.gov (United States)

    J. E. Winandy; P. K. Lebow

    2001-01-01

    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. Prediction of composite fatigue life under variable amplitude loading using artificial neural network trained by genetic algorithm

    Science.gov (United States)

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

    2018-04-01

    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.

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

    DEFF Research Database (Denmark)

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

    2004-01-01

    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....

  10. Development and Validation of a Constitutive Model for Dental Composites during the Curing Process

    Science.gov (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.

  11. 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.

    1977-01-01

    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

  12. Evaluation and modeling of the eutectic composition of various drug-polyethylene glycol solid dispersions.

    Science.gov (United States)

    Baird, Jared A; Taylor, Lynne S

    2011-06-01

    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.

  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

    2011-01-01

    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. 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)

    2017-09-27

    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

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

    NARCIS (Netherlands)

    Sman, van der R.G.M.

    2008-01-01

    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

  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

    2011-01-01

    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. Prediction of Composite Pressure Vessel Failure Location using Fiber Bragg Grating Sensors

    Science.gov (United States)

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

    2006-01-01

    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.

  18. Boron carbide reinforced aluminium matrix composite: Physical, mechanical characterization and mathematical modelling

    International Nuclear Information System (INIS)

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

    2016-01-01

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

  19. A Local Composition Model for Paraffinic Solid Solutions

    DEFF Research Database (Denmark)

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

    1996-01-01

    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....

  20. 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.

    2016-01-01

    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...

  1. Predicting Magnetoelectric Coupling in Layered and Graded Composites

    Directory of Open Access Journals (Sweden)

    Mirza Bichurin

    2017-07-01

    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.

  2. Fabrication and modelling of 3-3 piezoelectric composites

    Energy Technology Data Exchange (ETDEWEB)

    Perry, Andrew John

    2002-07-01

    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)

  3. 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

    1998-01-01

    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

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

    International Nuclear Information System (INIS)

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

    1995-04-01

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

  5. Finite elements modeling of delaminations in composite laminates

    DEFF Research Database (Denmark)

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

    2011-01-01

    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. Constitutive modelling of composite biopolymer networks.

    Science.gov (United States)

    Fallqvist, B; Kroon, M

    2016-04-21

    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.

  7. Multi-physics modeling of multifunctional composite materials for damage detection

    Science.gov (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

  8. Analytical design model for a piezo-composite unimorph actuator and its verification using lightweight piezo-composite curved actuators

    Science.gov (United States)

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

    2004-06-01

    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.

  9. Applications of a composite model of microstructural evolution

    International Nuclear Information System (INIS)

    Stoller, R.E.

    1986-01-01

    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

  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

    2016-01-01

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

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

    DEFF Research Database (Denmark)

    Hietanen, Ari; Lewis, Randy; Pica, Claudio

    2014-01-01

    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...

  12. Predicting invertebrate assemblage composition from harvesting pressure and environmental characteristics on tropical reef flats

    Science.gov (United States)

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

    2012-03-01

    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.

  13. Modeling and Predicting the Electrical Conductivity of Composite Cathode for Solid Oxide Fuel Cell by Using Support Vector Regression

    Science.gov (United States)

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

    2012-07-01

    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.

  14. A study of composite models at LEP with ALEPH

    International Nuclear Information System (INIS)

    Badaud, F.

    1992-04-01

    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

  15. Empirical Model Development for Predicting Shock Response on Composite Materials Subjected to Pyroshock Loading. Volume 2, Part 1; Appendices

    Science.gov (United States)

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

    2015-01-01

    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.

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

    Directory of Open Access Journals (Sweden)

    Palani Kannan Kandavel

    2017-12-01

    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.

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

    International Nuclear Information System (INIS)

    Kenney, B.; Karan, K.

    2004-01-01

    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)

  18. Bayesian inference model for fatigue life of laminated composites

    DEFF Research Database (Denmark)

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

    2016-01-01

    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 ...

  19. Micromechanical models for graded composite materials

    DEFF Research Database (Denmark)

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

    1997-01-01

    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...

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

    DEFF Research Database (Denmark)

    Pirzamanbein, Behnaz; Lindström, Johan; Poska, Anneli

    2018-01-01

    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...

  1. Predictive Surface Complexation Modeling

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-11-29

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

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

    International Nuclear Information System (INIS)

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

    1997-01-01

    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

  3. Verification and Validation of a Three-Dimensional Generalized Composite Material Model

    Science.gov (United States)

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

    2015-01-01

    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

  4. Modelling of Damage Evolution in Braided Composites: Recent Developments

    Science.gov (United States)

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

    2017-12-01

    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. 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.

    1999-01-01

    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

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

    Directory of Open Access Journals (Sweden)

    Dan Niu

    2016-03-01

    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.

  7. NOAA's National Air Quality Prediction and Development of Aerosol and Atmospheric Composition Prediction Components for NGGPS

    Science.gov (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.

    2017-12-01

    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 http://airquality.weather.gov. 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.

  8. Modelling and simulation of the consolidation behavior during thermoplastic prepreg composites forming process

    Science.gov (United States)

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

    2017-10-01

    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.

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

    Science.gov (United States)

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

    2017-04-28

    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. 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.

    2008-01-01

    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

  11. 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.

    2007-01-01

    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

  12. Thermoviscoelastic characterization and prediction of Kevlar/epoxy composite laminates

    Science.gov (United States)

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

    1990-01-01

    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.

  13. Multifunctional multiscale composites: Processing, modeling and characterization

    Science.gov (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

  14. 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)

    2017-09-01

    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.

  15. Structural modeling for multicell composite rotor blades

    Science.gov (United States)

    Rehfield, Lawrence W.; Atilgan, Ali R.

    1987-01-01

    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.

  16. 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.

    2016-01-01

    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.

  17. 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)

    2004-03-01

    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.)

  18. 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: movahhed@sharif.edu [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)

    2017-01-02

    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.

  19. Predictions of the electro-mechanical response of conductive CNT-polymer composites

    Science.gov (United States)

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

    2018-05-01

    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.

  20. LANDIS PRO: a landscape model that predicts forest composition and structure changes at regional scales

    Science.gov (United States)

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

    2014-01-01

    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...

  1. Short cellulosic fiber/starch acetate composites — micromechanical modeling of Young’s modulus

    DEFF Research Database (Denmark)

    Madsen, Bo; Joffe, Roberts; Peltola, Heidi

    2011-01-01

    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....

  2. Prediction of stock markets by the evolutionary mix-game model

    Science.gov (United States)

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

    2008-06-01

    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.

  3. 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.

    1996-10-01

    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

  4. Statistical Models for Inferring Vegetation Composition from Fossil Pollen

    Science.gov (United States)

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

    2011-12-01

    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.

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

    KAUST Repository

    Mulamba, Pierre Abraham

    2014-12-01

    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.

  6. Text Comprehension Mediates Morphological Awareness, Syntactic Processing, and Working Memory in Predicting Chinese Written Composition Performance

    Science.gov (United States)

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

    2014-01-01

    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

  7. Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou's General Pseudo Amino Acid Composition.

    Science.gov (United States)

    Ahmad, Khurshid; Waris, Muhammad; Hayat, Maqsood

    2016-06-01

    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.

  8. Computational modeling of elastic properties of carbon nanotube/polymer composites with interphase regions. Part II: Mechanical modeling

    KAUST Repository

    Han, Fei; Azdoud, Yan; Lubineau, Gilles

    2014-01-01

    We present two modeling approaches for predicting the macroscopic elastic properties of carbon nanotubes/polymer composites with thick interphase regions at the nanotube/matrix frontier. The first model is based on local continuum mechanics

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

    DEFF Research Database (Denmark)

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

    2012-01-01

    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...

  10. Predicting bioactive glass properties from the molecular chemical composition: glass transition temperature.

    Science.gov (United States)

    O'Donnell, Matthew D

    2011-05-01

    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.

  11. Shared Task System Description: Frustratingly Hard Compositionality Prediction

    DEFF Research Database (Denmark)

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

    2011-01-01

    , 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...

  12. NOAA ESRI Shapefile - sediment composition class predictions in New York offshore planning area from Biogeography Branch

    Data.gov (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...

  13. Predicting the Atmospheric Composition of Extrasolar Giant Planets

    Science.gov (United States)

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

    2004-01-01

    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

  14. 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 ...

  15. 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

    2013-10-01

    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.

  16. Modelling of a multi-temperature plasma composition

    International Nuclear Information System (INIS)

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

    2005-01-01

    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)

  17. A mechanical model for surface layer formation on self-lubricating ceramic composites

    NARCIS (Netherlands)

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

    2010-01-01

    To predict the thickness of a self-lubricating layer on the contact surface of ceramic composite material containing a soft phase during dry sliding test, a mechanical model was built to calculate the material transfer of the soft second phase in the composite to the surface. The tribological test,

  18. A dynamic growth model for prediction of nutrient partitioning and manure production in growing–finishing pigs: Model development and evaluation

    DEFF Research Database (Denmark)

    Danfær, Allan Christian; Jørgensen, Henry; Kebreab, E

    2015-01-01

    trials using growing–finishing pig diets that had a wide range of nutrient chemical composition. Nutrient and water excretion were quantified using the principle of mass conservation. The average daily observed and predicted manure production was 3.79 and 3.99 kg/d, respectively, with a RMSPE of 0.49 kg......Nutrient loading and air emissions from swine operations raise environmental concerns. The objective of the study was to describe and evaluate a mathematical model (Davis Swine Model) of nutrient partitioning and predict manure excretion and composition on a daily basis. State variables...... the body constituent pools. It was assumed that fluxes of metabolites follow saturation kinetics, depending on metabolite concentrations. The main inputs to the model were diet nutrient composition, feed intake, water-to-feed ratio, and initial BW. First, the model was challenged with nutrient partitioning...

  19. Genome-Wide Prediction of DNA Methylation Using DNA Composition and Sequence Complexity in Human.

    Science.gov (United States)

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

    2017-02-16

    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.

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

    DEFF Research Database (Denmark)

    Dimitrov, Nikolay Krasimirov; Kiureghian, Armen Der

    2014-01-01

    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...

  1. Properties predictive modeling through the concept of a hybrid interphase existing between phases in contact

    Science.gov (United States)

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

    2018-02-01

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

  2. Protein and oil composition predictions of single soybeans by transmission Raman spectroscopy.

    Science.gov (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

    2012-08-22

    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.

  3. 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.

  4. 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...

  5. Modelling of polypropylene fibre-matrix composites using finite element analysis

    Directory of Open Access Journals (Sweden)

    2009-01-01

    Full Text Available Polypropylene (PP fibre-matrix composites previously prepared and studied experimentally were modelled using finite element analysis (FEA in this work. FEA confirmed that fibre content and composition controlled stress distribution in all-PP composites. The stress concentration at the fibre-matrix interface became greater with less fibre content. Variations in fibre composition were more significant in higher stress regions of the composites. When fibre modulus increased, the stress concentration at the fibres decreased and the shear stress at the fibre-matrix interface became more intense. The ratio between matrix modulus and fibre modulus was important, as was the interfacial stress in reducing premature interfacial failure and increasing mechanical properties. The model demonstrated that with low fibre concentration, there were insufficient fibres to distribute the applied stress. Under these conditions the matrix yielded when the applied stress reached the matrix yield stress, resulting in increased fibre axial stress. When the fibre content was high, there was matrix depletion and stress transfer was inefficient. The predictions of the FEA model were consistent with experimental and published data.

  6. Modeling and Simulation of Voids in Composite Tape Winding Process Based on Domain Superposition Technique

    Science.gov (United States)

    Deng, Bo; Shi, Yaoyao

    2017-11-01

    The tape winding technology is an effective way to fabricate rotationally composite materials. Nevertheless, some inevitable defects will seriously influence the performance of winding products. One of the crucial ways to identify the quality of fiber-reinforced composite material products is examining its void content. Significant improvement in products' mechanical properties can be achieved by minimizing the void defect. Two methods were applied in this study, finite element analysis and experimental testing, respectively, to investigate the mechanism of how void forming in composite tape winding processing. Based on the theories of interlayer intimate contact and Domain Superposition Technique (DST), a three-dimensional model of prepreg tape void with SolidWorks has been modeled in this paper. Whereafter, ABAQUS simulation software was used to simulate the void content change with pressure and temperature. Finally, a series of experiments were performed to determine the accuracy of the model-based predictions. The results showed that the model is effective for predicting the void content in the composite tape winding process.

  7. Supervised learning classification models for prediction of plant virus encoded RNA silencing suppressors.

    Directory of Open Access Journals (Sweden)

    Zeenia Jagga

    Full Text Available Viral encoded RNA silencing suppressor proteins interfere with the host RNA silencing machinery, facilitating viral infection by evading host immunity. In plant hosts, the viral proteins have several basic science implications and biotechnology applications. However in silico identification of these proteins is limited by their high sequence diversity. In this study we developed supervised learning based classification models for plant viral RNA silencing suppressor proteins in plant viruses. We developed four classifiers based on supervised learning algorithms: J48, Random Forest, LibSVM and Naïve Bayes algorithms, with enriched model learning by correlation based feature selection. Structural and physicochemical features calculated for experimentally verified primary protein sequences were used to train the classifiers. The training features include amino acid composition; auto correlation coefficients; composition, transition, and distribution of various physicochemical properties; and pseudo amino acid composition. Performance analysis of predictive models based on 10 fold cross-validation and independent data testing revealed that the Random Forest based model was the best and achieved 86.11% overall accuracy and 86.22% balanced accuracy with a remarkably high area under the Receivers Operating Characteristic curve of 0.95 to predict viral RNA silencing suppressor proteins. The prediction models for plant viral RNA silencing suppressors can potentially aid identification of novel viral RNA silencing suppressors, which will provide valuable insights into the mechanism of RNA silencing and could be further explored as potential targets for designing novel antiviral therapeutics. Also, the key subset of identified optimal features may help in determining compositional patterns in the viral proteins which are important determinants for RNA silencing suppressor activities. The best prediction model developed in the study is available as a

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

    Directory of Open Access Journals (Sweden)

    Stamenović Marina R.

    2007-01-01

    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.

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

    Directory of Open Access Journals (Sweden)

    Nick eBouskill

    2012-10-01

    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.

  10. Predicting the composition of red wine blends using an array of multicomponent Peptide-based sensors.

    Science.gov (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

    2015-05-20

    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.

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

    Directory of Open Access Journals (Sweden)

    Eman Ghanem

    2015-05-01

    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.

  12. Testing and Life Prediction for Composite Rotor Hub Flexbeams

    Science.gov (United States)

    Murri, Gretchen B.

    2004-01-01

    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.

  13. Composite Cure Process Modeling and Simulations using COMPRO(Registered Trademark) and Validation of Residual Strains using Fiber Optics Sensors

    Science.gov (United States)

    Sreekantamurthy, Thammaiah; Hudson, Tyler B.; Hou, Tan-Hung; Grimsley, Brian W.

    2016-01-01

    Composite cure process induced residual strains and warping deformations in composite components present significant challenges in the manufacturing of advanced composite structure. As a part of the Manufacturing Process and Simulation initiative of the NASA Advanced Composite Project (ACP), research is being conducted on the composite cure process by developing an understanding of the fundamental mechanisms by which the process induced factors influence the residual responses. In this regard, analytical studies have been conducted on the cure process modeling of composite structural parts with varied physical, thermal, and resin flow process characteristics. The cure process simulation results were analyzed to interpret the cure response predictions based on the underlying physics incorporated into the modeling tool. In the cure-kinetic analysis, the model predictions on the degree of cure, resin viscosity and modulus were interpreted with reference to the temperature distribution in the composite panel part and tool setup during autoclave or hot-press curing cycles. In the fiber-bed compaction simulation, the pore pressure and resin flow velocity in the porous media models, and the compaction strain responses under applied pressure were studied to interpret the fiber volume fraction distribution predictions. In the structural simulation, the effect of temperature on the resin and ply modulus, and thermal coefficient changes during curing on predicted mechanical strains and chemical cure shrinkage strains were studied to understand the residual strains and stress response predictions. In addition to computational analysis, experimental studies were conducted to measure strains during the curing of laminated panels by means of optical fiber Bragg grating sensors (FBGs) embedded in the resin impregnated panels. The residual strain measurements from laboratory tests were then compared with the analytical model predictions. The paper describes the cure process

  14. Gene prediction using the Self-Organizing Map: automatic generation of multiple gene models.

    Science.gov (United States)

    Mahony, Shaun; McInerney, James O; Smith, Terry J; Golden, Aaron

    2004-03-05

    Many current gene prediction methods use only one model to represent protein-coding regions in a genome, and so are less likely to predict the location of genes that have an atypical sequence composition. It is likely that future improvements in gene finding will involve the development of methods that can adequately deal with intra-genomic compositional variation. This work explores a new approach to gene-prediction, based on the Self-Organizing Map, which has the ability to automatically identify multiple gene models within a genome. The current implementation, named RescueNet, uses relative synonymous codon usage as the indicator of protein-coding potential. While its raw accuracy rate can be less than other methods, RescueNet consistently identifies some genes that other methods do not, and should therefore be of interest to gene-prediction software developers and genome annotation teams alike. RescueNet is recommended for use in conjunction with, or as a complement to, other gene prediction methods.

  15. Fatigue life prediction in woven carbon fabric polyester composites

    International Nuclear Information System (INIS)

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

    1999-01-01

    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)

  16. 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.

    2000-01-01

    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

  17. Assessment of Fecal Near-infrared Spectroscopy to Predict Feces Chemical Composition and Apparent Total Tract Digestibility of Nutrients in Pigs.

    Science.gov (United States)

    Nirea, K G; Pérez de Nanclares, M; Skugor, A; Afseth, N K; Meuwissen, T H E; Hansen, J Ø; Mydland, L T; Øverland, M

    2018-05-08

    Apparent total tract digestibility (ATTD) of nutrients could be an alternative measure of feed efficiency when breeding for robust animals that are fed fiber-rich diets. Apparent total tract digestibility of nutrients requires measuring individual feed intake of a large number of animals which is expensive and complex. Alternatively, ATTD of nutrients and feces chemical composition can be predicted using fecal near-infrared spectroscopy (FNIRS). The objective of this study was to assess if the feces chemical composition and ATTD of nutrients can be predicted using FNIRS that originate from various pig experimental datasets. Fecal samples together with detailed information on the feces chemical composition and ATTD of nutrients were obtained from four different pig experiments. Feces near-infrared spectroscopy were analyzed from fecal samples of a complete dataset. The model was calibrated using the FNIRS and reference samples of feces chemical composition and ATTD of nutrients. The robustness and predictability of the model was evaluated by the r2 and the closeness between SE of calibration (SEC) and SE of cross-validation (SECV). Prediction of the feces chemical components and ATTD of nutrients was successful as SEC and SECV were equivalent. Calibration model was developed to estimate the ATTD of nutrients and fecal chemical composition from the FNIRS and worked well for OM (r2 = 0.94; SEC = 48.5; SECV = 56.6), CP ( r2 = 0.89; SEC = 18.1; SECV = 18.8), GE ( r2 = 0.92; SEC = 1.2; SECV = 1.4), NDF (r2 = 0.94 ; SEC = 55; SECV = 60.2), OM digestibility (r2 = 0.94; SEC = 5.5; SECV = 6.7), GE digestibility (r2 = 0.88; SEC = 2.3; SECV = 2.6) and fat digestibility (r2 = 0.79 ; SEC = 6, SECV = 6.8). However, the SE of prediction was slightly higher than what has been reported in another study. The prediction of feces chemical composition for fat (r2 = 0.69; SEC = 11.7, SECV = 12.3), CP digestibility (r2 = 0.63; SEC = 2.3; SECV = 2.7) and NDF digestibility (r2 = 0.64, SEC

  18. Predictive modeling of reactive wetting and metal joining.

    Energy Technology Data Exchange (ETDEWEB)

    van Swol, Frank B.

    2013-09-01

    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.

  19. 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.

    2011-01-01

    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.

  20. Modelling low velocity impact induced damage in composite laminates

    Science.gov (United States)

    Shi, Yu; Soutis, Constantinos

    2017-12-01

    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.

  1. Elastic-plastic analysis of AS4/PEEK composite laminate using a one-parameter plasticity model

    Science.gov (United States)

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

    1992-01-01

    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.

  2. Nonlinear analysis of AS4/PEEK thermoplastic composite laminate using a one parameter plasticity model

    Science.gov (United States)

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

    1990-01-01

    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.

  3. 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)

    2007-07-01

    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.

  4. Experimental verification of a progressive damage model for composite laminates based on continuum damage mechanics. M.S. Thesis Final Report

    Science.gov (United States)

    Coats, Timothy William

    1994-01-01

    Progressive failure is a crucial concern when using laminated composites in structural design. Therefore the ability to model damage and predict the life of laminated composites is vital. The purpose of this research was to experimentally verify the application of the continuum damage model, a progressive failure theory utilizing continuum damage mechanics, to a toughened material system. Damage due to tension-tension fatigue was documented for the IM7/5260 composite laminates. Crack density and delamination surface area were used to calculate matrix cracking and delamination internal state variables, respectively, to predict stiffness loss. A damage dependent finite element code qualitatively predicted trends in transverse matrix cracking, axial splits and local stress-strain distributions for notched quasi-isotropic laminates. The predictions were similar to the experimental data and it was concluded that the continuum damage model provided a good prediction of stiffness loss while qualitatively predicting damage growth in notched laminates.

  5. Finite element model updating of natural fibre reinforced composite structure in structural dynamics

    Directory of Open Access Journals (Sweden)

    Sani M.S.M.

    2016-01-01

    Full Text Available Model updating is a process of making adjustment of certain parameters of finite element model in order to reduce discrepancy between analytical predictions of finite element (FE and experimental results. Finite element model updating is considered as an important field of study as practical application of finite element method often shows discrepancy to the test result. The aim of this research is to perform model updating procedure on a composite structure as well as trying improving the presumed geometrical and material properties of tested composite structure in finite element prediction. The composite structure concerned in this study is a plate of reinforced kenaf fiber with epoxy. Modal properties (natural frequency, mode shapes, and damping ratio of the kenaf fiber structure will be determined using both experimental modal analysis (EMA and finite element analysis (FEA. In EMA, modal testing will be carried out using impact hammer test while normal mode analysis using FEA will be carried out using MSC. Nastran/Patran software. Correlation of the data will be carried out before optimizing the data from FEA. Several parameters will be considered and selected for the model updating procedure.

  6. Molecular modeling of polymer composite-analyte interactions in electronic nose sensors

    Science.gov (United States)

    Shevade, A. V.; Ryan, M. A.; Homer, M. L.; Manfreda, A. M.; Zhou, H.; Manatt, K. S.

    2003-01-01

    We report a molecular modeling study to investigate the polymer-carbon black (CB) composite-analyte interactions in resistive sensors. These sensors comprise the JPL electronic nose (ENose) sensing array developed for monitoring breathing air in human habitats. The polymer in the composite is modeled based on its stereoisomerism and sequence isomerism, while the CB is modeled as uncharged naphthalene rings with no hydrogens. The Dreiding 2.21 force field is used for the polymer, solvent molecules and graphite parameters are assigned to the carbon black atoms. A combination of molecular mechanics (MM) and molecular dynamics (NPT-MD and NVT-MD) techniques are used to obtain the equilibrium composite structure by inserting naphthalene rings in the polymer matrix. Polymers considered for this work include poly(4-vinylphenol), polyethylene oxide, and ethyl cellulose. Analytes studied are representative of both inorganic and organic compounds. The results are analyzed for the composite microstructure by calculating the radial distribution profiles as well as for the sensor response by predicting the interaction energies of the analytes with the composites. c2003 Elsevier Science B.V. All rights reserved.

  7. Dust Composition in Climate Models: Current Status and Prospects

    Science.gov (United States)

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

    2015-12-01

    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.

  8. Constitutive modeling of two-phase metallic composites with application to tungsten-based composite 93W–4.9Ni–2.1Fe

    International Nuclear Information System (INIS)

    Lu, W.R.; Gao, C.Y.; Ke, Y.L.

    2014-01-01

    The two-phase metallic composites, composed by the metallic particulate reinforcing phase and the metallic matrix phase, have attracted a lot of attention in recent years for their excellent material properties. However, the constitutive modeling of two-phase metallic composites is still lacking currently. Most used models for them are basically oriented for single-phase homogeneous metallic materials, and have not considered the microstructural evolution of the components in the composite. This paper develops a new constitutive model for two-phase metallic composites based on the thermally activated dislocation motion mechanism and the volume fraction evolution. By establishing the relation between microscopic volume fraction and macroscopic state variables (strain, strain rate and temperature), the evolution law of volume fraction during the plastic deformation in two-phase composites is proposed for the first time and introduced into the new model. Then the new model is applied to a typical two-phase tungsten-based composite – 93W–4.9Ni–2.1Fe tungsten heavy alloy. It has been found that our model can effectively describe the plastic deformation behaviors of the tungsten-based composite, because of the introduction of volume fraction evolution and the connecting of macroscopic state variables and micromechanical characteristics in the constitutive model. The model's validation by experimental data indicates that our new model can provide a satisfactory prediction of flow stress for two-phase metallic composites, which is better than conventional single-phase homogeneous constitutive models including the Johnson–Cook (JC), Khan–Huang–Liang (KHL), Nemat-Nasser–Li (NNL), Zerilli–Armstrong (ZA) and Voyiadjis–Abed (VA) models

  9. Novel Predictive Model of the Debonding Strength for Masonry Members Retrofitted with FRP

    Directory of Open Access Journals (Sweden)

    Iman Mansouri

    2016-11-01

    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.

  10. Host Genome Influence on Gut Microbial Composition and Microbial Prediction of Complex Traits in Pigs.

    Science.gov (United States)

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

    2017-07-01

    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.

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

    CERN Document Server

    Thostenson, E T

    2003-01-01

    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...

  12. Bootstrap prediction and Bayesian prediction under misspecified models

    OpenAIRE

    Fushiki, Tadayoshi

    2005-01-01

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

  13. 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....

  14. 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

    2014-01-01

    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

  15. Control and design of volumetric composition in pultruded hybrid fibre composites

    DEFF Research Database (Denmark)

    Madsen, Bo; Hashemi, Fariborz; Tahir, Paridah

    2016-01-01

    composition (i.e. volume fractions of fibres, matrix and porosity) in hybrid fibre composites. The model is based on a constant local fibre volume fraction criterion. Good agreement is found between model predictions and experimental data of pultruded hybrid kenaf/glass fibre composites with variable hybrid...... fibre weight mixing ratios. To demonstrate the suitability of the model, simulations are performed for four different cases of volumetric composition in hybrid kenaf/glass composites....

  16. Thermal modeling of the ceramic composite fuel for light water reactors

    International Nuclear Information System (INIS)

    Revankar, S.T.; Latta, R.; Solomon, A.A.

    2005-01-01

    Full text of publication follows: Composite fuel designs capable of providing improved thermal performance are of great interest in advanced reactor designs where high efficiency and long fuel cycles are desired. Thermal modeling of the composite fuel consisting of continuous second phase in a ceramic (uranium oxide) matrix has been carried out with detailed examination of the microstructure of the composite and the interface. Assuming that constituent phases are arranged as slabs, upper and lower bounds for the thermal conductivity of the composite are derived analytically. Bounding calculations on the thermal conductivity of the composite were performed for SiC dispersed in the UO 2 matrix. It is found that with 10% SiC, the thermal conductivity increases from 5.8 to 9.8 W/m.deg. K at 500 K, or an increase of 69% was observed in UO 2 matrix. The finite element analysis computer program ANSYS was used to create composite fuel geometries with set boundary conditions to produce accurate thermal conductivity predictions. A model developed also accounts for SiC-matrix interface resistance and the addition of coatings or interaction barriers. The first set of calculations using the code was to model simple series and parallel fuel slab geometries, and then advance to inter-connected parallel pathways. The analytical calculations were compared with the ANSYS results. The geometry of the model was set up as a 1 cm long by 400 micron wide rectangle. This rectangle was then divided into one hundred sections with the first ninety percent of a single section being UO 2 and the remaining ten percent consisting of SiC. The model was then meshed using triangular type elements. The boundary conditions were set with the sides of the rectangle being adiabatic and having an assigned temperature at the end of the rectangle. A heat flux was then applied to one end of the model producing a temperature gradient. The effective thermal conductivity was then calculated using the geometry

  17. Modelling hydrodynamic parameters to predict flow assisted corrosion

    International Nuclear Information System (INIS)

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

    1992-01-01

    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

  18. Theoretical Development of an Orthotropic Elasto-Plastic Generalized Composite Material Model

    Science.gov (United States)

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

    2014-01-01

    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.

  19. A composite computational model of liver glucose homeostasis. I. Building the composite model.

    Science.gov (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

    2012-04-07

    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.

  20. 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)

    1997-11-01

    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.

  1. 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)

    2010-10-15

    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.

  2. On the Effect of Unit-Cell Parameters in Predicting the Elastic Response of Wood-Plastic Composites

    Directory of Open Access Journals (Sweden)

    Fatemeh Alavi

    2013-01-01

    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.

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

    International Nuclear Information System (INIS)

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

    2011-01-01

    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)

  4. 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.

    2013-03-01

    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.

  5. Partially composite Higgs models

    DEFF Research Database (Denmark)

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

    2018-01-01

    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...

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

    OpenAIRE

    David Ebert

    2006-01-01

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

  7. Assessment and propagation of mechanical property uncertainties in fatigue life prediction of composite laminates

    DEFF Research Database (Denmark)

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

    2018-01-01

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

  8. Probabilistic predictive modelling of carbon nanocomposites for medical implants design.

    Science.gov (United States)

    Chua, Matthew; Chui, Chee-Kong

    2015-04-01

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

  9. Predicting stone composition before treatment – can it really drive clinical decisions?

    Science.gov (United States)

    Bres–Niewada, Ewa; Radziszewski, Piotr

    2014-01-01

    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

  10. Statistical shear lag model - unraveling the size effect in hierarchical composites.

    Science.gov (United States)

    Wei, Xiaoding; Filleter, Tobin; Espinosa, Horacio D

    2015-05-01

    Numerous experimental and computational studies have established that the hierarchical structures encountered in natural materials, such as the brick-and-mortar structure observed in sea shells, are essential for achieving defect tolerance. Due to this hierarchy, the mechanical properties of natural materials have a different size dependence compared to that of typical engineered materials. This study aimed to explore size effects on the strength of bio-inspired staggered hierarchical composites and to define the influence of the geometry of constituents in their outstanding defect tolerance capability. A statistical shear lag model is derived by extending the classical shear lag model to account for the statistics of the constituents' strength. A general solution emerges from rigorous mathematical derivations, unifying the various empirical formulations for the fundamental link length used in previous statistical models. The model shows that the staggered arrangement of constituents grants composites a unique size effect on mechanical strength in contrast to homogenous continuous materials. The model is applied to hierarchical yarns consisting of double-walled carbon nanotube bundles to assess its predictive capabilities for novel synthetic materials. Interestingly, the model predicts that yarn gauge length does not significantly influence the yarn strength, in close agreement with experimental observations. Copyright © 2015 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

  11. Derivation of a new ADAS-cog composite using tree-based multivariate analysis: prediction of conversion from mild cognitive impairment to Alzheimer disease.

    Science.gov (United States)

    Llano, Daniel A; Laforet, Genevieve; Devanarayan, Viswanath

    2011-01-01

    Model-based statistical approaches were used to compare the ability of the Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-cog), cerebrospinal fluid (CSF), fluorodeoxyglucose positron emission tomography and volumetric magnetic resonance imaging (MRI) markers to predict 12-month progression from mild cognitive impairment (MCI) to Alzheimer disease (AD). Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set, properties of the 11-item ADAS-cog (ADAS.11), the 13-item ADAS-cog (ADAS.All) and novel composite scores were compared, using weighting schemes derived from the Random Forests (RF) tree-based multivariate model. Weighting subscores using the RF model of ADAS.All enhanced discrimination between elderly controls, MCI and AD patients. The ability of the RF-weighted ADAS-cog composite and individual scores, along with neuroimaging or biochemical biomarkers to predict MCI to AD conversion over 12 months was also assessed. Although originally optimized to discriminate across diagnostic categories, the ADAS. All, weighted according to the RF model, did nearly as well or better than individual or composite baseline neuroimaging or CSF biomarkers in prediction of 12-month conversion from MCI to AD. These suggest that a modified subscore weighting scheme applied to the 13-item ADAS-cog is comparable to imaging or CSF markers in prediction of conversion from MCI to AD at 12 months. Copyright © 2011 by Lippincott Williams & Wilkins

  12. Theoretical modeling of iodine value and saponification value of biodiesel fuels from their fatty acid composition

    Energy Technology Data Exchange (ETDEWEB)

    Gopinath, A.; Puhan, Sukumar; Nagarajan, G. [Internal Combustion Engineering Division, Department of Mechanical Engineering, Anna University, Chennai 600 025, Tamil Nadu (India)

    2009-07-15

    Biodiesel is an alternative fuel consisting of alkyl esters of fatty acids from vegetable oils or animal fats. The properties of biodiesel depend on the type of vegetable oil used for the transesterification process. The objective of the present work is to theoretically predict the iodine value and the saponification value of different biodiesels from their fatty acid methyl ester composition. The fatty acid ester compositions and the above values of different biodiesels were taken from the available published data. A multiple linear regression model was developed to predict the iodine value and saponification value of different biodiesels. The predicted results showed that the prediction errors were less than 3.4% compared to the available published data. The predicted values were also verified by substituting in the available published model which was developed to predict the higher heating values of biodiesel fuels from their iodine value and the saponification value. The resulting heating values of biodiesels were then compared with the published heating values and reported. (author)

  13. 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.

    2011-01-01

    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.

  14. Numerical simulation of elasto-plastic deformation of composites: evolution of stress microfields and implications for homogenization models

    Science.gov (United States)

    González, C.; Segurado, J.; LLorca, J.

    2004-07-01

    The deformation of a composite made up of a random and homogeneous dispersion of elastic spheres in an elasto-plastic matrix was simulated by the finite element analysis of three-dimensional multiparticle cubic cells with periodic boundary conditions. "Exact" results (to a few percent) in tension and shear were determined by averaging 12 stress-strain curves obtained from cells containing 30 spheres, and they were compared with the predictions of secant homogenization models. In addition, the numerical simulations supplied detailed information of the stress microfields, which was used to ascertain the accuracy and the limitations of the homogenization models to include the nonlinear deformation of the matrix. It was found that secant approximations based on the volume-averaged second-order moment of the matrix stress tensor, combined with a highly accurate linear homogenization model, provided excellent predictions of the composite response when the matrix strain hardening rate was high. This was not the case, however, in composites which exhibited marked plastic strain localization in the matrix. The analysis of the evolution of the matrix stresses revealed that better predictions of the composite behavior can be obtained with new homogenization models which capture the essential differences in the stress carried by the elastic and plastic regions in the matrix at the onset of plastic deformation.

  15. A composite model of electroweak interactions and its manifestation at current collider energies

    International Nuclear Information System (INIS)

    Craigie, N.S.

    1984-05-01

    We present a preon model based on an ASF confining gauge theory, which has as a low energy effective Lagrangian, an electroweak gauge theory very close to the standard model. However, it is predicted that there are some specific and necessary deviations from the Glashow-Salam-Weinberg model. In this preon model, we assume a spontaneous breakdown (or an induced breakdown) of the left-right symmetry, which prevents spin-one composites made up of right-handed fermions propagating well below the composite scale of order 1 TeV. A consequence of this assumption is shown to be the existence of a pion-like scalar, in addition to the Higgs particle of the standard model. Such a particle - it is further claimed - can give rise to single photon events, through a large branching ratio into the channel π → Z γ or if lighter than the Z through Z → π(→νν-bar) + γ. The model also predicts a signal very similar to the associated gluino production one of supersymmetric grand unified theories. (author)

  16. Evaluation of Material Models within LS-DYNA(Registered TradeMark) for a Kevlar/Epoxy Composite Honeycomb

    Science.gov (United States)

    Polanco, Michael A.; Kellas, Sotiris; Jackson, Karen

    2009-01-01

    The performance of material models to simulate a novel composite honeycomb Deployable Energy Absorber (DEA) was evaluated using the nonlinear explicit dynamic finite element code LS-DYNA(Registered TradeMark). Prototypes of the DEA concept were manufactured using a Kevlar/Epoxy composite material in which the fibers are oriented at +/-45 degrees with respect to the loading axis. The development of the DEA has included laboratory tests at subcomponent and component levels such as three-point bend testing of single hexagonal cells, dynamic crush testing of single multi-cell components, and impact testing of a full-scale fuselage section fitted with a system of DEA components onto multi-terrain environments. Due to the thin nature of the cell walls, the DEA was modeled using shell elements. In an attempt to simulate the dynamic response of the DEA, it was first represented using *MAT_LAMINATED_COMPOSITE_FABRIC, or *MAT_58, in LS-DYNA. Values for each parameter within the material model were generated such that an in-plane isotropic configuration for the DEA material was assumed. Analytical predictions showed that the load-deflection behavior of a single-cell during three-point bending was within the range of test data, but predicted the DEA crush response to be very stiff. In addition, a *MAT_PIECEWISE_LINEAR_PLASTICITY, or *MAT_24, material model in LS-DYNA was developed, which represented the Kevlar/Epoxy composite as an isotropic elastic-plastic material with input from +/-45 degrees tensile coupon data. The predicted crush response matched that of the test and localized folding patterns of the DEA were captured under compression, but the model failed to predict the single-cell three-point bending response.

  17. Mathematical model for predicting molecular-beam epitaxy growth rates for wafer production

    International Nuclear Information System (INIS)

    Shi, B.Q.

    2003-01-01

    An analytical mathematical model for predicting molecular-beam epitaxy (MBE) growth rates is reported. The mathematical model solves the mass-conservation equation for liquid sources in conical crucibles and predicts the growth rate by taking into account the effect of growth source depletion on the growth rate. Assumptions made for deducing the analytical model are discussed. The model derived contains only one unknown parameter, the value of which can be determined by using data readily available to MBE growers. Procedures are outlined for implementing the model in MBE production of III-V compound semiconductor device wafers. Results from use of the model to obtain targeted layer compositions and thickness of InP-based heterojunction bipolar transistor wafers are presented

  18. 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)

    2016-11-11

    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.

  19. Modeling of robotic fish propelled by an ionic polymer-metal composite caudal fin

    Science.gov (United States)

    Chen, Zheng; Shatara, Stephan; Tan, Xiaobo

    2009-03-01

    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.

  20. Metabolic modeling to identify engineering targets for Komagataella phaffii: The effect of biomass composition on gene target identification.

    Science.gov (United States)

    Cankorur-Cetinkaya, Ayca; Dikicioglu, Duygu; Oliver, Stephen G

    2017-11-01

    Genome-scale metabolic models are valuable tools for the design of novel strains of industrial microorganisms, such as Komagataella phaffii (syn. Pichia pastoris). However, as is the case for many industrial microbes, there is no executable metabolic model for K. phaffiii that confirms to current standards by providing the metabolite and reactions IDs, to facilitate model extension and reuse, and gene-reaction associations to enable identification of targets for genetic manipulation. In order to remedy this deficiency, we decided to reconstruct the genome-scale metabolic model of K. phaffii by reconciling the extant models and performing extensive manual curation in order to construct an executable model (Kp.1.0) that conforms to current standards. We then used this model to study the effect of biomass composition on the predictive success of the model. Twelve different biomass compositions obtained from published empirical data obtained under a range of growth conditions were employed in this investigation. We found that the success of Kp1.0 in predicting both gene essentiality and growth characteristics was relatively unaffected by biomass composition. However, we found that biomass composition had a profound effect on the distribution of the fluxes involved in lipid, DNA, and steroid biosynthetic processes, cellular alcohol metabolic process, and oxidation-reduction process. Furthermore, we investigated the effect of biomass composition on the identification of suitable target genes for strain development. The analyses revealed that around 40% of the predictions of the effect of gene overexpression or deletion changed depending on the representation of biomass composition in the model. Considering the robustness of the in silico flux distributions to the changing biomass representations enables better interpretation of experimental results, reduces the risk of wrong target identification, and so both speeds and improves the process of directed strain development

  1. Steady-state creep of discontinuous fibre composites

    International Nuclear Information System (INIS)

    Boecker Pedersen, O.

    1975-07-01

    A review is given of the relevant literature on creep of composites, including a presentation of existing models for the steady-state creep of composites containing aligned discontinuous fibres where creep of the matrix and fibres is assumed to follow a power law. A model is suggested for predicting the composite creep law from a matrix creep law given in a general form, in the case where the fibres do not creep. The composite creep law predicted by this model is compared with those predicted by previous models, when these are extended to comprise a general matrix creep law. Experimentally, pure copper and composites consisting of aligned discontinuous tungsten fibres in a copper matrix were creep tested at a temperature of 500 deg C. The results indicate a relatively low stress sensitivity of the steady-state creep-rate for pure copper and relatively high stress sensitivity for the composites. This may be explained by the creep models based upon a general matrix creep law. A quantitative prediction shows promising agreement with the present experimental results. (author)

  2. Design prediction for long term stress rupture service of composite pressure vessels

    Science.gov (United States)

    Robinson, Ernest Y.

    1992-01-01

    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.

  3. An Ice Model That is Consistent with Composite Rheology in GIA Modelling

    Science.gov (United States)

    Huang, P.; Patrick, W.

    2017-12-01

    There are several popular approaches in constructing ice history models. One of them is mainly based on thermo-mechanical ice models with forcing or boundary conditions inferred from paleoclimate data. The second one is mainly based on the observed response of the Earth to glacial loading and unloading, a process called Glacial Isostatic Adjustment or GIA. The third approach is a hybrid version of the first and second approaches. In this presentation, we will follow the second approach which also uses geological data such as ice flow, terminal moraine data and simple ice dynamic for the ice sheet re-construction (Peltier & Andrew 1976). The global ice model ICE-6G (Peltier et al. 2015) and all its predecessors (Tushingham & Peltier 1991, Peltier 1994, 1996, 2004, Lambeck et al. 2014) are constructed this way with the assumption that mantle rheology is linear. However, high temperature creep experiments on mantle rocks show that non-linear creep laws can also operate in the mantle. Since both linear (e.g. diffusion creep) and non-linear (e.g. dislocation) creep laws can operate simultaneously in the mantle, mantle rheology is likely composite, where the total creep is the sum of both linear and onlinear creep. Preliminary GIA studies found that composite rheology can fit regional RSL observations better than that from linear rheology(e.g. van der Wal et al. 2010). The aim of this paper is to construct ice models in Laurentia and Fennoscandia using this second approach, but with composite rheology, so that its predictions can fit GIA observations such as global RSL data, land uplift rate and g-dot simultaneously in addition to geological data and simple ice dynamics. The g-dot or gravity-rate-of-change data is from the GRACE gravity mission but with the effects of hydrology removed. Our GIA model is based on the Coupled Laplace-Finite Element method as described in Wu(2004) and van der Wal et al.(2010). It is found that composite rheology generally supports a thicker

  4. Case study on prediction of remaining methane potential of landfilled municipal solid waste by statistical analysis of waste composition data.

    Science.gov (United States)

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

    2016-10-01

    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.

  5. Finite element modeling of small-scale tapered wood-laminated composite poles with biomimicry features

    Science.gov (United States)

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

    2008-01-01

    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...

  6. Life Prediction for FRP composites with Data Fusion & Machine Learning

    Data.gov (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...

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

    International Nuclear Information System (INIS)

    Gregory, Jeremy R.; Spearing, S. Mark

    2004-01-01

    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

  8. Performance modeling of direct contact membrane distillation (DCMD) seawater desalination process using a commercial composite membrane

    KAUST Repository

    Lee, Junggil

    2015-01-10

    This paper presents the development of a rigorous theoretical model to predict the transmembrane flux of a flat sheet hydrophobic composite membrane, comprising both an active layer of polytetrafluoroethylene and a scrim-backing support layer of polypropylene, in the direct contact membrane distillation (DCMD) process. An integrated model includes the mass, momentum, species and energy balances for both retentate and permeate flows, coupled with the mass transfer of water vapor through the composite membrane and the heat transfer across the membrane and through the boundary layers adjacent to the membrane surfaces. Experimental results and model predictions for permeate flux and performance ratio are compared and shown to be in good agreement. The permeate flux through the composite layer can be ignored in the consideration of mass transfer pathways at the composite membrane. The effect of the surface porosity and the thickness of active and support layers on the process performance of composite membrane has also been studied. Among these parameters, surface porosity is identified to be the main factor significantly influencing the permeate flux and performance ratio, while the relative influence of the surface porosity on the performance ratio is less than that on flux.

  9. Modeling Mechanical Properties of Aluminum Composite Produced Using Stir Casting Method

    Directory of Open Access Journals (Sweden)

    Muhammad Hayat Jokhio

    2011-01-01

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

  10. Modeling mechanical properties of aluminum composite produced using stir casting method

    International Nuclear Information System (INIS)

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

    2011-01-01

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

  11. Vector and Axial-vector resonances in composite models of the Higgs boson

    DEFF Research Database (Denmark)

    Franzosi, Diogo Buarque; Cacciapaglia, Giacomo; Cai, Haiying

    2016-01-01

    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 const...... 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.......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...

  12. 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). http://dx.doi.org/10.4314/jassa.v6i2.16844.

  13. 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

    2007-01-01

    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

  14. A nonlinear magnetoelectric model for magnetoelectric layered composite with coupling stress

    International Nuclear Information System (INIS)

    Shi, Yang; Gao, Yuanwen

    2014-01-01

    Based on a linear piezoelectric relation and a nonlinear magnetostrictive constitutive relation, A nonlinear magnetoelectric (ME) effect model for flexural layered ME composites is established in in-plane magnetic field. In the proposed model, the true coupling stress and the equivalent piezomagnetic coefficient are taken into account and obtained through an iterative approach. Some calculations on nonlinear ME coefficient are conducted and discussed. Our results show that for both the flexural bilayer and trilayer composites, the true coupling stress in the composites first increase and then approach to a constant value with the increase of applied magnetic fields, affecting the nonlinear ME effect significantly. With consideration of the true coupling stress, the ME effect is smaller than that without consideration of the true coupling stress. Moreover, the proposed theoretical model predicts that the ME coefficient of the trilayer composite (does not generate the bending deflection) is much larger than that of bilayer composite (generates the bending deflection), which is in well agreement with the previous works. The influences of the applied magnetic field on the true coupling stress and fraction ratio corresponding to the extreme ME coefficients of layered structures are also investigated. - Highlights: • This paper develops a nonlinear model for layered ME composite. • The true coupling stress is obtained through an iterative approach. • The influences of coupling stress and flexural deformation are discussed. • The dependence of ME coefficient on magnetic field is studied

  15. Incorporation of lysosomal sequestration in the mechanistic model for prediction of tissue distribution of basic drugs.

    Science.gov (United States)

    Assmus, Frauke; Houston, J Brian; Galetin, Aleksandra

    2017-11-15

    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

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

    KAUST Repository

    Zhang, Zhang

    2010-11-08

    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.

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

    KAUST Repository

    Zhang, Zhang; Yu, Jun

    2010-01-01

    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.

  18. 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.

    2007-01-01

    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

  19. Micromechanical Models of Mechanical Response of High Performance Fibre Reinforced Cement Composites

    DEFF Research Database (Denmark)

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

    1996-01-01

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

  20. Analytical method for predicting plastic flow in notched fiber composite materials

    International Nuclear Information System (INIS)

    Flynn, P.L.; Ebert, L.J.

    1977-01-01

    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

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

    Directory of Open Access Journals (Sweden)

    Kazi Tani Nabil

    2018-01-01

    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

  2. Volumetric composition in composites and historical data

    DEFF Research Database (Denmark)

    Lilholt, Hans; Madsen, Bo

    2013-01-01

    The obtainable volumetric composition in composites is of importance for the prediction of mechanical and physical properties, and in particular to assess the best possible (normally the highest) values for these properties. The volumetric model for the composition of (fibrous) composites gives...... guidance to the optimal combination of fibre content, matrix content and porosity content, in order to achieve the best obtainable properties. Several composite materials systems have been shown to be handleable with this model. An extensive series of experimental data for the system of cellulose fibres...... and polymer (resin) was produced in 1942 – 1944, and these data have been (re-)analysed by the volumetric composition model, and the property values for density, stiffness and strength have been evaluated. Good agreement has been obtained and some further observations have been extracted from the analysis....

  3. Developing models for the prediction of hospital healthcare waste generation rate.

    Science.gov (United States)

    Tesfahun, Esubalew; Kumie, Abera; Beyene, Abebe

    2016-01-01

    An increase in the number of health institutions, along with frequent use of disposable medical products, has contributed to the increase of healthcare waste generation rate. For proper handling of healthcare waste, it is crucial to predict the amount of waste generation beforehand. Predictive models can help to optimise healthcare waste management systems, set guidelines and evaluate the prevailing strategies for healthcare waste handling and disposal. However, there is no mathematical model developed for Ethiopian hospitals to predict healthcare waste generation rate. Therefore, the objective of this research was to develop models for the prediction of a healthcare waste generation rate. A longitudinal study design was used to generate long-term data on solid healthcare waste composition, generation rate and develop predictive models. The results revealed that the healthcare waste generation rate has a strong linear correlation with the number of inpatients (R(2) = 0.965), and a weak one with the number of outpatients (R(2) = 0.424). Statistical analysis was carried out to develop models for the prediction of the quantity of waste generated at each hospital (public, teaching and private). In these models, the number of inpatients and outpatients were revealed to be significant factors on the quantity of waste generated. The influence of the number of inpatients and outpatients treated varies at different hospitals. Therefore, different models were developed based on the types of hospitals. © The Author(s) 2015.

  4. Model Predictive Control of Mineral Column Flotation Process

    Directory of Open Access Journals (Sweden)

    Yahui Tian

    2018-06-01

    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. Predicting growth of the healthy infant using a genome scale metabolic model.

    Science.gov (United States)

    Nilsson, Avlant; Mardinoglu, Adil; Nielsen, Jens

    2017-01-01

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

  6. 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)

    2006-07-01

    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)

  7. Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition.

    Science.gov (United States)

    Hayat, Maqsood; Khan, Asifullah

    2011-02-21

    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 http://111.68.99.218/Mem-Predictor. Copyright © 2010 Elsevier Ltd. All rights reserved.

  8. A new aeroelastic model for composite rotor blades with straight and swept tips

    Science.gov (United States)

    Yuan, Kuo-An; Friedmann, Peretz P.; Venkatesan, Comandur

    1992-01-01

    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.

  9. Validation of an Acoustic Impedance Prediction Model for Skewed Resonators

    Science.gov (United States)

    Howerton, Brian M.; Parrott, Tony L.

    2009-01-01

    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.

  10. Process conditions and volumetric composition in composites

    DEFF Research Database (Denmark)

    Madsen, Bo

    2013-01-01

    The obtainable volumetric composition in composites is linked to the gravimetric composition, and it is influenced by the conditions of the manufacturing process. A model for the volumetric composition is presented, where the volume fractions of fibers, matrix and porosity are calculated...... as a function of the fiber weight fraction, and where parameters are included for the composite microstructure, and the fiber assembly compaction behavior. Based on experimental data of composites manufactured with different process conditions, together with model predictions, different types of process related...... effects are analyzed. The applied consolidation pressure is found to have a marked effect on the volumetric composition. A power-law relationship is found to well describe the found relations between the maximum obtainable fiber volume fraction and the consolidation pressure. The degree of fiber...

  11. Body composition and hydration factors in infants and young children using multicompartment models

    International Nuclear Information System (INIS)

    Villegas-Valle, Rosa Consuelo; Valencia, Mauro E; Sotelo-Cruz, Norberto; Antunez-Roman, Lesley Evelyn; Lopez-Jimenez, Cesar A; Monreal-Barraza, Brianda I; Robles-Valenzuela, Edna L; Hurtado-Valenzuela, Jaime Gabriel

    2014-01-01

    Full text: Background. Until recently deuterium (2H2O) analysis has been performed almost exclusively by isotope ratio mass spectrometry (IRMS). The IAEA has promoted the FTIR methodology to measure deuterium (2H2O) enrichment, but there is limited information in infants and small children, which have different hydration status than adults. Due to the limited information available, the optimum deuterium dose amount to be administered to children in these studies has also been controversial. The aim of this investigation were to measure body composition and determine the hydration factors in infants and young children using multi-compartment models generating algorithms for prediction of body composition. Subjects and Methods. Seventy-eight male and female infants and young children (ages 3-24 months), from the urban and agricultural zones of Hermosillo, Sonora, Mexico participated. We measured weight, length and circumferences to evaluate nutritional status using the WHO Growth Reference 2006. We also measured total body water (TBW) by deuterium oxide dilution, bone mineral content (BMC) through a DXA scan and body density was estimated through published algorithms. Bioimpedance analysis (BIA) was also measured to explore the prediction of body composition using this technique. Results. In general, children from the urban area had better nutritional indicators than children from the agricultural area. Eleven (16.1%) children had some type of malnutrition (any nutritional index below -2 Z cutoff point) and 2 were overweight. Optimal amount of deuterium for dosing in this age range was 0.53 to 0.83 mg/kg body weight, which has implications for future studies of body composition in infants and young children. DXA overestimated body fat percentage compared to other 2, 3 and 4 compartment models (p 0.05). Resistance or impedance indexes (Height2/R or Z) were not important predictors of FFM or TBW (increase in R2 = 0.004). Prediction of FFM was then performed by using

  12. Constitutive modeling of fiber-reinforced cement composites

    Science.gov (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

  13. Prediction of Material Properties of Nanostructured Polymer Composites Using Atomistic Simulations

    Science.gov (United States)

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

    2009-01-01

    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.

  14. Life Modeling and Design Analysis for Ceramic Matrix Composite Materials

    Science.gov (United States)

    2005-01-01

    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.

  15. Modelling bankruptcy prediction models in Slovak companies

    Directory of Open Access Journals (Sweden)

    Kovacova Maria

    2017-01-01

    Full Text Available An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression and early artificial intelligence models (e.g. artificial neural networks, there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models.

  16. Predicting growth of the healthy infant using a genome scale metabolic model

    DEFF Research Database (Denmark)

    Nilsson, Avlant; Mardinoglu, Adil; Nielsen, Jens

    2017-01-01

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

  17. A Kinetic Model for Predicting the Relative Humidity in Modified Atmosphere Packaging and Its Application in Lentinula edodes Packages

    Directory of Open Access Journals (Sweden)

    Li-xin Lu

    2013-01-01

    Full Text Available Adjusting and controlling the relative humidity (RH inside package is crucial for ensuring the quality of modified atmosphere packaging (MAP of fresh produce. In this paper, an improved kinetic model for predicting the RH in MAP was developed. The model was based on heat exchange and gases mass transport phenomena across the package, gases heat convection inside the package, and mass and heat balances accounting for the respiration and transpiration behavior of fresh produce. Then the model was applied to predict the RH in MAP of fresh Lentinula edodes (one kind of Chinese mushroom. The model equations were solved numerically using Adams-Moulton method to predict the RH in model packages. In general, the model predictions agreed well with the experimental data, except that the model predictions were slightly high in the initial period. The effect of the initial gas composition on the RH in packages was notable. In MAP of lower oxygen and higher carbon dioxide concentrations, the ascending rate of the RH was reduced, and the RH inside packages was saturated slowly during storage. The influence of the initial gas composition on the temperature inside package was not much notable.

  18. Micromechanical modeling of tungsten-based bulk metallic glass matrix composites

    Energy Technology Data Exchange (ETDEWEB)

    Li Hao [Department of Mechanical Engineering-Engineering Mechanics, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931 (United States); Li Ke [Department of Mechanical Engineering, Texas A and M University, TAMU 3123, College Station, TX 77843 (United States)]. E-mail: keli@tamu.edu; Subhash, Ghatu [Department of Mechanical Engineering-Engineering Mechanics, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931 (United States); Kecskes, Laszlo J. [Weapons and Materials Research Directorate, US Army Research Laboratory, Aberdeen Proving Ground, MD 21005 (United States); Dowding, Robert J. [Weapons and Materials Research Directorate, US Army Research Laboratory, Aberdeen Proving Ground, MD 21005 (United States)

    2006-08-15

    Micromechanics models are developed for tungsten (W)-based bulk metallic glass (BMG) matrix composites employing the Voronoi tessellation technique and the finite element (FE) method. The simulation results indicate that the computed elastic moduli are close to those measured in the experiments. The predicted stress-strain curves agree well with their experimentally obtained counterparts in the early stage of the plastic deformation. An increase in the W volume fraction leads to a decrease in the yield stress and an increase in the Young's modulus of the composite. In addition, contours of equivalent plastic strain for increasing applied strains provide an explanation why shear bands were observed in the glassy phase, along the W/BMG interface, and in the W phase of failed W/BMG composite specimens.

  19. In Situ Strength Model for Continuous Fibers and Multi-Scale Modeling the Fracture of C/SiC Composites

    Science.gov (United States)

    Zhang, Sheng; Gao, Xiguang; Song, Yingdong

    2018-04-01

    A new in situ strength model of carbon fibers was developed based on the distribution of defects to predict the stress-strain response and the strength of C/SiC composites. Different levels of defects in the fibers were considered in this model. The defects in the fibers were classified by their effects on the strength of the fiber. The strength of each defect and the probability that the defect appears were obtained from the tensile test of single fibers. The strength model of carbon fibers was combined with the shear-lag model to predict the stress-strain responses and the strengths of fiber bundles and C/SiC minicomposites. To verify the strength model, tensile tests were performed on fiber bundles and C/SiC minicomposites. The predicted and experimental results were in good agreement. Effects of the fiber length, the fiber number and the heat treatment on the final strengths of fiber bundles and C/SiC minicomposites were also discussed.

  20. Prediction of Process-Induced Distortions in L-Shaped Composite Profiles Using Path-Dependent Constitutive Law

    Science.gov (United States)

    Ding, Anxin; Li, Shuxin; Wang, Jihui; Ni, Aiqing; Sun, Liangliang; Chang, Lei

    2016-10-01

    In this paper, the corner spring-in angles of AS4/8552 L-shaped composite profiles with different thicknesses are predicted using path-dependent constitutive law with the consideration of material properties variation due to phase change during curing. The prediction accuracy mainly depends on the properties in the rubbery and glassy states obtained by homogenization method rather than experimental measurements. Both analytical and finite element (FE) homogenization methods are applied to predict the overall properties of AS4/8552 composite. The effect of fiber volume fraction on the properties is investigated for both rubbery and glassy states using both methods. And the predicted results are compared with experimental measurements for the glassy state. Good agreement is achieved between the predicted results and available experimental data, showing the reliability of the homogenization method. Furthermore, the corner spring-in angles of L-shaped composite profiles are measured experimentally and the reliability of path-dependent constitutive law is validated as well as the properties prediction by FE homogenization method.

  1. Support for the Logical Execution Time Model on a Time-predictable Multicore Processor

    DEFF Research Database (Denmark)

    Kluge, Florian; Schoeberl, Martin; Ungerer, Theo

    2016-01-01

    The logical execution time (LET) model increases the compositionality of real-time task sets. Removal or addition of tasks does not influence the communication behavior of other tasks. In this work, we extend a multicore operating system running on a time-predictable multicore processor to support...... the LET model. For communication between tasks we use message passing on a time-predictable network-on-chip to avoid the bottleneck of shared memory. We report our experiences and present results on the costs in terms of memory and execution time....

  2. Strategies to predict and improve eating quality of cooked beef using carcass and meat composition traits in Angus cattle.

    Science.gov (United States)

    Mateescu, R G; Oltenacu, P A; Garmyn, A J; Mafi, G G; VanOverbeke, D L

    2016-05-01

    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

  3. Modeling plant composition as community continua in a forest landscape with LiDAR and hyperspectral remote sensing.

    Science.gov (United States)

    Hakkenberg, C R; Peet, R K; Urban, D L; Song, C

    2018-01-01

    In light of the need to operationalize the mapping of forest composition at landscape scales, this study uses multi-scale nested vegetation sampling in conjunction with LiDAR-hyperspectral remotely sensed data from the G-LiHT airborne sensor to map vascular plant compositional turnover in a compositionally and structurally complex North Carolina Piedmont forest. Reflecting a shift in emphasis from remotely sensing individual crowns to detecting aggregate optical-structural properties of forest stands, predictive maps reflect the composition of entire vascular plant communities, inclusive of those species smaller than the resolution of the remotely sensed imagery, intertwined with proximate taxa, or otherwise obscured from optical sensors by dense upper canopies. Stand-scale vascular plant composition is modeled as community continua: where discrete community-unit classes at different compositional resolutions provide interpretable context for continuous gradient maps that depict n-dimensional compositional complexity as a single, consistent RGB color combination. In total, derived remotely sensed predictors explain 71%, 54%, and 48% of the variation in the first three components of vascular plant composition, respectively. Among all remotely sensed environmental gradients, topography derived from LiDAR ground returns, forest structure estimated from LiDAR all returns, and morphological-biochemical traits determined from hyperspectral imagery each significantly correspond to the three primary axes of floristic composition in the study site. Results confirm the complementarity of LiDAR and hyperspectral sensors for modeling the environmental gradients constraining landscape turnover in vascular plant composition and hold promise for predictive mapping applications spanning local land management to global ecosystem modeling. © 2017 by the Ecological Society of America.

  4. A Bayesian Performance Prediction Model for Mathematics Education: A Prototypical Approach for Effective Group Composition

    Science.gov (United States)

    Bekele, Rahel; McPherson, Maggie

    2011-01-01

    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…

  5. Thermodynamic modeling of saturated liquid compositions and densities for asymmetric binary systems composed of carbon dioxide, alkanes and alkanols

    International Nuclear Information System (INIS)

    Bayestehparvin, Bita; Nourozieh, Hossein; Kariznovi, Mohammad; Abedi, Jalal

    2015-01-01

    Highlights: • Phase behavior of the binary systems containing largely different components. • Equation of state modeling of binary polar and non-polar systems by utilizing different mixing rules. • Three different mixing rules (one-parameter, two-parameters and Wong–Sandler) coupled with Peng–Robinson equation of state. • Two-parameter mixing rule shows promoting results compared to one-parameter mixing rule. • Wong–Sandler mixing rule is unable to predict saturated liquid densities with sufficient accuracy. - Abstract: The present study mainly focuses on the phase behavior modeling of asymmetric binary mixtures. Capability of different mixing rules and volume shift in the prediction of solubility and saturated liquid density has been investigated. Different binary systems of (alkane + alkanol), (alkane + alkane), (carbon dioxide + alkanol), and (carbon dioxide + alkane) are considered. The composition and the density of saturated liquid phase at equilibrium condition are the properties of interest. Considering composition and saturated liquid density of different binary systems, three main objectives are investigated. First, three different mixing rules (one-parameter, two parameters and Wong–Sandler) coupled with Peng–Robinson equation of state were used to predict the equilibrium properties. The Wong–Sandler mixing rule was utilized with the non-random two-liquid (NRTL) model. Binary interaction coefficients and NRTL model parameters were optimized using the Levenberg–Marquardt algorithm. Second, to improve the density prediction, the volume translation technique was applied. Finally, Two different approaches were considered to tune the equation of state; regression of experimental equilibrium compositions and densities separately and spontaneously. The modeling results show that there is no superior mixing rule which can predict the equilibrium properties for different systems. Two-parameter and Wong–Sandler mixing rule show promoting

  6. Artificial Neural Networks for the Prediction of Wear Properties of Al6061-TiO2 Composites

    Science.gov (United States)

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

    2017-08-01

    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.

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

    Science.gov (United States)

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

    2018-05-16

    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.

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

    International Nuclear Information System (INIS)

    Kozak, Vladislav; Chlup, Zdenek

    2010-01-01

    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.

  9. 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

    2009-01-01

    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

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

    DEFF Research Database (Denmark)

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

    2017-01-01

    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....

  11. Numerical Material Model for Composite Laminates in High-Velocity Impact Simulation

    Directory of Open Access Journals (Sweden)

    Tao Liu

    Full Text Available Abstract A numerical material model for composite laminate, was developed and integrated into the nonlinear dynamic explicit finite element programs as a material user subroutine. This model coupling nonlinear state of equation (EOS, was a macro-mechanics model, which was used to simulate the major mechanical behaviors of composite laminate under high-velocity impact conditions. The basic theoretical framework of the developed material model was introduced. An inverse flyer plate simulation was conducted, which demonstrated the advantage of the developed model in characterizing the nonlinear shock response. The developed model and its implementation were validated through a classic ballistic impact issue, i.e. projectile impacting on Kevlar29/Phenolic laminate. The failure modes and ballistic limit velocity were analyzed, and a good agreement was achieved when comparing with the analytical and experimental results. The computational capacity of this model, for Kevlar/Epoxy laminates with different architectures, i.e. plain-woven and cross-plied laminates, was further evaluated and the residual velocity curves and damage cone were accurately predicted.

  12. In vitro/in silico investigation of failure criteria to predict flexural strength of composite resins.

    Science.gov (United States)

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

    2018-01-30

    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.

  13. Plant fibre composites - porosity and volumetric interaction

    DEFF Research Database (Denmark)

    Madsen, Bo; Thygesen, Anders; Lilholt, Hans

    2007-01-01

    the combination of a high fibre volume fraction, a low porosity and a high composite density is optimal. Experimental data from the literature on volumetric composition and density of four types of plant fibre composites are used to validate the model. It is demonstrated that the model provides a concept......Plant fibre composites contain typically a relative large amount of porosity, which considerably influences properties and performance of the composites. The large porosity must be integrated in the conversion of weight fractions into volume fractions of the fibre and matrix parts. A model...... is presented to predict the porosity as a function of the fibre weight fractions, and to calculate the related fibre and matrix volume fractions, as well as the density of the composite. The model predicts two cases of composite volumetric interaction separated by a transition fibre weight fraction, at which...

  14. Prediction of wrinklings and porosities of thermoplastic composits after thermostamping

    Science.gov (United States)

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

    2018-05-01

    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. Modelling of high temperature interfacial reactions in continuously reinforced Ti/SiC metal matrix composites (MMCs)

    International Nuclear Information System (INIS)

    Fox, K.M.

    1993-01-01

    Previous experimental work by Gundel and Wawner showed that the matrix alloy has a strong effect on reaction layer growth in Ti alloy/SCS-6 composite systems. A finite difference technique was used to model the reaction layer growth, which predicts the same trends as those exhibited by the experimental data. Matrix alloying elements such as Mo and Cr in metastable β alloys will affect the equilibrium compositions and diffusivities in the matrix, but matrix diffusion is not found to be rate controlling. Regular solution thermodynamic models indicate that the main affect of matrix composition is in controlling carbon-flux through the reaction layer by altering equilibrium C-TiC-Ti interfacial compositions. (orig.)

  16. Predicting the chromatographic retention of polymers: application of the polymer model to poly(styrene/ethylacrylate)copolymers.

    Science.gov (United States)

    Bashir, Mubasher A; Radke, Wolfgang

    2012-02-17

    The retention behavior of a range of statistical poly(styrene/ethylacrylate) copolymers is investigated, in order to determine the possibility to predict retention volumes of these copolymers based on a suitable chromatographic retention model. It was found that the composition of elution in gradient chromatography of the copolymers is closely related to the eluent composition at which, in isocratic chromatography, the transition from elution in adsorption to exclusion mode occurs. For homopolymers this transition takes place at a critical eluent composition at which the molar mass dependence of elution volume vanishes. Thus, similar critical eluent compositions can be defined for statistical copolymers. The existence of a critical eluent composition is further supported by the narrower peak width, indicating that the broad molar mass distribution of the samples does not contribute to the retention volume. It is shown that the existing retention model for homopolymers allows for correct quantitative predictions of retention volumes based on only three appropriate initial experiments. The selection of these initial experiments involves a gradient run and two isocratic experiments, one at the composition of elution calculated from first gradient run and second at a slightly higher eluent strength. Copyright © 2011 Elsevier B.V. All rights reserved.

  17. Plasticity and fracture modeling of three-layer steel composite Tribond® 1200 for crash simulation

    NARCIS (Netherlands)

    Eller, Tom; Ramaker, Kenny; Greve, Lars; Andres, M.T.; Hazrati Marangalou, Javad; van den Boogaard, Antonius H.

    2017-01-01

    A constitutive model is presented for the three-layer steel composite Tribond® 1200. Tribond® is a hot forming steel which consists of three layers: a high strength steel core between two outer layers of ductile low strength steel. The model is designed to provide an accurate prediction of the

  18. A New Material Constitutive Model for Predicting Cladding Failure

    Energy Technology Data Exchange (ETDEWEB)

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

    2009-06-15

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

  19. Predictive modeling of complications.

    Science.gov (United States)

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

    2016-09-01

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

  20. Effective electrical conductivity of carbon nanotube-polymer composites: a simplified model and its validation

    International Nuclear Information System (INIS)

    Jang, Sung-Hwan; Yin, Huiming

    2015-01-01

    A simplified model is presented to predict the effective electrical conductivity of carbon nanotube(CNT)-polymer composite with different material proportions, which is validated by the experiments of multi-walled CNT/polydimethylsiloxane (PDMS) composites. CNTs are well dispersed in a PDMS matrix, and the mixture is then cured and cast into thin films for electrical characterization. The CNTs are assumed to be statistically uniformly distributed in the PDMS matrix with the three-dimensional (3D) waviness. As the proportion of CNTs increases to a certain level, namely the percolation threshold, the discrete CNTs start to connect with each other, forming a 3D network which exhibits a significant increase of effective electrical conductivity. The eight-chain model has been used to predict the effective electrical conductivity of the composite, in which the contact resistance between CNTs has been considered through the Simmons’ equation. The eight-chain network features can be significantly changed with the modification to mixing process, CNT length and diameter, and CNT clustering and curling. A Gaussian statistics-based formulation is used to calculate the effective length of a single CNT well dispersed in the matrix. The modeling results of effective electrical conductivity agree with the experiments very well, which are highly dependent on a contact resistance between CNTs and the waviness of the CNTs. The effect of inner-nanotube distance and diameter of CNTs on the effective electrical conductivity of the CNT/PDMS composite is also discussed. (paper)

  1. An effective simplified model of composite compression struts for partially-restrained steel frame with reinforced concrete infill walls

    Science.gov (United States)

    Sun, Guohua; Chuang-Sheng, Walter Yang; Gu, Qiang; DesRoches, Reginald

    2018-04-01

    To resolve the issue regarding inaccurate prediction of the hysteretic behavior by micro-based numerical analysis for partially-restrained (PR) steel frames with solid reinforced concrete (RC) infill walls, an innovative simplified model of composite compression struts is proposed on the basis of experimental observation on the cracking distribution, load transferring mechanism, and failure modes of RC infill walls filled in PR steel frame. The proposed composite compression struts model for the solid RC infill walls is composed of α inclined struts and main diagonal struts. The α inclined struts are used to reflect the part of the lateral force resisted by shear connectors along the frame-wall interface, while the main diagonal struts are introduced to take into account the rest of the lateral force transferred along the diagonal direction due to the complicated interaction between the steel frame and RC infill walls. This study derives appropriate formulas for the effective widths of the α inclined strut and main diagonal strut, respectively. An example of PR steel frame with RC infill walls simulating simulated by the composite inclined compression struts model is illustrated. The maximum lateral strength and the hysteresis curve shape obtained from the proposed composite strut model are in good agreement with those from the test results, and the backbone curve of a PR steel frame with RC infill walls can be predicted precisely when the inter-story drift is within 1%. This simplified model can also predict the structural stiffness and the equivalent viscous damping ratio well when the inter-story drift ratio exceeds 0.5%.

  2. Rotary ultrasonic machining of CFRP: a mechanistic predictive model for cutting force.

    Science.gov (United States)

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

    2014-02-01

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

  3. A nonlinear efficient layerwise finite element model for smart piezolaminated composites under strong applied electric field

    International Nuclear Information System (INIS)

    Kapuria, S; Yaqoob Yasin, M

    2013-01-01

    In this work, we present an electromechanically coupled efficient layerwise finite element model for the static response of piezoelectric laminated composite and sandwich plates, considering the nonlinear behavior of piezoelectric materials under strong electric field. The nonlinear model is developed consistently using a variational principle, considering a rotationally invariant second order nonlinear constitutive relationship, and full electromechanical coupling. In the piezoelectric layer, the electric potential is approximated to have a quadratic variation across the thickness, as observed from exact three dimensional solutions, and the equipotential condition of electroded piezoelectric surfaces is modeled using the novel concept of an electric node. The results predicted by the nonlinear model compare very well with the experimental data available in the literature. The effect of the piezoelectric nonlinearity on the static response and deflection/stress control is studied for piezoelectric bimorph as well as hybrid laminated plates with isotropic, angle-ply composite and sandwich substrates. For high electric fields, the difference between the nonlinear and linear predictions is large, and cannot be neglected. The error in the prediction of the smeared counterpart of the present theory with the same number of primary displacement unknowns is also examined. (paper)

  4. Polynomial Chaos Characterization of Uncertainty in Multiscale Models and Behavior of Carbon Reinforced Composites

    Energy Technology Data Exchange (ETDEWEB)

    Mehrez, Loujaine [University of Southern California; Ghanem, Roger [University of Southern California; Aitharaju, Venkat [General Motors; Rodgers, William [General Motors

    2017-10-23

    Design of non-crimp fabric (NCF) composites entails major challenges pertaining to (1) the complex fine-scale morphology of the constituents, (2) the manufacturing-produced inconsistency of this morphology spatially, and thus (3) the ability to build reliable, robust, and efficient computational surrogate models to account for this complex nature. Traditional approaches to construct computational surrogate models have been to average over the fluctuations of the material properties at different scale lengths. This fails to account for the fine-scale features and fluctuations in morphology, material properties of the constituents, as well as fine-scale phenomena such as damage and cracks. In addition, it fails to accurately predict the scatter in macroscopic properties, which is vital to the design process and behavior prediction. In this work, funded in part by the Department of Energy, we present an approach for addressing these challenges by relying on polynomial chaos representations of both input parameters and material properties at different scales. Moreover, we emphasize the efficiency and robustness of integrating the polynomial chaos expansion with multiscale tools to perform multiscale assimilation, characterization, propagation, and prediction, all of which are necessary to construct the data-driven surrogate models required to design under the uncertainty of composites. These data-driven constructions provide an accurate map from parameters (and their uncertainties) at all scales and the system-level behavior relevant for design. While this perspective is quite general and applicable to all multiscale systems, NCF composites present a particular hierarchy of scales that permits the efficient implementation of these concepts.

  5. Disturbance observer based model predictive control for accurate atmospheric entry of spacecraft

    Science.gov (United States)

    Wu, Chao; Yang, Jun; Li, Shihua; Li, Qi; Guo, Lei

    2018-05-01

    Facing the complex aerodynamic environment of Mars atmosphere, a composite atmospheric entry trajectory tracking strategy is investigated in this paper. External disturbances, initial states uncertainties and aerodynamic parameters uncertainties are the main problems. The composite strategy is designed to solve these problems and improve the accuracy of Mars atmospheric entry. This strategy includes a model predictive control for optimized trajectory tracking performance, as well as a disturbance observer based feedforward compensation for external disturbances and uncertainties attenuation. 500-run Monte Carlo simulations show that the proposed composite control scheme achieves more precise Mars atmospheric entry (3.8 km parachute deployment point distribution error) than the baseline control scheme (8.4 km) and integral control scheme (5.8 km).

  6. 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

    2015-01-01

    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...

  7. External intermittency prediction using AMR solutions of RANS turbulence and transported PDF models

    Science.gov (United States)

    Olivieri, D. A.; Fairweather, M.; Falle, S. A. E. G.

    2011-12-01

    External intermittency in turbulent round jets is predicted using a Reynolds-averaged Navier-Stokes modelling approach coupled to solutions of the transported probability density function (pdf) equation for scalar variables. Solutions to the descriptive equations are obtained using a finite-volume method, combined with an adaptive mesh refinement algorithm, applied in both physical and compositional space. This method contrasts with conventional approaches to solving the transported pdf equation which generally employ Monte Carlo techniques. Intermittency-modified eddy viscosity and second-moment turbulence closures are used to accommodate the effects of intermittency on the flow field, with the influence of intermittency also included, through modifications to the mixing model, in the transported pdf equation. Predictions of the overall model are compared with experimental data on the velocity and scalar fields in a round jet, as well as against measurements of intermittency profiles and scalar pdfs in a number of flows, with good agreement obtained. For the cases considered, predictions based on the second-moment turbulence closure are clearly superior, although both turbulence models give realistic predictions of the bimodal scalar pdfs observed experimentally.

  8. Surface complexation modeling of Cd(II) sorption to montmorillonite, bacteria, and their composite

    Science.gov (United States)

    Wang, Ning; Du, Huihui; Huang, Qiaoyun; Cai, Peng; Rong, Xingmin; Feng, Xionghan; Chen, Wenli

    2016-10-01

    Surface complexation modeling (SCM) has emerged as a powerful tool for simulating heavy metal adsorption processes on the surface of soil solid components under different geochemical conditions. The component additivity (CA) approach is one of the strategies that have been widely used in multicomponent systems. In this study, potentiometric titration, isothermal adsorption, zeta potential measurement, and extended X-ray absorption fine-structure (EXAFS) spectra analysis were conducted to investigate Cd adsorption on 2 : 1 clay mineral montmorillonite, on Gram-positive bacteria Bacillus subtilis, and their mineral-organic composite. We developed constant capacitance models of Cd adsorption on montmorillonite, bacterial cells, and mineral-organic composite. The adsorption behavior of Cd on the surface of the composite was well explained by CA-SCM. Some deviations were observed from the model simulations at pH SCM closely coincided with the estimated value of EXAFS at pH 6. The model could be useful for the prediction of heavy metal distribution at the interface of multicomponents and their risk evaluation in soils and associated environments.

  9. Composites

    International Nuclear Information System (INIS)

    Kasen, M.B.

    1983-01-01

    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

  10. Mechanical Model Development for Composite Structural Supercapacitors

    Science.gov (United States)

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

    2016-01-01

    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.

  11. Thermo-mechanical characterization of a thermoplastic composite and prediction of the residual stresses and lamina curvature during cooling

    Science.gov (United States)

    Péron, Mael; Jacquemin, Frédéric; Casari, Pascal; Orange, Gilles; Bailleul, Jean-Luc; Boyard, Nicolas

    2017-10-01

    The prediction of process induced stresses during the cooling of thermoplastic composites still represents a challenge for the scientific community. However, a precise determination of these stresses is necessary in order to optimize the process conditions and thus lower the stresses effects on the final part health. A model is presented here, that permits the estimation of residual stresses during cooling. It relies on the nonlinear laminate theory, which has been adapted to arbitrary layup sequences. The developed model takes into account the heat transfers through the thickness of the laminate, together with the crystallization kinetics. The development of the composite mechanical properties during cooling is addressed by an incremental linear elastic constitutive law, which also considers thermal and crystallization strains. In order to feed the aforementioned model, a glass fiber and PA6.6 matrix unidirectional (UD) composite has been characterized. This work finally focuses on the identification of the material and process related parameters that lower the residual stresses level, including the ply sequence, the fiber volume fraction and the cooling rate.

  12. Modeled hydrologic metrics show links between hydrology and the functional composition of stream assemblages.

    Science.gov (United States)

    Patrick, Christopher J; Yuan, Lester L

    2017-07-01

    Flow alteration is widespread in streams, but current understanding of the effects of differences in flow characteristics on stream biological communities is incomplete. We tested hypotheses about the effect of variation in hydrology on stream communities by using generalized additive models to relate watershed information to the values of different flow metrics at gauged sites. Flow models accounted for 54-80% of the spatial variation in flow metric values among gauged sites. We then used these models to predict flow metrics in 842 ungauged stream sites in the mid-Atlantic United States that were sampled for fish, macroinvertebrates, and environmental covariates. Fish and macroinvertebrate assemblages were characterized in terms of a suite of metrics that quantified aspects of community composition, diversity, and functional traits that were expected to be associated with differences in flow characteristics. We related modeled flow metrics to biological metrics in a series of stressor-response models. Our analyses identified both drying and base flow instability as explaining 30-50% of the observed variability in fish and invertebrate community composition. Variations in community composition were related to variations in the prevalence of dispersal traits in invertebrates and trophic guilds in fish. The results demonstrate that we can use statistical models to predict hydrologic conditions at bioassessment sites, which, in turn, we can use to estimate relationships between flow conditions and biological characteristics. This analysis provides an approach to quantify the effects of spatial variation in flow metrics using readily available biomonitoring data. © 2017 by the Ecological Society of America.

  13. Candidate Prediction Models and Methods

    DEFF Research Database (Denmark)

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

    2005-01-01

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

  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.

    1975-02-01

    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. Effects of glass composition on the residual rate of alteration and modelling parameters

    International Nuclear Information System (INIS)

    Fleury, Benjamin

    2013-01-01

    This PhD thesis deals with the long-term behavior of the French nuclear glasses R7T7. An experiment plan (based on 27 glass compositions) is developed for studying the effect of glass composition on the residual rate of alteration. The impact of Mg-phase precipitation on glass alteration is also studied and several modelling exercises are performed. There is one order of magnitude between the different measurements (rate or pH...) associated with the different glass compositions. The statistical treatment of these measurements results in predictive equations and several observable trends are valid for all materials with a composition complying with the experiment plan conditions. The effect of Si, Na, B and Al on alteration (.i.e, gel and secondary phase's formation, pH) is confirmed, the influence of Zn, Zr and Ni-Co is evidenced. The role of Cr has to be clarified. Experiments show that glass alteration rate in underground water, which contains high level of Ca and Mg, is one order of magnitude higher than in the case of pure water. The glass composition plays the same role for the alteration in the two types of solution. During alteration, the late addition of Mg introduces a time lag in the resumption response because silicon is first provided from partial dissolution of the previously-formed alteration gel. The nucleation process does not limit Mg-silicate precipitation whereas a pH above 8-8.5 is necessary for Mg-silicate precipitation. The glass alteration rate can be a limiting factor if the quantity of Mg supplied to the reaction is enough. The Mg silicate phase seems to have systematically a molar ratio Mg/Si between 0.2 and 0.4. It is also shown that the air tightness of the reactor influences the rate of dissolution of CO 2 in the solution leading to a decrease of pH. Finally, modelling exercises with GRAAL show promising results. Such modelling is required in order to extend the prediction of the long-term alteration behavior to different glass

  16. Composite fermions in the quantum Hall effect

    International Nuclear Information System (INIS)

    Johnson, B.L.; Kirczenow, G.

    1997-01-01

    The quantum Hall effect and associated quantum transport phenomena in low-dimensional systems have been the focus of much attention for more than a decade. Recent theoretical development of interesting quasiparticles - 'composite fermions' - has led to significant advances in understanding and predicting the behaviour of two-dimensional electron systems under high transverse magnetic fields. Composite fermions may be viewed as fermions carrying attached (fictitious) magnetic flux. Here we review models of the integer and fractional quantum Hall effects, including the development of a unified picture of the integer and fractional effects based upon composite fermions. The composite fermion picture predicts remarkable new physics: the formation of a Fermi surface at high magnetic fields, and anomalous ballistic transport, thermopower, and surface acoustic wave behaviour. The specific theoretical predictions of the model, as well as the body of experimental evidence for these phenomena are reviewed. We also review recent edge-state models for magnetotransport in low-dimensional devices based on the composite fermion picture. These models explain the fractional quantum Hall effect and transport phenomena in nanoscale devices in a unified framework that also includes edge state models of the integer quantum Hall effect. The features of the composite fermion edge-state model are compared and contrasted with those of other recent edge-state models of the fractional quantum Hall effect. (author)

  17. Composite Social Network for Predicting Mobile Apps Installation

    Science.gov (United States)

    2011-06-02

    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 ...ar 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

  18. Composite hadron models

    International Nuclear Information System (INIS)

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

    1983-01-01

    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

  19. Wind power prediction models

    Science.gov (United States)

    Levy, R.; Mcginness, H.

    1976-01-01

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

  20. In vivo ultrasound and biometric measurements predict the empty body chemical composition in Nellore cattle.

    Science.gov (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

    2018-05-04

    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

  1. A Critique of a Phenomenological Fiber Breakage Model for Stress Rupture of Composite Materials

    Science.gov (United States)

    Reeder, James R.

    2010-01-01

    Stress rupture is not a critical failure mode for most composite structures, but there are a few applications where it can be critical. One application where stress rupture can be a critical design issue is in Composite Overwrapped Pressure Vessels (COPV's), where the composite material is highly and uniformly loaded for long periods of time and where very high reliability is required. COPV's are normally required to be proof loaded before being put into service to insure strength, but it is feared that the proof load may cause damage that reduces the stress rupture reliability. Recently, a fiber breakage model was proposed specifically to estimate a reduced reliability due to proof loading. The fiber breakage model attempts to model physics believed to occur at the microscopic scale, but validation of the model has not occurred. In this paper, the fiber breakage model is re-derived while highlighting assumptions that were made during the derivation. Some of the assumptions are examined to assess their effect on the final predicted reliability.

  2. Inverse and Predictive Modeling

    Energy Technology Data Exchange (ETDEWEB)

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

    2017-09-27

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

  3. Archaeological predictive model set.

    Science.gov (United States)

    2015-03-01

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

  4. Prediction of strength of wood composite materials using ultrasonic

    International Nuclear Information System (INIS)

    Mahmoud, M.K.; Emam, A.

    2005-01-01

    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

  5. 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....

  6. Mathematical Model For Autoclave Curing Of Unsaturated Polyester Based Composite Materials

    Directory of Open Access Journals (Sweden)

    Adnan A. Abdul Razak

    2013-05-01

    Full Text Available Heat transfer process involved in the autoclave curing of fiber-reinforced thermosetting composites is investigated numerically. A model for the prediction of the temperature and the extent of the reaction across the laminate thickness during curing process in the autoclave of unsaturated polyester based composite has been developed. The governing equation for one dimensional heat transfer, and accounting for the heat generation due to the exothermic cure reaction in the composites had been used.  It was found that the temperature at the central of the laminate increases up to the external imposed temperature, because of the thermal conductivity of the resin and fiber. The heat generated by the exothermic reaction of the resin is not adequately removed; the increase in the temperature at the center increases the resins rate reaction, which in turn generates more heat.

  7. 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)

    2016-07-13

    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.

  8. Effective longitudinal strength of high temperature metal-matrix composites

    International Nuclear Information System (INIS)

    Craddock, J.N.; Savvides, I.

    1991-01-01

    Several models for predicting the longitudinal strength of fiber composites are presented, ranging from a simple netting analysis to a model incorporating curvilinear strain hardening for all the components. Results from these models are presented for tungsten fiber reinforced superalloys, FeCrAlY and MARM200. It is shown that a simple elastic limit micromechanical model does not always adequately describe the useful strength of the composites. The methods proposed here are shown to be more appropriate for predicting the effective composite strength. 2 refs

  9. Prediction of bakery products nutritive value based on mathematical modeling of biochemical reactions

    Directory of Open Access Journals (Sweden)

    E. I. Ponomareva

    2013-01-01

    Full Text Available Researches are devoted to identifying changes in the chemical composition of whole-grain wheat bread during baking and to forecasting of food value of bakery products by mathematical modeling of biochemical transformations. The received model represents the invariant composition, considering speed of biochemical reactions at a batch of bakery products, and allowing conduct virtual experiments to develop new types of bread for various categories of the population, including athletes. The offered way of modeling of biochemical transformations at a stage of heat treatment allows to predict food value of bakery products, without spending funds for raw materials and large volume of experiment that will provide possibility of economy of material resources at a stage of development of new types of bakery products and possibility of production efficiency increase.

  10. 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: hendrick@ufpa.br; yuuitai@ufpa.br; mfmn@ufpa.br; sbrito@ufpa.br; brigida@ufpa.br

    2008-07-01

    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)

  11. A Large-scale Finite Element Model on Micromechanical Damage and Failure of Carbon Fiber/Epoxy Composites Including Thermal Residual Stress

    Science.gov (United States)

    Liu, P. F.; Li, X. K.

    2018-06-01

    The purpose of this paper is to study micromechanical progressive failure properties of carbon fiber/epoxy composites with thermal residual stress by finite element analysis (FEA). Composite microstructures with hexagonal fiber distribution are used for the representative volume element (RVE), where an initial fiber breakage is assumed. Fiber breakage with random fiber strength is predicted using Monte Carlo simulation, progressive matrix damage is predicted by proposing a continuum damage mechanics model and interface failure is simulated using Xu and Needleman's cohesive model. Temperature dependent thermal expansion coefficients for epoxy matrix are used. FEA by developing numerical codes using ANSYS finite element software is divided into two steps: 1. Thermal residual stresses due to mismatch between fiber and matrix are calculated; 2. Longitudinal tensile load is further exerted on the RVE to perform progressive failure analysis of carbon fiber/epoxy composites. Numerical convergence is solved by introducing the viscous damping effect properly. The extended Mori-Tanaka method that considers interface debonding is used to get homogenized mechanical responses of composites. Three main results by FEA are obtained: 1. the real-time matrix cracking, fiber breakage and interface debonding with increasing tensile strain is simulated. 2. the stress concentration coefficients on neighbouring fibers near the initial broken fiber and the axial fiber stress distribution along the broken fiber are predicted, compared with the results using the global and local load-sharing models based on the shear-lag theory. 3. the tensile strength of composite by FEA is compared with those by the shear-lag theory and experiments. Finally, the tensile stress-strain curve of composites by FEA is applied to the progressive failure analysis of composite pressure vessel.

  12. Combinatory Models for Predicting the Effective Thermal Conductivity of Frozen and Unfrozen Food Materials

    OpenAIRE

    K. S. Reddy; P Karthikeyan

    2010-01-01

    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...

  13. Models for Strength Prediction of High-Porosity Cast-In-Situ Foamed Concrete

    Directory of Open Access Journals (Sweden)

    Wenhui Zhao

    2018-01-01

    Full Text Available A study was undertaken to develop a prediction model of compressive strength for three types of high-porosity cast-in-situ foamed concrete (cement mix, cement-fly ash mix, and cement-sand mix with dry densities of less than 700 kg/m3. The model is an extension of Balshin’s model and takes into account the hydration ratio of the raw materials, in which the water/cement ratio was a constant for the entire construction period for a certain casting density. The results show that the measured porosity is slightly lower than the theoretical porosity due to few inaccessible pores. The compressive strength increases exponentially with the increase in the ratio of the dry density to the solid density and increases with the curing time following the composite function A2ln⁡tB2 for all three types of foamed concrete. Based on the results that the compressive strength changes with the porosity and the curing time, a prediction model taking into account the mix constitution, curing time, and porosity is developed. A simple prediction model is put forward when no experimental data are available.

  14. Composite quantum collision models

    Science.gov (United States)

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

    2017-09-01

    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.

  15. Re-parametrization of a swine model to predict growth performance of broilers

    OpenAIRE

    Dukhta, G.; van Milgen, Jacob; Kövér, G.; Halas, V.

    2017-01-01

    The aim of the study was to investigate whether a pig growth model is suitable to be modified and adapted for broilers. As monogastric animals, pigs and poultry share many similarities in their digestion and metabolism, many structures (body protein and lipid stores) and the nutrient flows of the underlying metabolic pathways are similar among species. For that purpose, the InraPorc model was used as a basis to predict growth performance and body composition at slaughter in broilers. First...

  16. Confidence scores for prediction models

    DEFF Research Database (Denmark)

    Gerds, Thomas Alexander; van de Wiel, MA

    2011-01-01

    In medical statistics, many alternative strategies are available for building a prediction model based on training data. Prediction models are routinely compared by means of their prediction performance in independent validation data. If only one data set is available for training and validation,...

  17. Prediction of composites behavior undergoing an ATP process through data-mining

    Science.gov (United States)

    Martin, Clara Argerich; Collado, Angel Leon; Pinillo, Rubén Ibañez; Barasinski, Anaïs; Abisset-Chavanne, Emmanuelle; Chinesta, Francisco

    2018-05-01

    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.

  18. A constitutive model for particulate-reinforced titanium matrix composites subjected to high strain rates and high temperatures

    Directory of Open Access Journals (Sweden)

    Song Wei-Dong

    2013-01-01

    Full Text Available Quasi-static and dynamic tension tests were conducted to study the mechanical properties of particulate-reinforced titanium matrix composites at strain rates ranging from 0.0001/s to 1000/s and at temperatures ranging from 20 °C to 650 °C Based on the experimental results, a constitutive model, which considers the effects of strain rate and temperature on hot deformation behavior, was proposed for particulate-reinforced titanium matrix composites subjected to high strain rates and high temperatures by using Zener-Hollomon equations including Arrhenius terms. All the material constants used in the model were identified by fitting Zener-Hollomon equations against the experimental results. By comparison of theoretical predictions presented by the model with experimental results, a good agreement was achieved, which indicates that this constitutive model can give an accurate and precise estimate for high temperature flow stress for the studied titanium matrix composites and can be used for numerical simulations of hot deformation behavior of the composites.

  19. Creep of plain weave polymer matrix composites

    Science.gov (United States)

    Gupta, Abhishek

    Polymer matrix composites are increasingly used in various industrial sectors to reduce structural weight and improve performance. Woven (also known as textile) composites are one class of polymer matrix composites with increasing market share mostly due to their lightweight, their flexibility to form into desired shape, their mechanical properties and toughness. Due to the viscoelasticity of the polymer matrix, time-dependent degradation in modulus (creep) and strength (creep rupture) are two of the major mechanical properties required by engineers to design a structure reliably when using these materials. Unfortunately, creep and creep rupture of woven composites have received little attention by the research community and thus, there is a dire need to generate additional knowledge and prediction models, given the increasing market share of woven composites in load bearing structural applications. Currently, available creep models are limited in scope and have not been validated for any loading orientation and time period beyond the experimental time window. In this thesis, an analytical creep model, namely the Modified Equivalent Laminate Model (MELM), was developed to predict tensile creep of plain weave composites for any orientation of the load with respect to the orientation of the fill and warp fibers, using creep of unidirectional composites. The ability of the model to predict creep for any orientation of the load is a "first" in this area. The model was validated using an extensive experimental involving the tensile creep of plain weave composites under varying loading orientation and service conditions. Plain weave epoxy (F263)/ carbon fiber (T300) composite, currently used in aerospace applications, was procured as fabrics from Hexcel Corporation. Creep tests were conducted under two loading conditions: on-axis loading (0°) and off-axis loading (45°). Constant load creep, in the temperature range of 80-240°C and stress range of 1-70% UTS of the

  20. Sound transmission through lined, composite panel structures: Transversely isotropic poro-elastic model

    Science.gov (United States)

    Kim, Jeong-Woo

    A joint experimental and analytical investigation of the sound transmission loss (STL) and two-dimensional free wave propagation in composite sandwich panels is presented here. An existing panel, a Nomex honeycomb sandwich panel, was studied in detail. For the purpose of understanding the typical behavior of sandwich panels, a composite structure comprising two aluminum sheets with a relatively soft, poro-elastic foam core was also constructed and studied. The cores of both panels were modeled using an anisotropic (transversely isotropic) poro-elastic material theory. Several estimation methods were used to obtain the material properties of the honeycomb core and the skin plates to be used in the numerical calculations. Appropriate values selected from among the estimates were used in the STL and free wave propagation models. The prediction model was then verified in two ways: first, the calculated wave speeds and STL of a single poro-elastic layer were numerically verified by comparison with the predictions of a previously developed isotropic model. Secondly, to physically validate the transversely isotropic model, the measured STL and the phase speeds of the sandwich panels were compared with their predicted values. To analyze the actual treatment of a fuselage structure, multi-layered configurations, including a honeycomb panel and several layers such as air gaps, acoustic blankets and membrane partitions, were formulated. Then, to find the optimal solution for improving the sound barrier performance of an actual fuselage system, air layer depth and glass fiber lining effects were investigated by using these multi-layer models. By using the free wave propagation model, the first anti-symmetric and symmetric modes of the sandwich panels were characterized to allow the identification of the coincidence frequencies of the sandwich panel. The behavior of the STL could then be clearly explained by comparison with the free wave propagation solutions. By performing a

  1. Prediction of foal carcass composition and wholesale cut yields by using video image analysis.

    Science.gov (United States)

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

    2018-01-01

    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.

  2. Finite element modelling and analysis of composites toecaps

    International Nuclear Information System (INIS)

    Yang, C C; Duhovic, M; Lin, R J T; Bhattacharyya, D

    2009-01-01

    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.

  3. Prediction on flexural strength of encased composite beam with cold-formed steel section

    Science.gov (United States)

    Khadavi, Tahir, M. M.

    2017-11-01

    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.

  4. 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: jhshim@kist.re.kr [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)

    2015-07-15

    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.

  5. Maximally Symmetric Composite Higgs Models.

    Science.gov (United States)

    Csáki, Csaba; Ma, Teng; Shu, Jing

    2017-09-29

    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.

  6. Anisotropic composite human skull model and skull fracture validation against temporo-parietal skull fracture.

    Science.gov (United States)

    Sahoo, Debasis; Deck, Caroline; Yoganandan, Narayan; Willinger, Rémy

    2013-12-01

    A composite material model for skull, taking into account damage is implemented in the Strasbourg University finite element head model (SUFEHM) in order to enhance the existing skull mechanical constitutive law. The skull behavior is validated in terms of fracture patterns and contact forces by reconstructing 15 experimental cases. The new SUFEHM skull model is capable of reproducing skull fracture precisely. The composite skull model is validated not only for maximum forces, but also for lateral impact against actual force time curves from PMHS for the first time. Skull strain energy is found to be a pertinent parameter to predict the skull fracture and based on statistical (binary logistical regression) analysis it is observed that 50% risk of skull fracture occurred at skull strain energy of 544.0mJ. © 2013 Elsevier Ltd. All rights reserved.

  7. Mathematical methods and models in composites

    CERN Document Server

    Mantic, Vladislav

    2014-01-01

    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

  8. Predicting relative species composition within mixed conifer forest pixels using zero‐inflated models and Landsat imagery

    Science.gov (United States)

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

    2015-01-01

    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),...

  9. 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

    2012-09-01

    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.

  10. The Phyre2 web portal for protein modeling, prediction and analysis.

    Science.gov (United States)

    Kelley, Lawrence A; Mezulis, Stefans; Yates, Christopher M; Wass, Mark N; Sternberg, Michael J E

    2015-06-01

    Phyre2 is a suite of tools available on the web to predict and analyze protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools. Phyre2 replaces Phyre, the original version of the server for which we previously published a paper in Nature Protocols. In this updated protocol, we describe Phyre2, which uses advanced remote homology detection methods to build 3D models, predict ligand binding sites and analyze the effect of amino acid variants (e.g., nonsynonymous SNPs (nsSNPs)) for a user's protein sequence. Users are guided through results by a simple interface at a level of detail they determine. This protocol will guide users from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality. A range of additional available tools is described to find a protein structure in a genome, to submit large number of sequences at once and to automatically run weekly searches for proteins that are difficult to model. The server is available at http://www.sbg.bio.ic.ac.uk/phyre2. A typical structure prediction will be returned between 30 min and 2 h after submission.

  11. Development of an Integrated Moisture Index for predicting species composition

    Science.gov (United States)

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

    1996-01-01

    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...

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

    Directory of Open Access Journals (Sweden)

    V. S. Sai

    2013-01-01

    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.

  13. A model of synovial fluid lubricant composition in normal and injured joints

    Directory of Open Access Journals (Sweden)

    M E Blewis

    2007-03-01

    Full Text Available The synovial fluid (SF of joints normally functions as a biological lubricant, providing low-friction and low-wear properties to articulating cartilage surfaces through the putative contributions of proteoglycan 4 (PRG4, hyaluronic acid (HA, and surface active phospholipids (SAPL. These lubricants are secreted by chondrocytes in articular cartilage and synoviocytes in synovium, and concentrated in the synovial space by the semi-permeable synovial lining. A deficiency in this lubricating system may contribute to the erosion of articulating cartilage surfaces in conditions of arthritis. A quantitative intercompartmental model was developed to predict in vivo SF lubricant concentration in the human knee joint. The model consists of a SF compartment that (a is lined by cells of appropriate types, (b is bound by a semi-permeable membrane, and (c contains factors that regulate lubricant secretion. Lubricant concentration was predicted with different chemical regulators of chondrocyte and synoviocyte secretion, and also with therapeutic interventions of joint lavage and HA injection. The model predicted steady-state lubricant concentrations that were within physiologically observed ranges, and which were markedly altered with chemical regulation. The model also predicted that when starting from a zero lubricant concentration after joint lavage, PRG4 reaches steady-state concentration ~10-40 times faster than HA. Additionally, analysis of the clearance rate of HA after therapeutic injection into SF predicted that the majority of HA leaves the joint after ~1-2 days. This quantitative intercompartmental model allows integration of biophysical processes to identify both environmental factors and clinical therapies that affect SF lubricant composition in whole joints.

  14. Foundations of compositional model theory

    Czech Academy of Sciences Publication Activity Database

    Jiroušek, Radim

    2011-01-01

    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 http://library.utia.cas.cz/separaty/2011/MTR/jirousek-foundations of compositional model theory.pdf

  15. Accurate and dynamic predictive model for better prediction in medicine and healthcare.

    Science.gov (United States)

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

    2018-05-01

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

  16. Unit-Sphere Multiaxial Stochastic-Strength Model Applied to Anisotropic and Composite Materials

    Science.gov (United States)

    Nemeth, Noel, N.

    2013-01-01

    Models that predict the failure probability of brittle materials under multiaxial loading have been developed by authors such as Batdorf, Evans, and Matsuo. These "unit-sphere" models assume that the strength-controlling flaws are randomly oriented, noninteracting planar microcracks of specified geometry but of variable size. This methodology has been extended to predict the multiaxial strength response of transversely isotropic brittle materials, including polymer matrix composites (PMCs), by considering (1) flaw-orientation anisotropy, whereby a preexisting microcrack has a higher likelihood of being oriented in one direction over another direction, and (2) critical strength, or K (sub Ic) orientation anisotropy, whereby the level of critical strength or fracture toughness for mode I crack propagation, K (sub Ic), changes with regard to the orientation of the microstructure. In this report, results from finite element analysis of a fiber-reinforced-matrix unit cell were used with the unit-sphere model to predict the biaxial strength response of a unidirectional PMC previously reported from the World-Wide Failure Exercise. Results for nuclear-grade graphite materials under biaxial loading are also shown for comparison. This effort was successful in predicting the multiaxial strength response for the chosen problems. Findings regarding stress-state interactions and failure modes also are provided.

  17. Modeling and additive manufacturing of bio-inspired composites with tunable fracture mechanical properties.

    Science.gov (United States)

    Dimas, Leon S; Buehler, Markus J

    2014-07-07

    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.

  18. Modeling Non-Linear Material Properties in Composite Materials

    Science.gov (United States)

    2016-06-28

    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 ...systems are increasingly incorporating composite materials into their design. Many of these systems subject the composites to environmental conditions

  19. Equivalent circuit method research of resonant magnetoelectric characteristic in magnetoelectric laminate composites using nonlinear magnetostrictive constitutive model

    International Nuclear Information System (INIS)

    Zhou, Hao-Miao; Li, Chao; Xuan, Li-Ming; Zhao, Ji-Xiang; Wei, Jing

    2011-01-01

    This paper analyzes the magnetoelectric (ME) response around the resonance frequency in the magnetostrictive/piezoelectric/magnetostrictive (MPM) magnetoelectric laminate composites. Following the equivalent circuit method and considering the mechanical loss, we select the nonlinear magnetostrictive constitutive model to present a novel explicit nonlinear expression for the resonant magnetoelectric (ME) coefficient of the magnetoelectric laminate composites. Compared with the experimental results, the predicted resonant ME coefficient of the explicit expression shows a good agreement both qualitatively and quantitatively. Also, when the electromechanical coupling factor of the piezoelectric material, k 31 p , is small, this explicit expression can be reduced to the existing model. On this basis, this paper considers and predicts the magnetoelectric conversion characteristics of the magnetoelectric laminate composites, calculates and analyzes the influences of the thickness ratio of magnetostrictive layer and piezoelectric material, bias magnetic field, and saturation magnetostrictive coefficient on the resonant ME coefficient. This research can provide a theoretical basis for the preparation of magnetoelectric devices with good magnetoelectric conversion characteristics, such as magnetoelectric sensors, energy harvesting transducers, microwave devices etc

  20. Modeling the Effects of the Cathode Composition of a Lithium Iron Phosphate Battery on the Discharge Behavior

    Directory of Open Access Journals (Sweden)

    Won Il Cho

    2013-10-01

    Full Text Available This paper reports a modeling methodology to predict the effects on the discharge behavior of the cathode composition of a lithium iron phosphate (LFP battery cell comprising a LFP cathode, a lithium metal anode, and an organic electrolyte. A one-dimensional model based on a finite element method is presented to calculate the cell voltage change of a LFP battery cell during galvanostatic discharge. To test the validity of the modeling approach, the modeling results for the variations of the cell voltage of the LFP battery as a function of time are compared with the experimental measurements during galvanostatic discharge at various discharge rates of 0.1C, 0.5C, 1.0C, and 2.0C for three different compositions of the LFP cathode. The discharge curves obtained from the model are in good agreement with the experimental measurements. On the basis of the validated modeling approach, the effects of the cathode composition on the discharge behavior of a LFP battery cell are estimated. The modeling results exhibit highly nonlinear dependencies of the discharge behavior of a LFP battery cell on the discharge C-rate and cathode composition.

  1. 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.

    2005-01-01

    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

  2. Supersymmetric composite models on intersecting D-branes

    International Nuclear Information System (INIS)

    Kitazawa, Noriaki

    2004-01-01

    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'

  3. Multi-model analysis in hydrological prediction

    Science.gov (United States)

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

    2017-12-01

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

  4. Work-principle model for predicting toxic fumes of nonideal explosives

    Energy Technology Data Exchange (ETDEWEB)

    Wieland, Michael S. [National Institute of Occupational Safety and Health, Pittsburgh Research Center, P.O. Box 18070, Pittsburgh, PA 15236-0070 (United States)

    2004-08-01

    The work-principle from thermodynamics was used to formulate a model for predicting toxic fumes from mining explosives in underground chamber tests, where rapid turbulent combustion within the surrounding air noticeably changes the resulting concentrations. Two model constants were required to help characterize the reaction zone undergoing rapid chemical transformations in conjunction with heat transfer and work output: a stoichiometry mixing fraction and a reaction-quenching temperature. Rudimentary theory with an unsteady uniform concentration gradient was taken to characterize the combustion zone, yielding 75% for the mixing fraction. Four quenching temperature trends were resolved and compared to test results of ammonium nitrate compositions with different fuel-oil percentages (ANFO). The quenching temperature 2345 K was the optimum choice for fitting the two major components of fume toxicity: carbon monoxide (CO) and total nitrogen oxides (NO{sub X}). The resulting two-constant model was used to generate comparisons for test results of ANFO compositions with additives. Though respectable fits were usually found, charge formulations which reacted weakly could not be resolved numerically. The work-principle model yields toxic concentrations for a range of charge formulations, making it a useful tool for investigating the potential hazard of released fumes and reducing the risk of unwanted incidents. (Abstract Copyright [2004], Wiley Periodicals, Inc.)

  5. Flexural creep behaviour of jute polypropylene composites

    Science.gov (United States)

    Chandekar, Harichandra; Chaudhari, Vikas

    2016-09-01

    Present study is about the flexural creep behaviour of jute fabric reinforced polypropylene (Jute-PP) composites. The PP sheet and alkali treated jute fabric is stacked alternately and hot pressed in compression molding machine to get Jute-PP composite laminate. The flexural creep study is carried out on dynamic mechanical analyzer. The creep behaviour of the composite is modeled using four-parameter Burgers model. Short-term accelerated creep testing is conducted which is later used to predict long term creep behaviour. The feasibility of the construction of a master curve using the time-temperature superposition (TTS) principle to predict long term creep behavior of unreinforced PP and Jute-PP composite is investigated.

  6. Composite inflation confronts BICEP2 and PLANCK

    International Nuclear Information System (INIS)

    Karwan, Khamphee; Channuie, Phongpichit

    2014-01-01

    We examine observational constraints on single-field inflation in which the inflaton is a composite field stemming from a four-dimensional strongly interacting field theory. We confront the predictions with the Planck and very recent BICEP2 data. In the large non-minimal coupling regions, we discover for the minimal composite inflationary model that the predictions lie well inside the joint 68% CL for the Planck data, but is in tension with the recent BICEP2 observations. In the case of the glueball inflationary model, the predictions satisfy the Planck results. However, this model can produce a large tensor-to-scalar ratio consistent with the recent BICEP2 observations if the number of e-foldings is slightly smaller than the range commonly used. For a super Yang-Mills paradigm, we discover that the predictions satisfy the Planck data, and surprisingly a large tensor-to-scalar ratio consistent with the BICEP2 results can also be produced for an acceptable range of the number of e-foldings and of the confining scale. In the small non-minimal coupling regions, all of the models can satisfy the BICEP2 results. However, the predictions of the glueball and superglueball inflationary models cannot satisfy the observational bound on the amplitude of the curvature perturbation launched by Planck, and the techni-inflaton self-coupling in the minimal composite inflationary model is constrained to be extremely small

  7. Composition models for the viscosity and chemical durability of West Valley related nuclear waste glasses

    International Nuclear Information System (INIS)

    Feng, X.; Saad, E.E.; Freeborn, W.P.; Macedo, P.B.; Pegg, I.L.; Sassoon, R.E.; Barkatt, A.; Finger, S.M.

    1988-01-01

    There are two important criteria that must be satisfied by a nuclear waste glass durability and processability. The chemical composition of the glass must be such that it does not dissolve or erode appreciably faster than the decay of the radioactive materials embedded in it. The second criterion, processability, means that the glass must melt with ease, must be easily pourable, and must not crystallize appreciably. This paper summarizes the development of simple models for predicting the durability and viscosity of nuclear waste glasses from their composition

  8. Versatile Micromechanics Model for Multiscale Analysis of Composite Structures

    Science.gov (United States)

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

    2013-08-01

    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.

  9. Extreme events and predictability of catastrophic failure in composite materials and in the Earth

    Science.gov (United States)

    Main, I.; Naylor, M.

    2012-05-01

    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.

  10. A Simplified Micromechanical Modeling Approach to Predict the Tensile Flow Curve Behavior of Dual-Phase Steels

    Science.gov (United States)

    Nanda, Tarun; Kumar, B. Ravi; Singh, Vishal

    2017-11-01

    Micromechanical modeling is used to predict material's tensile flow curve behavior based on microstructural characteristics. This research develops a simplified micromechanical modeling approach for predicting flow curve behavior of dual-phase steels. The existing literature reports on two broad approaches for determining tensile flow curve of these steels. The modeling approach developed in this work attempts to overcome specific limitations of the existing two approaches. This approach combines dislocation-based strain-hardening method with rule of mixtures. In the first step of modeling, `dislocation-based strain-hardening method' was employed to predict tensile behavior of individual phases of ferrite and martensite. In the second step, the individual flow curves were combined using `rule of mixtures,' to obtain the composite dual-phase flow behavior. To check accuracy of proposed model, four distinct dual-phase microstructures comprising of different ferrite grain size, martensite fraction, and carbon content in martensite were processed by annealing experiments. The true stress-strain curves for various microstructures were predicted with the newly developed micromechanical model. The results of micromechanical model matched closely with those of actual tensile tests. Thus, this micromechanical modeling approach can be used to predict and optimize the tensile flow behavior of dual-phase steels.

  11. Nonlinear finite element modeling of concrete deep beams with openings strengthened with externally-bonded composites

    International Nuclear Information System (INIS)

    Hawileh, Rami A.; El-Maaddawy, Tamer A.; Naser, Mohannad Z.

    2012-01-01

    Highlights: ► A 3D nonlinear FE model is developed of RC deep beams with web openings. ► We used cohesion elements to simulate bond. ► The developed FE model is suitable for analysis of such complex structures. -- Abstract: This paper aims to develop 3D nonlinear finite element (FE) models for reinforced concrete (RC) deep beams containing web openings and strengthened in shear with carbon fiber reinforced polymer (CFRP) composite sheets. The web openings interrupted the natural load path either fully or partially. The FE models adopted realistic materials constitutive laws that account for the nonlinear behavior of materials. In the FE models, solid elements for concrete, multi-layer shell elements for CFRP and link elements for steel reinforcement were used to simulate the physical models. Special interface elements were implemented in the FE models to simulate the interfacial bond behavior between the concrete and CFRP composites. A comparison between the FE results and experimental data published in the literature demonstrated the validity of the computational models in capturing the structural response for both unstrengthened and CFRP-strengthened deep beams with openings. The developed FE models can serve as a numerical platform for performance prediction of RC deep beams with openings strengthened in shear with CFRP composites.

  12. Load sharing in tungsten fiber reinforced Kanthal composites

    International Nuclear Information System (INIS)

    Clausen, B.; Bourke, Mark A.M.; Brown, Donald W.; Ustuendag, E.

    2006-01-01

    The load sharing in three tungsten fiber reinforced Kanthal matrix composites (with fiber volume fractions of 10, 20 and 30%) have been determined using in situ neutron diffraction measurements. The expected iso-strain region was limited in the 20 and 30% composites due to thermal residual stresses. The experimental data have been used to validate the predictions of a unit-cell finite element model. The model was able to accurately predict the measured in situ loading data for all three composites using the same material properties for all calculations

  13. Load sharing in tungsten fiber reinforced Kanthal composites

    Energy Technology Data Exchange (ETDEWEB)

    Clausen, B. [Los Alamos National Laboratory, LANSCE-12, P.O. Box 1663, MS H805, Los Alamos, NM 87545 (United States)]. E-mail: clausen@lanl.gov; Bourke, Mark A.M. [Los Alamos National Laboratory, MST-8, P.O. Box 1663, MS H805, Los Alamos, NM 87545 (United States); Brown, Donald W. [Los Alamos National Laboratory, MST-8, P.O. Box 1663, MS H805, Los Alamos, NM 87545 (United States); Ustuendag, E. [California Institute of Technology, Keck Laboratory, M/C 138-78, 1200 E. California Blvd., Pasadena, CA 91125 (United States)

    2006-04-15

    The load sharing in three tungsten fiber reinforced Kanthal matrix composites (with fiber volume fractions of 10, 20 and 30%) have been determined using in situ neutron diffraction measurements. The expected iso-strain region was limited in the 20 and 30% composites due to thermal residual stresses. The experimental data have been used to validate the predictions of a unit-cell finite element model. The model was able to accurately predict the measured in situ loading data for all three composites using the same material properties for all calculations.

  14. Rapid analysis of composition and reactivity in cellulosic biomass feedstocks with near-infrared spectroscopy.

    Science.gov (United States)

    Payne, Courtney E; Wolfrum, Edward J

    2015-01-01

    Obtaining accurate chemical composition and reactivity (measures of carbohydrate release and yield) information for biomass feedstocks in a timely manner is necessary for the commercialization of biofuels. Our objective was to use near-infrared (NIR) spectroscopy and partial least squares (PLS) multivariate analysis to develop calibration models to predict the feedstock composition and the release and yield of soluble carbohydrates generated by a bench-scale dilute acid pretreatment and enzymatic hydrolysis assay. Major feedstocks included in the calibration models are corn stover, sorghum, switchgrass, perennial cool season grasses, rice straw, and miscanthus. We present individual model statistics to demonstrate model performance and validation samples to more accurately measure predictive quality of the models. The PLS-2 model for composition predicts glucan, xylan, lignin, and ash (wt%) with uncertainties similar to primary measurement methods. A PLS-2 model was developed to predict glucose and xylose release following pretreatment and enzymatic hydrolysis. An additional PLS-2 model was developed to predict glucan and xylan yield. PLS-1 models were developed to predict the sum of glucose/glucan and xylose/xylan for release and yield (grams per gram). The release and yield models have higher uncertainties than the primary methods used to develop the models. It is possible to build effective multispecies feedstock models for composition, as well as carbohydrate release and yield. The model for composition is useful for predicting glucan, xylan, lignin, and ash with good uncertainties. The release and yield models have higher uncertainties; however, these models are useful for rapidly screening sample populations to identify unusual samples.

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

    Directory of Open Access Journals (Sweden)

    Mohammad Ramezani

    2015-07-01

    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.

  16. Composite Linear Models | Division of Cancer Prevention

    Science.gov (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

  17. Behaviour of glued fibre composite sandwich structure in flexure: Experiment and Fibre Model Analysis

    International Nuclear Information System (INIS)

    Manalo, Allan; Aravinthan, Thiru

    2012-01-01

    Highlights: ► Fibre Model Analysis is used to examine the flexural behaviour of sandwich beams. ► Theoretical prediction using FMA is in good agreement with the experiment. ► Using the constituent materials in FMA predicted accurately the beam’s behaviour. ► FMA can be used for analysing sandwich beams with high-strength core in flexure. -- Abstract: The behaviour of glued composite sandwich beams in flexure was investigated with a view of using this material for structural and civil engineering applications. The building block of this glue-laminated beam is a new generation composite sandwich structure made up of glass fibre reinforced polymer skins and a high strength phenolic core material. A simplified Fibre Model Analysis (FMA) usually used to analyse a concrete beam section is adopted to theoretically describe the flexural behaviour of the innovative sandwich beam structure. The analysis included the flexural behaviour of the glued sandwich beams in the flatwise and the edgewise positions. The FMA accounted for the non-linear behaviour of the phenolic core in compression, the cracking of the core in tension and the linear elastic behaviour of the fibre composite skin. The results of the FMA showed a good agreement with the experimental data showing the efficiency and practical applications of the simplified FMA in analysing and designing sandwich structures with high strength core material.

  18. 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.

    1989-01-01

    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%)

  19. Direct and indirect signals of natural composite Higgs models

    Science.gov (United States)

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

    2016-01-01

    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.

  20. Modelling techniques for predicting the long term consequences of radiation on natural aquatic populations

    International Nuclear Information System (INIS)

    Wallis, I.G.

    1978-01-01

    The purpose of this working paper is to describe modelling techniques for predicting the long term consequences of radiation on natural aquatic populations. Ideally, it would be possible to use aquatic population models: (1) to predict changes in the health and well-being of all aquatic populations as a result of changing the composition, amount and location of radionuclide discharges; (2) to compare the effects of steady, fluctuating and accidental releases of radionuclides; and (3) to evaluate the combined impact of the discharge of radionuclides and other wastes, and natural environmental stresses on aquatic populations. At the onset it should be stated that there is no existing model which can achieve this ideal performance. However, modelling skills and techniques are available to develop useful aquatic population models. This paper discusses the considerations involved in developing these models and briefly describes the various types of population models which have been developed to date

  1. Surface tensions of multi-component mixed inorganic/organic aqueous systems of atmospheric significance: measurements, model predictions and importance for cloud activation predictions

    Directory of Open Access Journals (Sweden)

    D. O. Topping

    2007-01-01

    Full Text Available In order to predict the physical properties of aerosol particles, it is necessary to adequately capture the behaviour of the ubiquitous complex organic components. One of the key properties which may affect this behaviour is the contribution of the organic components to the surface tension of aqueous particles in the moist atmosphere. Whilst the qualitative effect of organic compounds on solution surface tensions has been widely reported, our quantitative understanding on mixed organic and mixed inorganic/organic systems is limited. Furthermore, it is unclear whether models that exist in the literature can reproduce the surface tension variability for binary and higher order multi-component organic and mixed inorganic/organic systems of atmospheric significance. The current study aims to resolve both issues to some extent. Surface tensions of single and multiple solute aqueous solutions were measured and compared with predictions from a number of model treatments. On comparison with binary organic systems, two predictive models found in the literature provided a range of values resulting from sensitivity to calculations of pure component surface tensions. Results indicate that a fitted model can capture the variability of the measured data very well, producing the lowest average percentage deviation for all compounds studied. The performance of the other models varies with compound and choice of model parameters. The behaviour of ternary mixed inorganic/organic systems was unreliably captured by using a predictive scheme and this was dependent on the composition of the solutes present. For more atmospherically representative higher order systems, entirely predictive schemes performed poorly. It was found that use of the binary data in a relatively simple mixing rule, or modification of an existing thermodynamic model with parameters derived from binary data, was able to accurately capture the surface tension variation with concentration. Thus

  2. Predicting the tensile strength of A UD basalt/ epoxy composite used for the confinement of concrete structures

    Science.gov (United States)

    Ciniņa, I.; Zīle, O.; Andersons, J.

    2013-01-01

    The principal aim of the present research was to predict the strength of UD basalt fiber/epoxy matrix composites in tension along the reinforcement direction. Tension tests on single basalt fibers were performed to determine the functional form of their strength distribution and to evaluate the parameters of the distribution. Also, microbond tests were carried out to assess the interfacial shear strength of the fibers and polymer matrix. UD composite specimens were produced and tested for the longitudinal tensile strength. The predicted strength of the composite was found to exceed the experimental values by ca. 20%, which can be explained by imperfections in the fiber alignment, impregnation, and adhesion in the composite specimens.

  3. Anchorage strength models for end-debonding predictions in RC beams strengthened with FRP composites

    Science.gov (United States)

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

    2008-05-01

    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.

  4. The Composite OLAP-Object Data Model

    Energy Technology Data Exchange (ETDEWEB)

    Pourabbas, Elaheh; Shoshani, Arie

    2005-12-07

    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.

  5. REALIGNED MODEL PREDICTIVE CONTROL OF A PROPYLENE DISTILLATION COLUMN

    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.

  6. Evaluation of a new CNRM-CM6 model version for seasonal climate predictions

    Science.gov (United States)

    Volpi, Danila; Ardilouze, Constantin; Batté, Lauriane; Dorel, Laurant; Guérémy, Jean-François; Déqué, Michel

    2017-04-01

    This work presents the quality assessment of a new version of the Météo-France coupled climate prediction system, which has been developed in the EU COPERNICUS Climate Change Services framework to carry out seasonal forecast. The system is based on the CNRM-CM6 model, with Arpege-Surfex 6.2.2 as atmosphere/land component and Nemo 3.2 as ocean component, which has directly embedded the sea-ice component Gelato 6.0. In order to have a robust diagnostic, the experiment is composed by 60 ensemble members generated with stochastic dynamic perturbations. The experiment has been performed over a 37-year re-forecast period from 1979 to 2015, with two start dates per year, respectively in May 1st and November 1st. The evaluation of the predictive skill of the model is shown under two perspectives: on the one hand, the ability of the model to faithfully respond to positive or negative ENSO, NAO and QBO events, independently of the predictability of these events. Such assessment is carried out through a composite analysis, and shows that the model succeeds in reproducing the main patterns for 2-meter temperature, precipitation and geopotential height at 500 hPa during the winter season. On the other hand, the model predictive skill of the same events (positive and negative ENSO, NAO and QBO) is evaluated.

  7. Analysis of composite hydrogen storage cylinders subjected to localized flame impingements

    Energy Technology Data Exchange (ETDEWEB)

    Hu, J.; Chen, J.; Sundararaman, S.; Chandrashekhara, K. [Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, MO 65409 (United States); Chernicoff, William [US Department of Transportation, Washington, DC 20509 (United States)

    2008-06-15

    A comprehensive non-linear finite element model is developed for predicting the behavior of composite hydrogen storage cylinders subjected to high pressure and localized flame impingements. The model is formulated in an axi-symmetric coordinate system and incorporates with various sub-models to describe the behavior of the composite cylinder under extreme thermo-mechanical loadings. A heat transfer sub-model is employed to predict the temperature evolution of the composite cylinder wall and accounts for heat transport due to decomposition and mass loss. A composite decomposition sub-model described by Arrhenius's law is implemented to predict the residual resin content of thermal damaged area. A sub-model for material degradation is implemented to account for the loss of mechanical properties. A progressive failure model is adopted to detect various types of mechanical failure. These sub-models are implemented in ABAQUS commercial finite element code using user subroutines. Numerical results are presented for thermal damage, residual properties and profile of resin content in the cylinder. The developed model provides a useful tool for safe design and structural assessment of high pressure composite hydrogen storage cylinders. (author)

  8. Predictive Modeling in Race Walking

    Directory of Open Access Journals (Sweden)

    Krzysztof Wiktorowicz

    2015-01-01

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

  9. Adding propensity scores to pure prediction models fails to improve predictive performance

    Directory of Open Access Journals (Sweden)

    Amy S. Nowacki

    2013-08-01

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

  10. Predictions and Experimental Microstructural Characterization of High Strain Rate Failure Modes in Layered Aluminum Composites

    Science.gov (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

  11. Model-free and model-based reward prediction errors in EEG.

    Science.gov (United States)

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

    2018-05-24

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

  12. Predictability of bee community composition after floral removals differs by floral trait group.

    Science.gov (United States)

    Urban-Mead, Katherine R

    2017-11-01

    Plant-bee visitor communities are complex networks. While studies show that deleting nodes alters network topology, predicting these changes in the field remains difficult. Here, a simple trait-based approach is tested for predicting bee community composition following disturbance. I selected six fields with mixed cover of flower species with shallow (open) and deep (tube) nectar access, and removed all flowers or flower heads of species of each trait in different plots paired with controls, then observed bee foraging and composition. I compared the bee community in each manipulated plot with bees on the same flower species in control plots. The bee morphospecies composition in manipulations with only tube flowers remaining was the same as that in the control plots, while the bee morphospecies on only open flowers were dissimilar from those in control plots. However, the proportion of short- and long-tongued bees on focal flowers did not differ between control and manipulated plots for either manipulation. So, bees within some functional groups are more strongly linked to their floral trait partners than others. And, it may be more fruitful to describe expected bee community compositions in terms of relative proportions of relevant ecological traits than species, particularly in species-diverse communities. © 2017 The Author(s).

  13. Modelling and prediction of pig iron variables in the blast furnace

    Energy Technology Data Exchange (ETDEWEB)

    Saxen, H.; Laaksonen, M.; Waller, M. [Aabo Akademi, Turku (Finland). Heat Engineering Lab.

    1996-12-31

    The blast furnace, where pig iron for steelmaking is produced, is an extremely complicated process, with heat and mass transfer and chemical reactions between several phases. Very few direct measurements on the internal state are available in the operation of the process. A main problem in on-line analysis and modelling is that the state of the furnace may undergo spontaneous changes, which alter the dynamic behaviour of the process. Moreover, large internal disturbances frequently occur, which affect the product quality. The work in this research project focuses on a central problem in the control of the blast furnace process, i.e., short-term prediction of pig iron variables. The problem is of considerable importance for fuel economy, product quality, and for an optimal decision making in integrated steel plants. The operation of the blast furnace aims at producing a product (hot metal) with variables maintained on a stable level (close to their setpoints) without waste of expensive fuel (metallurgical coke). The hot metal temperature and composition affect the downstream (steelmaking) processes, so fluctuations in the pig iron quality must be `corrected` in the steel plant. The goal is to develop a system which predicts the evolution of the hot metal variables (temperature, chemical composition) during the next few taps, and that can be used for decision-making in the operation of the blast furnace. Because of the complicated behaviour of the process, it is considered important to include both deterministic and stochastic components in the modelling: Mathematical models, which on the basis of measurements describe the physical state of the process, and statistical (black-box) models will be combined in the system. Moreover, different models will be applied in different domains in order to capture structural changes in the dynamics of the process SULA 2 Research Programme; 17 refs.

  14. Modelling and prediction of pig iron variables in the blast furnace

    Energy Technology Data Exchange (ETDEWEB)

    Saxen, H; Laaksonen, M; Waller, M [Aabo Akademi, Turku (Finland). Heat Engineering Lab.

    1997-12-31

    The blast furnace, where pig iron for steelmaking is produced, is an extremely complicated process, with heat and mass transfer and chemical reactions between several phases. Very few direct measurements on the internal state are available in the operation of the process. A main problem in on-line analysis and modelling is that the state of the furnace may undergo spontaneous changes, which alter the dynamic behaviour of the process. Moreover, large internal disturbances frequently occur, which affect the product quality. The work in this research project focuses on a central problem in the control of the blast furnace process, i.e., short-term prediction of pig iron variables. The problem is of considerable importance for fuel economy, product quality, and for an optimal decision making in integrated steel plants. The operation of the blast furnace aims at producing a product (hot metal) with variables maintained on a stable level (close to their setpoints) without waste of expensive fuel (metallurgical coke). The hot metal temperature and composition affect the downstream (steelmaking) processes, so fluctuations in the pig iron quality must be `corrected` in the steel plant. The goal is to develop a system which predicts the evolution of the hot metal variables (temperature, chemical composition) during the next few taps, and that can be used for decision-making in the operation of the blast furnace. Because of the complicated behaviour of the process, it is considered important to include both deterministic and stochastic components in the modelling: Mathematical models, which on the basis of measurements describe the physical state of the process, and statistical (black-box) models will be combined in the system. Moreover, different models will be applied in different domains in order to capture structural changes in the dynamics of the process SULA 2 Research Programme; 17 refs.

  15. An experimental study on prediction of gallstone composition by ultrasonography and computed tomography

    International Nuclear Information System (INIS)

    Lee, Jong Beum; Chung, Sae Yul; Kim, Kun Sang; Lee, Yong Chul; Han, Man Chung; Kim, Jin Kyu

    1992-01-01

    Prediction of chemical composition of gallstones is a prerequisite in contemplating the chemical dissolution or extracorporeal shock wave lithotripsy of gallstones. The author retrospectively analysed the correlation between quantitative chemical composition of gallstones and their ultrasonographic and computed tomographic findings. The ultrasonography(US) and computed tomography(CT) of 100 consecutive stones obtained from 100 patients were performed under the in vitro condition. Their US and CT findings were grouped with certain pattern and each group was compared with the chemical composition of the stones. Stones with entirely discernible circumference and homogeneous internal echo on US had high bilirubin and low cholesterol content. Acoustic shadows were frequently absent with those stones. Stones with variable internal echo on US had relatively high cholesterol content but their distribution range were wide. There was no correlationship between the cholesterol content and the CT No. of the gallstones. There was positive correlationship between the calcium content and the CT No. of gallstones. The near totally calcified gallstones had very low cholesterol and high residue content. There was no relationship between the calcification type and the ultrasonographic pattern. In conclusion, those stones with entirely discernible circumference and homogeneous internal echo on US were pigment stones. On the contrary, stones with variable internal echo had relatively high cholesterol content. CT could predict the calcium content with CT No., but could not predict the cholesterol content

  16. Predictive model for local scour downstream of hydrokinetic turbines in erodible channels

    Science.gov (United States)

    Musa, Mirko; Heisel, Michael; Guala, Michele

    2018-02-01

    A modeling framework is derived to predict the scour induced by marine hydrokinetic turbines installed on fluvial or tidal erodible bed surfaces. Following recent advances in bridge scour formulation, the phenomenological theory of turbulence is applied to describe the flow structures that dictate the equilibrium scour depth condition at the turbine base. Using scaling arguments, we link the turbine operating conditions to the flow structures and scour depth through the drag force exerted by the device on the flow. The resulting theoretical model predicts scour depth using dimensionless parameters and considers two potential scenarios depending on the proximity of the turbine rotor to the erodible bed. The model is validated at the laboratory scale with experimental data comprising the two sediment mobility regimes (clear water and live bed), different turbine configurations, hydraulic settings, bed material compositions, and migrating bedform types. The present work provides future developers of flow energy conversion technologies with a physics-based predictive formula for local scour depth beneficial to feasibility studies and anchoring system design. A potential prototype-scale deployment in a large sandy river is also considered with our model to quantify how the expected scour depth varies as a function of the flow discharge and rotor diameter.

  17. Nonlinear chaotic model for predicting storm surges

    Directory of Open Access Journals (Sweden)

    M. Siek

    2010-09-01

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

  18. COMPARING FINANCIAL DISTRESS PREDICTION MODELS BEFORE AND DURING RECESSION

    Directory of Open Access Journals (Sweden)

    Nataša Šarlija

    2011-02-01

    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.

  19. An online model composition tool for system biology models.

    Science.gov (United States)

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

    2013-09-05

    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.

  20. Predicting Madura cattle growth curve using non-linear model

    Science.gov (United States)

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

    2018-03-01

    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.

  1. 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

    2014-09-01

    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.

  2. The logical primitives of thought: Empirical foundations for compositional cognitive models.

    Science.gov (United States)

    Piantadosi, Steven T; Tenenbaum, Joshua B; Goodman, Noah D

    2016-07-01

    The notion of a compositional language of thought (LOT) has been central in computational accounts of cognition from earliest attempts (Boole, 1854; Fodor, 1975) to the present day (Feldman, 2000; Penn, Holyoak, & Povinelli, 2008; Fodor, 2008; Kemp, 2012; Goodman, Tenenbaum, & Gerstenberg, 2015). Recent modeling work shows how statistical inferences over compositionally structured hypothesis spaces might explain learning and development across a variety of domains. However, the primitive components of such representations are typically assumed a priori by modelers and theoreticians rather than determined empirically. We show how different sets of LOT primitives, embedded in a psychologically realistic approximate Bayesian inference framework, systematically predict distinct learning curves in rule-based concept learning experiments. We use this feature of LOT models to design a set of large-scale concept learning experiments that can determine the most likely primitives for psychological concepts involving Boolean connectives and quantification. Subjects' inferences are most consistent with a rich (nonminimal) set of Boolean operations, including first-order, but not second-order, quantification. Our results more generally show how specific LOT theories can be distinguished empirically. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  3. Predicting the weathering of fuel and oil spills: A diffusion-limited evaporation model.

    Science.gov (United States)

    Kotzakoulakis, Konstantinos; George, Simon C

    2018-01-01

    The majority of the evaporation models currently available in the literature for the prediction of oil spill weathering do not take into account diffusion-limited mass transport and the formation of a concentration gradient in the oil phase. The altered surface concentration of the spill caused by diffusion-limited transport leads to a slower evaporation rate compared to the predictions of diffusion-agnostic evaporation models. The model presented in this study incorporates a diffusive layer in the oil phase and predicts the diffusion-limited evaporation rate. The information required is the composition of the fluid from gas chromatography or alternatively the distillation data. If the density or a single viscosity measurement is available the accuracy of the predictions is higher. Environmental conditions such as water temperature, air pressure and wind velocity are taken into account. The model was tested with synthetic mixtures, petroleum fuels and crude oils with initial viscosities ranging from 2 to 13,000 cSt. The tested temperatures varied from 0 °C to 23.4 °C and wind velocities from 0.3 to 3.8 m/s. The average absolute deviation (AAD) of the diffusion-limited model ranged between 1.62% and 24.87%. In comparison, the AAD of a diffusion-agnostic model ranged between 2.34% and 136.62% against the same tested fluids. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. Extracting falsifiable predictions from sloppy models.

    Science.gov (United States)

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

    2007-12-01

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

  5. Predicting the mineral composition of dust aerosols: Insights from elemental composition measured at the Izaña Observatory

    Science.gov (United States)

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

    2016-10-01

    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.

  6. A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials

    Energy Technology Data Exchange (ETDEWEB)

    Matouš, Karel, E-mail: kmatous@nd.edu [Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556 (United States); Geers, Marc G.D.; Kouznetsova, Varvara G. [Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven (Netherlands); Gillman, Andrew [Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556 (United States)

    2017-02-01

    Since the beginning of the industrial age, material performance and design have been in the midst of innovation of many disruptive technologies. Today's electronics, space, medical, transportation, and other industries are enriched by development, design and deployment of composite, heterogeneous and multifunctional materials. As a result, materials innovation is now considerably outpaced by other aspects from component design to product cycle. In this article, we review predictive nonlinear theories for multiscale modeling of heterogeneous materials. Deeper attention is given to multiscale modeling in space and to computational homogenization in addressing challenging materials science questions. Moreover, we discuss a state-of-the-art platform in predictive image-based, multiscale modeling with co-designed simulations and experiments that executes on the world's largest supercomputers. Such a modeling framework consists of experimental tools, computational methods, and digital data strategies. Once fully completed, this collaborative and interdisciplinary framework can be the basis of Virtual Materials Testing standards and aids in the development of new material formulations. Moreover, it will decrease the time to market of innovative products.

  7. A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials

    Science.gov (United States)

    Matouš, Karel; Geers, Marc G. D.; Kouznetsova, Varvara G.; Gillman, Andrew

    2017-02-01

    Since the beginning of the industrial age, material performance and design have been in the midst of innovation of many disruptive technologies. Today's electronics, space, medical, transportation, and other industries are enriched by development, design and deployment of composite, heterogeneous and multifunctional materials. As a result, materials innovation is now considerably outpaced by other aspects from component design to product cycle. In this article, we review predictive nonlinear theories for multiscale modeling of heterogeneous materials. Deeper attention is given to multiscale modeling in space and to computational homogenization in addressing challenging materials science questions. Moreover, we discuss a state-of-the-art platform in predictive image-based, multiscale modeling with co-designed simulations and experiments that executes on the world's largest supercomputers. Such a modeling framework consists of experimental tools, computational methods, and digital data strategies. Once fully completed, this collaborative and interdisciplinary framework can be the basis of Virtual Materials Testing standards and aids in the development of new material formulations. Moreover, it will decrease the time to market of innovative products.

  8. A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials

    International Nuclear Information System (INIS)

    Matouš, Karel; Geers, Marc G.D.; Kouznetsova, Varvara G.; Gillman, Andrew

    2017-01-01

    Since the beginning of the industrial age, material performance and design have been in the midst of innovation of many disruptive technologies. Today's electronics, space, medical, transportation, and other industries are enriched by development, design and deployment of composite, heterogeneous and multifunctional materials. As a result, materials innovation is now considerably outpaced by other aspects from component design to product cycle. In this article, we review predictive nonlinear theories for multiscale modeling of heterogeneous materials. Deeper attention is given to multiscale modeling in space and to computational homogenization in addressing challenging materials science questions. Moreover, we discuss a state-of-the-art platform in predictive image-based, multiscale modeling with co-designed simulations and experiments that executes on the world's largest supercomputers. Such a modeling framework consists of experimental tools, computational methods, and digital data strategies. Once fully completed, this collaborative and interdisciplinary framework can be the basis of Virtual Materials Testing standards and aids in the development of new material formulations. Moreover, it will decrease the time to market of innovative products.

  9. Long fiber polymer composite property calculation in injection molding simulation

    Science.gov (United States)

    Jin, Xiaoshi; Wang, Jin; Han, Sejin

    2013-05-01

    Long fiber filled polymer composite materials have attracted a great attention and usage in recent years. However, the injection and compression molded long fiber composite materials possess complex microstructures that include spatial variations in fiber orientation and length. This paper presents the recent implemented anisotropic rotary diffusion - reduced strain closure (ARD-RSC) model for predicting fiber orientation distribution[1] and a newly developed fiber breakage model[2] for predicting fiber length distribution in injection and compression molding simulation, and Eshelby-Mori-Tanaka model[3,4] with fiber-matrix de-bonding model[5] have been implemented to calculate the long fiber composite property distribution with predicted fiber orientation and fiber length distributions. A validation study on fiber orientation, fiber breakage and mechanical property distributions are given with injection molding process simulation.

  10. Thermophysical characterization tools and numerical models for high temperature thermo-structural composite materials; Outils de caracterisation thermophysique et modeles numeriques pour les composites thermostructuraux a haute temperature

    Energy Technology Data Exchange (ETDEWEB)

    Lorrette, Ch

    2007-04-15

    This work is an original contribution to the study of the thermo-structural composite materials thermal behaviour. It aims to develop a methodology with a new experimental device for thermal characterization adapted to this type of material and to model the heat transfer by conduction within these heterogeneous media. The first part deals with prediction of the thermal effective conductivity of stratified composite materials in the three space directions. For that, a multi scale model using a rigorous morphology analysis of the structure and the elementary properties is proposed and implemented. The second part deals with the thermal characterization at high temperature. It shows how to estimate simultaneously the thermal effusiveness and the thermal conductivity. The present method is based on the observation of the heating from a plane sample submitted to a continuous excitation generated by Joule Effect. Heat transfer is modelled with the quadrupole formalism, temperature is here measured on two sides of the sample. The development of both resistive probes for excitation and linear probes for temperature measurements enables the thermal properties measured up to 1000 C. Finally, some experimental and numerical application examples lead to review the obtained results. (author)

  11. Fixed recurrence and slip models better predict earthquake behavior than the time- and slip-predictable models 1: repeating earthquakes

    Science.gov (United States)

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

    2012-01-01

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

  12. EFFICIENT PREDICTIVE MODELLING FOR ARCHAEOLOGICAL RESEARCH

    OpenAIRE

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

    2014-01-01

    The study presents a general methodology for designing, developing and implementing predictive modelling for identifying areas of archaeological interest. The methodology is based on documented archaeological data and geographical factors, geospatial analysis and predictive modelling, and has been applied to the identification of possible Macedonian tombs’ locations in Northern Greece. The model was tested extensively and the results were validated using a commonly used predictive gain, which...

  13. Ridge regression for predicting elastic moduli and hardness of calcium aluminosilicate glasses

    Science.gov (United States)

    Deng, Yifan; Zeng, Huidan; Jiang, Yejia; Chen, Guorong; Chen, Jianding; Sun, Luyi

    2018-03-01

    It is of great significance to design glasses with satisfactory mechanical properties predictively through modeling. Among various modeling methods, data-driven modeling is such a reliable approach that can dramatically shorten research duration, cut research cost and accelerate the development of glass materials. In this work, the ridge regression (RR) analysis was used to construct regression models for predicting the compositional dependence of CaO-Al2O3-SiO2 glass elastic moduli (Shear, Bulk, and Young’s moduli) and hardness based on the ternary diagram of the compositions. The property prediction over a large glass composition space was accomplished with known experimental data of various compositions in the literature, and the simulated results are in good agreement with the measured ones. This regression model can serve as a facile and effective tool for studying the relationship between the compositions and the property, enabling high-efficient design of glasses to meet the requirements for specific elasticity and hardness.

  14. Spatial Economics Model Predicting Transport Volume

    Directory of Open Access Journals (Sweden)

    Lu Bo

    2016-10-01

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

  15. Advanced Manufacturing Technologies (AMT): Composites Integrated Modeling

    Data.gov (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...

  16. Creep of high temperature composites

    International Nuclear Information System (INIS)

    Sadananda, K.; Feng, C.R.

    1993-01-01

    High temperature creep deformation of composites is examined. Creep of composites depends on the interplay of many factors. One of the basic issues in the design of the creep resistant composites is the ability to predict their creep behavior from the knowledge of the creep behavior of the individual components. In this report, the existing theoretical models based on continuum mechanics principles are reviewed. These models are evaluated using extensive experimental data on molydisilicide-silicon carbide composites obtained by the authors. The analysis shows that the rule of mixture based on isostrain and isostress provides two limiting bounds wherein all other theoretical predictions fall. For molydisilicide composites, the creep is predominantly governed by the creep of the majority phase, i.e. the matrix with fibers deforming elastically. The role of back stresses both on creep rates and activation energies are shown to be minimum. Kinetics of creep in MoSi 2 is shown to be controlled by the process of dislocation glide with climb involving the diffusion of Mo atoms

  17. A model for prediction of fume formation rate in gas metal arc welding (GMAW), globular and spray modes, DC electrode positive.

    Science.gov (United States)

    Dennis, J H; Hewitt, P J; Redding, C A; Workman, A D

    2001-03-01

    Prediction of fume formation rate during metal arc welding and the composition of the fume are of interest to occupational hygienists concerned with risk assessment and to manufacturers of welding consumables. A model for GMAW (DC electrode positive) is described based on the welder determined process parameters (current, wire feed rate and wire composition), on the surface area of molten metal in the arc and on the partial vapour pressures of the component metals of the alloy wire. The model is applicable to globular and spray welding transfer modes but not to dip mode. Metal evaporation from a droplet is evaluated for short time increments and total evaporation obtained by summation over the life of the droplet. The contribution of fume derived from the weld pool and spatter (particles of metal ejected from the arc) is discussed, as are limitations of the model. Calculated droplet temperatures are similar to values determined by other workers. A degree of relationship between predicted and measured fume formation rates is demonstrated but the model does not at this stage provide a reliable predictive tool.

  18. Improved prediction of higher heating value of biomass using an artificial neural network model based on proximate analysis.

    Science.gov (United States)

    Uzun, Harun; Yıldız, Zeynep; Goldfarb, Jillian L; Ceylan, Selim

    2017-06-01

    As biomass becomes more integrated into our energy feedstocks, the ability to predict its combustion enthalpies from routine data such as carbon, ash, and moisture content enables rapid decisions about utilization. The present work constructs a novel artificial neural network model with a 3-3-1 tangent sigmoid architecture to predict biomasses' higher heating values from only their proximate analyses, requiring minimal specificity as compared to models based on elemental composition. The model presented has a considerably higher correlation coefficient (0.963) and lower root mean square (0.375), mean absolute (0.328), and mean bias errors (0.010) than other models presented in the literature which, at least when applied to the present data set, tend to under-predict the combustion enthalpy. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Comparison of various tool wear prediction methods during end milling of metal matrix composite

    Science.gov (United States)

    Wiciak, Martyna; Twardowski, Paweł; Wojciechowski, Szymon

    2018-02-01

    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.

  20. Linking Compositional and Functional Predictions to Decipher the Biogeochemical Significance in DFAA Turnover of Abundant Bacterioplankton Lineages in the North Sea

    Directory of Open Access Journals (Sweden)

    Bernd Wemheuer

    2017-11-01

    Full Text Available Deciphering the ecological traits of abundant marine bacteria is a major challenge in marine microbial ecology. In the current study, we linked compositional and functional predictions to elucidate such traits for abundant bacterioplankton lineages in the North Sea. For this purpose, we investigated entire and active bacterioplankton composition along a transect ranging from the German Bight to the northern North Sea by pyrotag sequencing of bacterial 16S rRNA genes and transcripts. Functional profiles were inferred from 16S rRNA data using Tax4Fun. Bacterioplankton communities were dominated by well-known marine lineages including clusters/genera that are affiliated with the Roseobacter group and the Flavobacteria. Variations in community composition and function were significantly explained by measured environmental and microbial properties. Turnover of dissolved free amino acids (DFAA showed the strongest correlation to community composition and function. We applied multinomial models, which enabled us to identify bacterial lineages involved in DFAA turnover. For instance, the genus Planktomarina was more abundant at higher DFAA turnover rates, suggesting its vital role in amino acid degradation. Functional predictions further indicated that Planktomarina is involved in leucine and isoleucine degradation. Overall, our results provide novel insights into the biogeochemical significance of abundant bacterioplankton lineages in the North Sea.

  1. Linking Compositional and Functional Predictions to Decipher the Biogeochemical Significance in DFAA Turnover of Abundant Bacterioplankton Lineages in the North Sea.

    Science.gov (United States)

    Wemheuer, Bernd; Wemheuer, Franziska; Meier, Dimitri; Billerbeck, Sara; Giebel, Helge-Ansgar; Simon, Meinhard; Scherber, Christoph; Daniel, Rolf

    2017-11-05

    Deciphering the ecological traits of abundant marine bacteria is a major challenge in marine microbial ecology. In the current study, we linked compositional and functional predictions to elucidate such traits for abundant bacterioplankton lineages in the North Sea. For this purpose, we investigated entire and active bacterioplankton composition along a transect ranging from the German Bight to the northern North Sea by pyrotag sequencing of bacterial 16S rRNA genes and transcripts. Functional profiles were inferred from 16S rRNA data using Tax4Fun. Bacterioplankton communities were dominated by well-known marine lineages including clusters/genera that are affiliated with the Roseobacter group and the Flavobacteria . Variations in community composition and function were significantly explained by measured environmental and microbial properties. Turnover of dissolved free amino acids (DFAA) showed the strongest correlation to community composition and function. We applied multinomial models, which enabled us to identify bacterial lineages involved in DFAA turnover. For instance, the genus Planktomarina was more abundant at higher DFAA turnover rates, suggesting its vital role in amino acid degradation. Functional predictions further indicated that Planktomarina is involved in leucine and isoleucine degradation. Overall, our results provide novel insights into the biogeochemical significance of abundant bacterioplankton lineages in the North Sea.

  2. Mechanical Degradation of Graphite/PVDF Composite Electrodes: A Model-Experimental Study

    Energy Technology Data Exchange (ETDEWEB)

    Takahashi, K; Higa, K; Mair, S; Chintapalli, M; Balsara, N; Srinivasan, V

    2015-12-11

    Mechanical failure modes of a graphite/polyvinylidene difluoride (PVDF) composite electrode for lithium-ion batteries were investigated by combining realistic stress-stain tests and mathematical model predictions. Samples of PVDF mixed with conductive additive were prepared in a similar way to graphite electrodes and tested while submerged in electrolyte solution. Young's modulus and tensile strength values of wet samples were found to be approximately one-fifth and one-half of those measured for dry samples. Simulations of graphite particles surrounded by binder layers given the measured material property values suggest that the particles are unlikely to experience mechanical damage during cycling, but that the fate of the surrounding composite of PVDF and conductive additive depends completely upon the conditions under which its mechanical properties were obtained. Simulations using realistic property values produced results that were consistent with earlier experimental observations.

  3. Modeling and Prediction of Coal Ash Fusion Temperature based on BP Neural Network

    Directory of Open Access Journals (Sweden)

    Miao Suzhen

    2016-01-01

    Full Text Available Coal ash is the residual generated from combustion of coal. The ash fusion temperature (AFT of coal gives detail information on the suitability of a coal source for gasification procedures, and specifically to which extent ash agglomeration or clinkering is likely to occur within the gasifier. To investigate the contribution of oxides in coal ash to AFT, data of coal ash chemical compositions and Softening Temperature (ST in different regions of China were collected in this work and a BP neural network model was established by XD-APC PLATFORM. In the BP model, the inputs were the ash compositions and the output was the ST. In addition, the ash fusion temperature prediction model was obtained by industrial data and the model was generalized by different industrial data. Compared to empirical formulas, the BP neural network obtained better results. By different tests, the best result and the best configurations for the model were obtained: hidden layer nodes of the BP network was setted as three, the component contents (SiO2, Al2O3, Fe2O3, CaO, MgO were used as inputs and ST was used as output of the model.

  4. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

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

  5. Incorporating uncertainty in predictive species distribution modelling.

    Science.gov (United States)

    Beale, Colin M; Lennon, Jack J

    2012-01-19

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

  6. Predictive user modeling with actionable attributes

    NARCIS (Netherlands)

    Zliobaite, I.; Pechenizkiy, M.

    2013-01-01

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

  7. Assessing composition and structure of soft biphasic media from Kelvin-Voigt fractional derivative model parameters

    Science.gov (United States)

    Zhang, Hongmei; Wang, Yue; Fatemi, Mostafa; Insana, Michael F.

    2017-03-01

    Kelvin-Voigt fractional derivative (KVFD) model parameters have been used to describe viscoelastic properties of soft tissues. However, translating model parameters into a concise set of intrinsic mechanical properties related to tissue composition and structure remains challenging. This paper begins by exploring these relationships using a biphasic emulsion materials with known composition. Mechanical properties are measured by analyzing data from two indentation techniques—ramp-stress relaxation and load-unload hysteresis tests. Material composition is predictably correlated with viscoelastic model parameters. Model parameters estimated from the tests reveal that elastic modulus E 0 closely approximates the shear modulus for pure gelatin. Fractional-order parameter α and time constant τ vary monotonically with the volume fraction of the material’s fluid component. α characterizes medium fluidity and the rate of energy dissipation, and τ is a viscous time constant. Numerical simulations suggest that the viscous coefficient η is proportional to the energy lost during quasi-static force-displacement cycles, E A . The slope of E A versus η is determined by α and the applied indentation ramp time T r. Experimental measurements from phantom and ex vivo liver data show close agreement with theoretical predictions of the η -{{E}A} relation. The relative error is less than 20% for emulsions 22% for liver. We find that KVFD model parameters form a concise features space for biphasic medium characterization that described time-varying mechanical properties. The experimental work was carried out at the Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. Methodological development, including numerical simulation and all data analysis, were carried out at the school of Life Science and Technology, Xi’an JiaoTong University, 710049, China.

  8. 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)

    2017-04-15

    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.

  9. 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)

    2016-06-01

    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.

  10. Inverse modeling with RZWQM2 to predict water quality

    Science.gov (United States)

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

    2011-01-01

    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

  11. Compositional and Quantitative Model Checking

    DEFF Research Database (Denmark)

    Larsen, Kim Guldstrand

    2010-01-01

    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...

  12. Computational modeling of elastic properties of carbon nanotube/polymer composites with interphase regions. Part II: Mechanical modeling

    KAUST Repository

    Han, Fei

    2014-01-01

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

  13. Modeling and Simulation of Fiber Orientation in Injection Molding of Polymer Composites

    Directory of Open Access Journals (Sweden)

    Jang Min Park

    2011-01-01

    Full Text Available We review the fundamental modeling and numerical simulation for a prediction of fiber orientation during injection molding process of polymer composite. In general, the simulation of fiber orientation involves coupled analysis of flow, temperature, moving free surface, and fiber kinematics. For the governing equation of the flow, Hele-Shaw flow model along with the generalized Newtonian constitutive model has been widely used. The kinematics of a group of fibers is described in terms of the second-order fiber orientation tensor. Folgar-Tucker model and recent fiber kinematics models such as a slow orientation model are discussed. Also various closure approximations are reviewed. Therefore, the coupled numerical methods are needed due to the above complex problems. We review several well-established methods such as a finite-element/finite-different hybrid scheme for Hele-Shaw flow model and a finite element method for a general three-dimensional flow model.

  14. A nonlinear model for ionic polymer metal composites as actuators

    Science.gov (United States)

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

    2007-02-01

    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.

  15. 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

    2012-05-08

    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.

  16. Cell wall composition throughout development for the model grass Brachypodium distanchyon

    Directory of Open Access Journals (Sweden)

    David eRancour

    2012-12-01

    Full Text Available Temperate perennial grasses are important worldwide as a livestock nutritive energy source and a potential feedstock for lignocellulosic biofuel production. The annual temperate grass Brachypodium distanchyon has been championed as a useful model system to facilitate biological research in agriculturally important temperate forage grasses based on phylogenetic relationships. To physically corroborate genetic predictions, we determined the chemical composition profiles of organ-specific cell walls throughout the development of two common diploid accessions of Brachypodium distanchyon, Bd21-3 and Bd21. Chemical analysis was performed on cell walls isolated from distinct organs (i.e. leaves, sheaths, stems and roots at three developmental stages of 1 12-day seedling, 2 vegetative-to-reproductive transition, and 3 mature seed-fill. In addition, we have included cell wall analysis of embryonic callus used for genetic transformations. Composition of cell walls based on components lignin, hydroxycinnamates, uronosyls, neutral sugars, and protein suggests that Brachypodium distanchyon is similar chemically to agriculturally important forage grasses. There were modest compositional differences in hydroxycinnamate profiles between accessions Bd21-3 and Bd21. In addition, when compared to agronomical important C3 grasses, more mature Brachypodium stem cell walls have a relative increase in glucose of 48% and a decrease in lignin of 36%. Though differences exists between Brachypodium and agronomical important C3 grasses, Brachypodium distanchyon should be still a useful model system for genetic manipulation of cell wall composition to determine the impact upon functional characteristics such as rumen digestibility or energy conversion efficiency for bioenergy production.

  17. Cell wall composition throughout development for the model grass Brachypodium distachyon

    Science.gov (United States)

    Rancour, David M.; Marita, Jane M.; Hatfield, Ronald D.

    2012-01-01

    Temperate perennial grasses are important worldwide as a livestock nutritive energy source and a potential feedstock for lignocellulosic biofuel production. The annual temperate grass Brachypodium distachyon has been championed as a useful model system to facilitate biological research in agriculturally important temperate forage grasses based on phylogenetic relationships. To physically corroborate genetic predictions, we determined the chemical composition profiles of organ-specific cell walls throughout the development of two common diploid accessions of Brachypodium distachyon, Bd21-3 and Bd21. Chemical analysis was performed on cell walls isolated from distinct organs (i.e., leaves, sheaths, stems, and roots) at three developmental stages of (1) 12-day seedling, (2) vegetative-to-reproductive transition, and (3) mature seed fill. In addition, we have included cell wall analysis of embryonic callus used for genetic transformations. Composition of cell walls based on components lignin, hydroxycinnamates, uronosyls, neutral sugars, and protein suggests that Brachypodium distachyon is similar chemically to agriculturally important forage grasses. There were modest compositional differences in hydroxycinnamate profiles between accessions Bd21-3 and Bd21. In addition, when compared to agronomical important C3 grasses, more mature Brachypodium stem cell walls have a relative increase in glucose of 48% and a decrease in lignin of 36%. Though differences exist between Brachypodium and agronomical important C3 grasses, Brachypodium distachyon should be still a useful model system for genetic manipulation of cell wall composition to determine the impact upon functional characteristics such as rumen digestibility or energy conversion efficiency for bioenergy production. PMID:23227028

  18. MJO prediction skill of the subseasonal-to-seasonal (S2S) prediction models

    Science.gov (United States)

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

    2017-12-01

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

  19. Application of bioelectrical impedance analysis in prediction of light kid carcass and muscle chemical composition.

    Science.gov (United States)

    Silva, S R; Afonso, J; Monteiro, A; Morais, R; Cabo, A; Batista, A C; Guedes, C M; Teixeira, A

    2018-06-01

    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.

  20. Evaluating Predictive Uncertainty of Hyporheic Exchange Modelling

    Science.gov (United States)

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

    2017-12-01

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

  1. A Novel Procedure for Prediction of Mixed Mode I/II in Fracture Toughness of Laminate Composites

    Directory of Open Access Journals (Sweden)

    M. Mahmood Shokrieh

    2014-06-01

    Full Text Available Delamination is one of the important modes of failure in laminated composite materials. In this respect, the mixed mode I/II fracture is the most major mode of delamination incidence in laminated composite. In the present research, a relation between the fracture toughness of double cantilever beam (DCB and asymmetric double cantilever beam (ADCB specimens is presented. The DCB and ADCB samples are used for measuring the mode I and mixed mode I/II fracture toughness (G of laminated composite materials, respectively. By considering the diversity of the stacking sequence of lay-ups, the test performance on all different types of lay-ups in order to measure the fracture toughness of laminated composites is a tedious, costly and time consuming task. The purpose of deriving this relation is to estimate the value of the strain energy release rate of laminated composite ADCB specimens by testing a unidirectional DCB. To develop this relationship, the geometry of DCB and ADCB specimens are considered to obtain fracture toughness of multi-directional laminate composites of ADCB samples with arbitrary ply sequence which may be used for design purposes. The procedure presented here reduces the calculation costs of the finite element modeling and its corresponding test significantly. The results obtained by this method are compared with those of experimental and numerical methods. It is shown that the fracture toughness of multi-directional lay-ups can be predicted by measuring the unidirectional ply with an error less than 10% demonstrating the accuracy of the procedure developed in the present research.

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

    NARCIS (Netherlands)

    Liu, S.

    2016-01-01

    In Model Predictive Control (MPC) for traffic networks, traffic models are crucial since they are used as prediction models for determining the optimal control actions. In order to reduce the computational complexity of MPC for traffic networks, macroscopic traffic models are often used instead of

  3. Flavor and CP invariant composite Higgs models

    International Nuclear Information System (INIS)

    Redi, Michele; Weiler, Andreas

    2011-09-01

    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.)

  4. 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

  5. Using different assumptions of aerosol mixing state and chemical composition to predict CCN concentrations based on field measurements in urban Beijing

    Science.gov (United States)

    Ren, Jingye; Zhang, Fang; Wang, Yuying; Collins, Don; Fan, Xinxin; Jin, Xiaoai; Xu, Weiqi; Sun, Yele; Cribb, Maureen; Li, Zhanqing

    2018-05-01

    Understanding the impacts of aerosol chemical composition and mixing state on cloud condensation nuclei (CCN) activity in polluted areas is crucial for accurately predicting CCN number concentrations (NCCN). In this study, we predict NCCN under five assumed schemes of aerosol chemical composition and mixing state based on field measurements in Beijing during the winter of 2016. Our results show that the best closure is achieved with the assumption of size dependent chemical composition for which sulfate, nitrate, secondary organic aerosols, and aged black carbon are internally mixed with each other but externally mixed with primary organic aerosol and fresh black carbon (external-internal size-resolved, abbreviated as EI-SR scheme). The resulting ratios of predicted-to-measured NCCN (RCCN_p/m) were 0.90 - 0.98 under both clean and polluted conditions. Assumption of an internal mixture and bulk chemical composition (INT-BK scheme) shows good closure with RCCN_p/m of 1.0 -1.16 under clean conditions, implying that it is adequate for CCN prediction in continental clean regions. On polluted days, assuming the aerosol is internally mixed and has a chemical composition that is size dependent (INT-SR scheme) achieves better closure than the INT-BK scheme due to the heterogeneity and variation in particle composition at different sizes. The improved closure achieved using the EI-SR and INT-SR assumptions highlight the importance of measuring size-resolved chemical composition for CCN predictions in polluted regions. NCCN is significantly underestimated (with RCCN_p/m of 0.66 - 0.75) when using the schemes of external mixtures with bulk (EXT-BK scheme) or size-resolved composition (EXT-SR scheme), implying that primary particles experience rapid aging and physical mixing processes in urban Beijing. However, our results show that the aerosol mixing state plays a minor role in CCN prediction when the κorg exceeds 0.1.

  6. Predictive modeling of crystal accumulation in high-level waste glass melters processing radioactive waste

    Energy Technology Data Exchange (ETDEWEB)

    Matyáš, Josef; Gervasio, Vivianaluxa; Sannoh, Sulaiman E.; Kruger, Albert A.

    2017-11-01

    The effectiveness of HLW vitrification is limited by precipitation/accumulation of spinel crystals [(Fe, Ni, Mn, Zn)(Fe, Cr)2O4] in the glass discharge riser of Joule-heated ceramic melters during idling. These crystals do not affect glass durability; however, if accumulated in thick layer, they can clog the melter and prevent discharge of molten glass into canisters. To address this problem, an empirical model was developed that can predict thicknesses of accumulated layers as a function of glass composition. This model predicts well the accumulation of single crystals and/or small-scale agglomerates, but, excessive agglomeration observed in high-Ni-Fe glass resulted in an under-prediction of accumulated layers, which gradually worsen over time as an increased number of agglomerates formed. Accumulation rate of ~53.8 ± 3.7 µm/h determined for this glass will result in ~26 mm thick layer in 20 days of melter idling.

  7. Origin and composition of Mercury

    International Nuclear Information System (INIS)

    Lewis, J.S.

    1988-01-01

    The predictions of the expected range of composition of Mercury at the time of its formation made on the basis of a suite of condensation-accretion models of Mercury spanning a range of condensation temperature and accretion sampling functions appropriate to Mercury are examined. It is concluded that these compositonal models can, if modified to take into account the nonselective loss of most of the silicate component of the planet during accretion, provide compositional predictions for the Weidenschilling (1978, 1980) mechanism for the accretion of a metal-rich Mercury. The silicate portion would, in this case, contain 3.6 to 4.5 percent alumina, roughly 1 percent of alkali oxides, and between 0.5 and 6 percent FeO

  8. Thermoresistive mechanisms of carbon nanotube/polymer composites

    Science.gov (United States)

    Cen-Puc, M.; Oliva-Avilés, A. I.; Avilés, F.

    2018-01-01

    The mechanisms governing thermoresistivity of carbon nanotube (CNT)/polymer composites are theoretically and experimentally investigated. Two modeling approaches are proposed to this aim considering a broad range of CNT concentrations (0.5-50 wt%). In the first model, thermal expansion of the polymer composite is predicted using a finite element model; the resulting CNT-to-CNT separation distance feeds a classical tunneling model to predict the dependence of the electrical resistance with temperature. The second approach uses the general effective medium considering the dilution of the CNT volume fraction due to the thermal expansion of the polymer. Both models predict that the electrical resistance increases with increased temperature (i.e. a positive temperature coefficient of resistance, TCR) for all investigated CNT concentrations, with higher TCRs for lower CNT concentrations. Comparison between modeling outcomes and experimental data suggests that polymer thermal expansion (and tunneling) play a dominant role for low CNT concentrations (≤ 10 wt%) heated above room temperature. On the other hand, for composites at high CNT concentrations (50 wt%) or for freezing temperatures (-110 °C), a negative TCR was experimentally obtained, suggesting that for those conditions the CNT intrinsic thermoresistivity and the electronic conduction between CNTs by thermal activation may play a paramount role.

  9. 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)

    2016-08-01

    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.

  10. Microstructure-based numerical modeling method for effective permittivity of ceramic/polymer composites

    Science.gov (United States)

    Jylhä, Liisi; Honkamo, Johanna; Jantunen, Heli; Sihvola, Ari

    2005-05-01

    Effective permittivity was modeled and measured for composites that consist of up to 35vol% of titanium dioxide powder dispersed in a continuous epoxy matrix. The study demonstrates a method that enables fast and accurate numerical modeling of the effective permittivity values of ceramic/polymer composites. The model requires electrostatic Monte Carlo simulations, where randomly oriented homogeneous prism-shaped inclusions occupy random positions in the background phase. The computation cost of solving the electrostatic problem by a finite-element code is decreased by the use of an averaging method where the same simulated sample is solved three times with orthogonal field directions. This helps to minimize the artificial anisotropy that results from the pseudorandomness inherent in the limited computational domains. All the required parameters for numerical simulations are calculated from the lattice structure of titanium dioxide. The results show a very good agreement between the measured and numerically calculated effective permittivities. When the prisms are approximated by oblate spheroids with the corresponding axial ratio, a fairly good prediction for the effective permittivity of the mixture can be achieved with the use of an advanced analytical mixing formula.

  11. Modelling and analysing interoperability in service compositions using COSMO

    NARCIS (Netherlands)

    Quartel, Dick; van Sinderen, Marten J.

    2008-01-01

    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,

  12. Computational modeling of elastic properties of carbon nanotube/polymer composites with interphase regions. Part I: Micro-structural characterization and geometric modeling

    KAUST Repository

    Han, Fei

    2014-01-01

    A computational strategy to predict the elastic properties of carbon nanotube-reinforced polymer composites is proposed in this two-part paper. In Part I, the micro-structural characteristics of these nano-composites are discerned. These characteristics include networks/agglomerations of carbon nanotubes and thick polymer interphase regions between the nanotubes and the surrounding matrix. An algorithm is presented to construct three-dimensional geometric models with large amounts of randomly dispersed and aggregated nanotubes. The effects of the distribution of the nanotubes and the thickness of the interphase regions on the concentration of the interphase regions are demonstrated with numerical results. © 2013 Elsevier B.V. All rights reserved.

  13. Prediction Models for Dynamic Demand Response

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-11-02

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

  14. Thermodynamic simulation model for predicting the performance of spark ignition engines using biogas as fuel

    International Nuclear Information System (INIS)

    Nunes de Faria, Mário M.; Vargas Machuca Bueno, Juan P.; Ayad, Sami M.M. Elmassalami; Belchior, Carlos R. Pereira

    2017-01-01

    Highlights: • A 0-D model for performance prediction of SI ICE fueled with biogas is proposed. • Relative difference between simulated and experimental values was under 5%. • Can be adapted for different biogas compositions and operating ranges. • Could be a valuable tool for predicting trends and guiding experimentation. • Is suitable for use with biogas supplies in developing regions. - Abstract: Biogas found its way from developing countries and is now an alternative to fossil fuels in internal combustion engines and with the advantage of lower greenhouse gas emissions. However, its use in gas engines requires engine modifications or adaptations that may be costly. This paper reports the results of experimental performance and emissions tests of an engine-generator unit fueled with biogas produced in a sewage plant in Brazil, operating under different loads, and with suitable engine modifications. These emissions and performance results were in agreement with the literature and it was confirmed that the penalties to engine performance were more significant than emission reduction in the operating range tested. Furthermore, a zero dimensional simulation model was employed to predict performance characteristics. Moreover, a differential thermodynamic equation system was solved, obtaining the pressure inside the cylinder as a function of the crank angle for different engine conditions. Mean effective pressure and indicated power were also obtained. The results of simulation and experimental tests of the engine in similar conditions were compared and the model validated. Although several simplifying assumptions were adopted and empirical correlations were used for Wiebe function, the model was adequate in predicting engine performance as the relative difference between simulated and experimental values was lower than 5%. The model can be adapted for use with different raw or enriched biogas compositions and could prove to be a valuable tool to guide

  15. Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models

    Science.gov (United States)

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

    2015-01-01

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

  16. Supervised maximum-likelihood weighting of composite protein networks for complex prediction

    Directory of Open Access Journals (Sweden)

    Yong Chern Han

    2012-12-01

    Full Text Available Abstract Background Protein complexes participate in many important cellular functions, so finding the set of existent complexes is essential for understanding the organization and regulation of processes in the cell. With the availability of large amounts of high-throughput protein-protein interaction (PPI data, many algorithms have been proposed to discover protein complexes from PPI networks. However, such approaches are hindered by the high rate of noise in high-throughput PPI data, including spurious and missing interactions. Furthermore, many transient interactions are detected between proteins that are not from the same complex, while not all proteins from the same complex may actually interact. As a result, predicted complexes often do not match true complexes well, and many true complexes go undetected. Results We address these challenges by integrating PPI data with other heterogeneous data sources to construct a composite protein network, and using a supervised maximum-likelihood approach to weight each edge based on its posterior probability of belonging to a complex. We then use six different clustering algorithms, and an aggregative clustering strategy, to discover complexes in the weighted network. We test our method on Saccharomyces cerevisiae and Homo sapiens, and show that complex discovery is improved: compared to previously proposed supervised and unsupervised weighting approaches, our method recalls more known complexes, achieves higher precision at all recall levels, and generates novel complexes of greater functional similarity. Furthermore, our maximum-likelihood approach allows learned parameters to be used to visualize and evaluate the evidence of novel predictions, aiding human judgment of their credibility. Conclusions Our approach integrates multiple data sources with supervised learning to create a weighted composite protein network, and uses six clustering algorithms with an aggregative clustering strategy to

  17. Predicting the glass transition temperature and viscosity of secondary organic material using molecular composition

    Science.gov (United States)

    Wong DeRieux, Wing-Sy; Li, Ying; Lin, Peng; Laskin, Julia; Laskin, Alexander; Bertram, Allan K.; Nizkorodov, Sergey A.; Shiraiwa, Manabu

    2018-05-01

    Secondary organic aerosol (SOA) accounts for a large fraction of submicron particles in the atmosphere. SOA can occur in amorphous solid or semi-solid phase states depending on chemical composition, relative humidity (RH), and temperature. The phase transition between amorphous solid and semi-solid states occurs at the glass transition temperature (Tg). We have recently developed a method to estimate Tg of pure compounds containing carbon, hydrogen, and oxygen atoms (CHO compounds) with molar mass less than 450 g mol-1 based on their molar mass and atomic O : C ratio. In this study, we refine and extend this method for CH and CHO compounds with molar mass up to ˜ 1100 g mol-1 using the number of carbon, hydrogen, and oxygen atoms. We predict viscosity from the Tg-scaled Arrhenius plot of fragility (viscosity vs. Tg/T) as a function of the fragility parameter D. We compiled D values of organic compounds from the literature and found that D approaches a lower limit of ˜ 10 (±1.7) as the molar mass increases. We estimated the viscosity of α-pinene and isoprene SOA as a function of RH by accounting for the hygroscopic growth of SOA and applying the Gordon-Taylor mixing rule, reproducing previously published experimental measurements very well. Sensitivity studies were conducted to evaluate impacts of Tg, D, the hygroscopicity parameter (κ), and the Gordon-Taylor constant on viscosity predictions. The viscosity of toluene SOA was predicted using the elemental composition obtained by high-resolution mass spectrometry (HRMS), resulting in a good agreement with the measured viscosity. We also estimated the viscosity of biomass burning particles using the chemical composition measured by HRMS with two different ionization techniques: electrospray ionization (ESI) and atmospheric pressure photoionization (APPI). Due to differences in detected organic compounds and signal intensity, predicted viscosities at low RH based on ESI and APPI measurements differ by 2-5 orders

  18. Analytical Modeling of the High Strain Rate Deformation of Polymer Matrix Composites

    Science.gov (United States)

    Goldberg, Robert K.; Roberts, Gary D.; Gilat, Amos

    2003-01-01

    The results presented here are part of an ongoing research program to develop strain rate dependent deformation and failure models for the analysis of polymer matrix composites subject to high strain rate impact loads. State variable constitutive equations originally developed for metals have been modified in order to model the nonlinear, strain rate dependent deformation of polymeric matrix materials. To account for the effects of hydrostatic stresses, which are significant in polymers, the classical 5 plasticity theory definitions of effective stress and effective plastic strain are modified by applying variations of the Drucker-Prager yield criterion. To verify the revised formulation, the shear and tensile deformation of a representative toughened epoxy is analyzed across a wide range of strain rates (from quasi-static to high strain rates) and the results are compared to experimentally obtained values. For the analyzed polymers, both the tensile and shear stress-strain curves computed using the analytical model correlate well with values obtained through experimental tests. The polymer constitutive equations are implemented within a strength of materials based micromechanics method to predict the nonlinear, strain rate dependent deformation of polymer matrix composites. In the micromechanics, the unit cell is divided up into a number of independently analyzed slices, and laminate theory is then applied to obtain the effective deformation of the unit cell. The composite mechanics are verified by analyzing the deformation of a representative polymer matrix composite (composed using the representative polymer analyzed for the correlation of the polymer constitutive equations) for several fiber orientation angles across a variety of strain rates. The computed values compare favorably to experimentally obtained results.

  19. Accuracy assessment of landslide prediction models

    International Nuclear Information System (INIS)

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

    2014-01-01

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

  20. Predictive models for fish assemblages in eastern USA streams: implications for assessing biodiversity

    Science.gov (United States)

    Meador, Michael R.; Carlisle, Daren M.

    2009-01-01

    Management and conservation of aquatic systems require the ability to assess biological conditions and identify changes in biodiversity. Predictive models for fish assemblages were constructed to assess biological condition and changes in biodiversity for streams sampled in the eastern United States as part of the U.S. Geological Survey's National Water Quality Assessment Program. Separate predictive models were developed for northern and southern regions. Reference sites were designated using land cover and local professional judgment. Taxonomic completeness was quantified based on the ratio of the number of observed native fish species expected to occur to the number of expected native fish species. Models for both regions accurately predicted fish species composition at reference sites with relatively high precision and low bias. In general, species that occurred less frequently than expected (decreasers) tended to prefer riffle areas and larger substrates, such as gravel and cobble, whereas increaser species (occurring more frequently than expected) tended to prefer pools, backwater areas, and vegetated and sand substrates. In the north, the percentage of species identified as increasers and the percentage identified as decreasers were equal, whereas in the south nearly two-thirds of the species examined were identified as decreasers. Predictive models of fish species can provide a standardized indicator for consistent assessments of biological condition at varying spatial scales and critical information for an improved understanding of fish species that are potentially at risk of loss with changing water quality conditions.

  1. A constitutive model for developing blood clots with various compositions and their nonlinear viscoelastic behavior.

    Science.gov (United States)

    van Kempen, Thomas H S; Donders, Wouter P; van de Vosse, Frans N; Peters, Gerrit W M

    2016-04-01

    The mechanical properties determine to a large extent the functioning of a blood clot. These properties depend on the composition of the clot and have been related to many diseases. However, the various involved components and their complex interactions make it difficult at this stage to fully understand and predict properties as a function of the components. Therefore, in this study, a constitutive model is developed that describes the viscoelastic behavior of blood clots with various compositions. Hereto, clots are formed from whole blood, platelet-rich plasma and platelet-poor plasma to study the influence of red blood cells, platelets and fibrin, respectively. Rheological experiments are performed to probe the mechanical behavior of the clots during their formation. The nonlinear viscoelastic behavior of the mature clots is characterized using a large amplitude oscillatory shear deformation. The model is based on a generalized Maxwell model that accurately describes the results for the different rheological experiments by making the moduli and viscosities a function of time and the past and current deformation. Using the same model with different parameter values enables a description of clots with different compositions. A sensitivity analysis is applied to study the influence of parameter variations on the model output. The relative simplicity and flexibility make the model suitable for numerical simulations of blood clots and other materials showing similar behavior.

  2. Development of mathematical model to predict the mechanical properties of friction stir

    Directory of Open Access Journals (Sweden)

    R. Palanivel

    2011-01-01

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

  3. Knowledge-based artificial neural network model to predict the properties of alpha+ beta titanium alloys

    Energy Technology Data Exchange (ETDEWEB)

    Banu, P. S. Noori; Rani, S. Devaki [Dept. of Metallurgical Engineering, Jawaharlal Nehru Technological University, HyderabadI (India)

    2016-08-15

    In view of emerging applications of alpha+beta titanium alloys in aerospace and defense, we have aimed to develop a Back propagation neural network (BPNN) model capable of predicting the properties of these alloys as functions of alloy composition and/or thermomechanical processing parameters. The optimized BPNN model architecture was based on the sigmoid transfer function and has one hidden layer with ten nodes. The BPNN model showed excellent predictability of five properties: Tensile strength (r: 0.96), yield strength (r: 0.93), beta transus (r: 0.96), specific heat capacity (r: 1.00) and density (r: 0.99). The developed BPNN model was in agreement with the experimental data in demonstrating the individual effects of alloying elements in modulating the above properties. This model can serve as the platform for the design and development of new alpha+beta titanium alloys in order to attain desired strength, density and specific heat capacity.

  4. Generalized Dissimilarity Modeling of Late-Quaternary Variations in Pollen-Based Compositional Dissimilarity

    Science.gov (United States)

    Williams, J. W.; Blois, J.; Ferrier, S.; Manion, G.; Fitzpatrick, M.; Veloz, S.; He, F.; Liu, Z.; Otto-Bliesner, B. L.

    2011-12-01

    In Quaternary paleoecology and paleoclimatology, compositionally dissimilar fossil assemblages usually indicate dissimilar environments; this relationship underpins assemblage-level techniques for paleoenvironmental reconstruction such as mutual climatic ranges or the modern analog technique. However, there has been relatively little investigation into the form of the relationship between compositional dissimilarity and climatic dissimilarity. Here we apply generalized dissimilarity modeling (GDM; Ferrier et al. 2007) as a tool for modeling the expected non-linear relationships between compositional and climatic dissimilarity. We use the CCSM3.0 transient paleoclimatic simulations from the SynTrace working group (Liu et al. 2009) and a new generation of fossil pollen maps from eastern North America (Blois et al. 2011) to 1) assess the spatial relationships between compositional dissimilarity and climatic dissimilarity and 2) whether these spatial relationships change over time. We used a taxonomic list of 106 genus-level pollen types, six climatic variables (winter precipitation and mean temperature, summer precipitation and temperature, seasonality of precipitation, and seasonality of temperature) that were chosen to minimize collinearity, and a cross-referenced pollen and climate dataset mapped for time slices spaced 1000 years apart. When GDM was trained for one time slice, the correlation between predicted and observed spatial patterns of community dissimilarity for other times ranged between 0.3 and 0.73. The selection of climatic predictor variables changed over time, as did the form of the relationship between compositional turnover and climatic predictors. Summer temperature was the only variable selected for all time periods. These results thus suggest that the relationship between compositional dissimilarity in pollen assemblages (and, by implication, beta diversity in plant communities) and climatic dissimilarity can change over time, for reasons to be

  5. Validating predictions made by a thermo-mechanical model of melt segregation in sub-volcanic systems

    Science.gov (United States)

    Roele, Katarina; Jackson, Matthew; Morgan, Joanna

    2014-05-01

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

  6. Mental models accurately predict emotion transitions.

    Science.gov (United States)

    Thornton, Mark A; Tamir, Diana I

    2017-06-06

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

  7. Mental models accurately predict emotion transitions

    Science.gov (United States)

    Thornton, Mark A.; Tamir, Diana I.

    2017-01-01

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

  8. Poisson Mixture Regression Models for Heart Disease Prediction.

    Science.gov (United States)

    Mufudza, Chipo; Erol, Hamza

    2016-01-01

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

  9. Poisson Mixture Regression Models for Heart Disease Prediction

    Science.gov (United States)

    Erol, Hamza

    2016-01-01

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

  10. Transverse Tensile Properties of 3 Dimension-4 Directional Braided Cf/SiC Composite Based on Double-Scale Model

    Science.gov (United States)

    Niu, Xuming; Sun, Zhigang; Song, Yingdong

    2017-11-01

    In this thesis, a double-scale model for 3 Dimension-4 directional(3D-4d) braided C/SiC composites(CMCs) has been proposed to investigate mechanical properties of it. The double-scale model involves micro-scale which takes fiber/matrix/porosity in fibers tows into consideration and the unit cell scale which considers the 3D-4d braiding structure. Basing on the Micro-optical photographs of composite, we can build a parameterized finite element model that reflects structure of 3D-4d braided composites. The mechanical properties of fiber tows in transverse direction are studied by combining the crack band theory for matrix cracking and cohesive zone model for interface debonding. Transverse tensile process of 3D-4d CMCs can be simulated by introducing mechanical properties of fiber tows into finite element of 3D-4d braided CMCs. Quasi-static tensile tests of 3D-4d braided CMCs have been performed with PWS-100 test system. The predicted tensile stress-strain curve by the double scale model finds good agreement with the experimental results.

  11. Comparisons of Faulting-Based Pavement Performance Prediction Models

    Directory of Open Access Journals (Sweden)

    Weina Wang

    2017-01-01

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

  12. Meso-modeling of Carbon Fiber Composite for Crash Safety Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Lin, Shih-Po; Chen, Yijung; Zeng, Danielle; Su, Xuming

    2017-04-06

    In the conventional approach, the material properties for crash safety simulations are typically obtained from standard coupon tests, where the test results only provide single layer material properties used in crash simulations. However, the lay-up effects for the failure behaviors of the real structure were not considered in numerical simulations. Hence, there was discrepancy between the crash simulations and experimental tests. Consequently, an intermediate stage is required for accurate predictions. Some component tests are required to correlate the material models in the intermediate stage. In this paper, a Mazda Tube under high-impact velocity is chosen as an example for the crash safety analysis. The tube consists of 24 layers of uni-directional (UD) carbon fiber composite materials, in which 4 layers are perpendicular to, while the other layers are parallel to the impact direction. An LS-DYNA meso-model was constructed with orthotropic material models counting for the single-layer material behaviors. Between layers, a node-based tie-break contact was used for modeling the delamination of the composite material. Since fiber directions are not single-oriented, the lay-up effects could be an important effect. From the first numerical trial, premature material failure occurred due to the use of material parameters obtained directly from the coupon tests. Some parametric studies were conducted to identify the cause of the numerical instability. The finding is that the material failure strength used in the numerical model needs to be enlarged to stabilize the numerical model. Some hypothesis was made to provide the foundation for enlarging the failure strength and the corresponding experiments will be conducted to validate the hypothesis.

  13. Viscoelastic characterization and self-heating behavior of laminated fiber composite driveshafts

    International Nuclear Information System (INIS)

    Henry, Todd C.; Bakis, Charles E.; Smith, Edward C.

    2015-01-01

    Highlights: • Carbon fiber composites with different matrix moduli were manufactured. • The composites are of interest for flexible driveshaft applications. • The composites are viscoelastically characterized using dynamic mechanical analysis. • The viscoelastic properties are used to predict self-heating in spinning shafts. • Measured and predicted temperatures of shafts agreed within 0.7 °C. - Abstract: The high cyclic strain capacity of fiber reinforced polymeric composites presents an opportunity to design driveshafts that can transmit high power under imperfect alignment conditions without the use of flexible couplers. In weight sensitive applications such as rotorcraft, the design of highly optimized driveshafts requires a general modeling capability that can predict a number of shaft performance characteristics—one of which is self-heating due to dynamic loading conditions. The current investigation developed three new flexible matrix composite materials of intermediate matrix modulus that, together with previously developed composites, cover the full range of material properties that are of potential interest in driveshaft design. An analytical model for the self-heating of spinning, misaligned, laminated composite shafts was refined to suit the full range of materials. Inputs to the model include ply-level dynamic material properties of the composite, cyclic strain amplitude and frequency, and various heat transfer constants related to conduction, radiation, and convection. Predictions of the surface temperature of spinning shafts correspond well with experimental measurements for bending strains of up to 2000 με, which encompasses the range of strains expected in rotorcraft driveshaft applications

  14. Unreachable Setpoints in Model Predictive Control

    DEFF Research Database (Denmark)

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

    2008-01-01

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

  15. Predictive model for the Dutch post-consumer plastic packaging recycling system and implications for the circular economy.

    Science.gov (United States)

    Brouwer, Marieke T; Thoden van Velzen, Eggo U; Augustinus, Antje; Soethoudt, Han; De Meester, Steven; Ragaert, Kim

    2018-01-01

    The Dutch post-consumer plastic packaging recycling network has been described in detail (both on the level of packaging types and of materials) from the household potential to the polymeric composition of the recycled milled goods. The compositional analyses of 173 different samples of post-consumer plastic packaging from different locations in the network were combined to indicatively describe the complete network with material flow analysis, data reconciliation techniques and process technological parameters. The derived potential of post-consumer plastic packages in the Netherlands in 2014 amounted to 341 Gg net (or 20.2 kg net.cap -1 .a -1 ). The complete recycling network produced 75.2 Gg milled goods, 28.1 Gg side products and 16.7 Gg process waste. Hence the net recycling chain yield for post-consumer plastic packages equalled 30%. The end-of-life fates for 35 different plastic packaging types were resolved. Additionally, the polymeric compositions of the milled goods and the recovered masses were derived with this model. These compositions were compared with experimentally determined polymeric compositions of recycled milled goods, which confirmed that the model predicts these compositions reasonably well. Also the modelled recovered masses corresponded reasonably well with those measured experimentally. The model clarified the origin of polymeric contaminants in recycled plastics, either sorting faults or packaging components, which gives directions for future improvement measures. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. 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: http://faculty.uaeu.ac.ae/nzaki/TMHindex.htm.

  17. A comparison of molecular dynamics and diffuse interface model predictions of Lennard-Jones fluid evaporation

    Energy Technology Data Exchange (ETDEWEB)

    Barbante, Paolo [Dipartimento di Matematica, Politecnico di Milano - Piazza Leonardo da Vinci 32 - 20133 Milano (Italy); Frezzotti, Aldo; Gibelli, Livio [Dipartimento di Scienze e Tecnologie Aerospaziali, Politecnico di Milano - Via La Masa 34 - 20156 Milano (Italy)

    2014-12-09

    The unsteady evaporation of a thin planar liquid film is studied by molecular dynamics simulations of Lennard-Jones fluid. The obtained results are compared with the predictions of a diffuse interface model in which capillary Korteweg contributions are added to hydrodynamic equations, in order to obtain a unified description of the liquid bulk, liquid-vapor interface and vapor region. Particular care has been taken in constructing a diffuse interface model matching the thermodynamic and transport properties of the Lennard-Jones fluid. The comparison of diffuse interface model and molecular dynamics results shows that, although good agreement is obtained in equilibrium conditions, remarkable deviations of diffuse interface model predictions from the reference molecular dynamics results are observed in the simulation of liquid film evaporation. It is also observed that molecular dynamics results are in good agreement with preliminary results obtained from a composite model which describes the liquid film by a standard hydrodynamic model and the vapor by the Boltzmann equation. The two mathematical model models are connected by kinetic boundary conditions assuming unit evaporation coefficient.

  18. A micromechanical four-phase model to predict the compressive failure surface of cement concrete

    Directory of Open Access Journals (Sweden)

    A. Caporale,

    2014-07-01

    Full Text Available In this work, a micromechanical model is used in order to predict the failure surface of cement concrete subject to multi-axial compression. In the adopted model, the concrete material is schematised as a composite with the following constituents: coarse aggregate (gravel, fine aggregate (sand and cement paste. The cement paste contains some voids which grow during the loading process. In fact, the non-linear behavior of the concrete is attributed to the creation of cracks in the cement paste; the effect of the cracks is taken into account by introducing equivalent voids (inclusions with zero stiffness in the cement paste. The three types of inclusions (namely gravel, sand and voids have different scales, so that the overall behavior of the concrete is obtained by the composition of three different homogenizations; in the sense that the concrete is regarded as the homogenized material of the two-phase composite constituted of the gravel and the mortar; in turn, the mortar is the homogenized material of the two-phase composite constituted of the sand inclusions and a (porous cement paste matrix; finally, the (porous cement paste is the homogenized material of the two-phase composite constituted of voids and the pure paste. The pure paste represents the cement paste before the loading process, so that it does not contain voids or other defects due to the loading process. The abovementioned three homogenizations are realized with the predictive scheme of Mori-Tanaka in conjunction with the Eshelby method. The adopted model can be considered an attempt to find micromechanical tools able to capture peculiar aspects of the cement concrete in load cases of uni-axial and multi-axial compression. Attributing the non-linear behavior of concrete to the creation of equivalent voids in the cement paste provides correspondence with many phenomenological aspects of concrete behavior. Trying to improve this correspondence, the influence of the parameters of the

  19. 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)

    2011-09-15

    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.)

  20. Modeling the curing process of thermosetting resin matrix composites

    Science.gov (United States)

    Loos, A. C.

    1986-01-01

    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.

  1. Modeling back-relaxation in ionic polymer metal composites: The role of steric effects and composite layers

    Science.gov (United States)

    Porfiri, Maurizio; Sharghi, Hesam; Zhang, Peng

    2018-01-01

    Ionic polymer metal composites (IPMCs) are a new class of active materials that are gaining traction as soft actuators in medical and industrial applications. IPMCs can undergo large deformations under modest voltage inputs, in dry and wet environments. Past studies have demonstrated that physical and geometric properties of all the IPMC constituents (ionomer, electrodes, and counterions) may all influence the time scales of the transient response and severity of the back-relaxation. In this study, we present a detailed mathematical model to investigate how the finite size of the counterions and the presence of metal particles in the vicinity of the electrodes modulate IPMC actuation. We build on previous work by our group on thermodynamically consistent modeling of IPMC mechanics and electrochemistry, which attributes IPMC actuation to the interplay between Maxwell stress and osmotic forces. To gain insight into the role of physical and geometric parameters, the resulting nonlinear partial differential equations are solved semianalytically using the method of matched asymptotic expansions, for the initial transient and the steady-state. A numerical solution in COMSOL Multiphysics® is developed to verify semianalytical findings and further explore IPMC actuation. Our model can successfully predict the entire response of IPMCs, from the initial bending toward the anode to the steady-state toward the cathode. We find that the steric effect can abolish the back-relaxation of IPMCs by restraining the counterions' concentration near the electrodes. We also find that increasing the thickness of the ionomer-metal composite layers may enhance IPMC actuation through increased osmotic forces and Maxwell stress.

  2. Model for the resistive critical current transition in composite superconductors

    International Nuclear Information System (INIS)

    Warnes, W.H.

    1988-01-01

    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

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

    Energy Technology Data Exchange (ETDEWEB)

    Whale, G. [and others

    2013-04-15

    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.

  4. Extensions of Island Biogeography Theory predict the scaling of functional trait composition with habitat area and isolation.

    Science.gov (United States)

    Jacquet, Claire; Mouillot, David; Kulbicki, Michel; Gravel, Dominique

    2017-02-01

    The Theory of Island Biogeography (TIB) predicts how area and isolation influence species richness equilibrium on insular habitats. However, the TIB remains silent about functional trait composition and provides no information on the scaling of functional diversity with area, an observation that is now documented in many systems. To fill this gap, we develop a probabilistic approach to predict the distribution of a trait as a function of habitat area and isolation, extending the TIB beyond the traditional species-area relationship. We compare model predictions to the body-size distribution of piscivorous and herbivorous fishes found on tropical reefs worldwide. We find that small and isolated reefs have a higher proportion of large-sized species than large and connected reefs. We also find that knowledge of species body-size and trophic position improves the predictions of fish occupancy on tropical reefs, supporting both the allometric and trophic theory of island biogeography. The integration of functional ecology to island biogeography is broadly applicable to any functional traits and provides a general probabilistic approach to study the scaling of trait distribution with habitat area and isolation. © 2016 John Wiley & Sons Ltd/CNRS.

  5. Load Composition Model Workflow (BPA TIP-371 Deliverable 1A)

    Energy Technology Data Exchange (ETDEWEB)

    Chassin, David P.; Cezar, Gustavo V.; /SLAC

    2017-07-17

    This project is funded under Bonneville Power Administration (BPA) Strategic Partnership Project (SPP) 17-005 between BPA and SLAC National Accelerator Laboratory. The project in a BPA Technology Improvement Project (TIP) that builds on and validates the Composite Load Model developed by the Western Electric Coordinating Council's (WECC) Load Modeling Task Force (LMTF). The composite load model is used by the WECC Modeling and Validation Work Group to study the stability and security of the western electricity interconnection. The work includes development of load composition data sets, collection of load disturbance data, and model development and validation. This work supports reliable and economic operation of the power system. This report was produced for Deliverable 1A of the BPA TIP-371 Project entitled \\TIP 371: Advancing the Load Composition Model". The deliverable documents the proposed work ow for the Composite Load Model, which provides the basis for the instrumentation, data acquisition, analysis and data dissemination activities addressed by later phases of the project.

  6. Compositional Reasoning in Early Childhood.

    Directory of Open Access Journals (Sweden)

    Steven Piantadosi

    Full Text Available Compositional "language of thought" models have recently been proposed to account for a wide range of children's conceptual and linguistic learning. The present work aims to evaluate one of the most basic assumptions of these models: children should have an ability to represent and compose functions. We show that 3.5-4.5 year olds are able to predictively compose two novel functions at significantly above chance levels, even without any explicit training or feedback on the composition itself. We take this as evidence that children at this age possess some capacity for compositionality, consistent with models that make this ability explicit, and providing an empirical challenge to those that do not.

  7. Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

    Science.gov (United States)

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

    2016-01-01

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

  8. Risk terrain modeling predicts child maltreatment.

    Science.gov (United States)

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

    2016-12-01

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

  9. Case studies in archaeological predictive modelling

    NARCIS (Netherlands)

    Verhagen, Jacobus Wilhelmus Hermanus Philippus

    2007-01-01

    In this thesis, a collection of papers is put together dealing with various quantitative aspects of predictive modelling and archaeological prospection. Among the issues covered are the effects of survey bias on the archaeological data used for predictive modelling, and the complexities of testing

  10. Predicting performance of polymer-bonded Terfenol-D composites under different magnetic fields

    International Nuclear Information System (INIS)

    Guan Xinchun; Dong Xufeng; Ou Jinping

    2009-01-01

    Considering demagnetization effect, the model used to calculate the magnetostriction of the single particle under the applied field is first created. Based on Eshelby equivalent inclusion and Mori-Tanaka method, the approach to calculate the average magnetostriction of the composites under any applied field, as well as the saturation, is studied by treating the magnetostriction particulate as an eigenstrain. The results calculated by the approach indicate that saturation magnetostriction of magnetostrictive composites increases with an increase of particle aspect and particle volume fraction, and a decrease of Young's modulus of the matrix. The influence of an applied field on magnetostriction of the composites becomes more significant with larger particle volume fraction or particle aspect. Experiments were done to verify the effectiveness of the model, the results of which indicate that the model only can provide approximate results.

  11. Development of a modified equilibrium model for biomass pilot-scale fluidized bed gasifier performance predictions

    International Nuclear Information System (INIS)

    Rodriguez-Alejandro, David A.; Nam, Hyungseok; Maglinao, Amado L.; Capareda, Sergio C.; Aguilera-Alvarado, Alberto F.

    2016-01-01

    The objective of this work is to develop a thermodynamic model considering non-stoichiometric restrictions. The model validation was done from experimental works using a bench-scale fluidized bed gasifier with wood chips, dairy manure, and sorghum. The model was used for a further parametric study to predict the performance of a pilot-scale fluidized biomass gasifier. The Gibbs free energy minimization was applied to the modified equilibrium model considering a heat loss to the surroundings, carbon efficiency, and two non-equilibrium factors based on empirical correlations of ER and gasification temperature. The model was in a good agreement with RMS <4 for the produced gas. The parametric study ranges were 0.01 < ER < 0.99 and 500 °C < T < 900 °C to predict syngas concentrations and its LHV (lower heating value) for the optimization. Higher aromatics in tar were contained in WC gasification compared to manure gasification. A wood gasification tar simulation was produced to predict the amount of tars at specific conditions. The operating conditions for the highest quality syngas were reconciled experimentally with three biomass wastes using a fluidized bed gasifier. The thermodynamic model was used to predict the gasification performance at conditions beyond the actual operation. - Highlights: • Syngas from experimental gasification was used to create a non-equilibrium model. • Different types of biomass (HTS, DM, and WC) were used for gasification modelling. • Different tar compositions were identified with a simulation of tar yields. • The optimum operating conditions were found through the developed model.

  12. A Model to Predict the Steady-State Concentration of Hydroxyl Radicals in the Surface Layer of Natural Waters

    International Nuclear Information System (INIS)

    Minero, C.; Lauri, V.; Maurino, V.; Pelizzetti, E.; Vione, D.

    2007-01-01

    A model was developed to predict the steady-state [·OH] in the surface layer of natural waters as a function of nitrate, inorganic carbon (IC) and dissolved organic matter (DOM). The parameter values were studied in the range detected in shallow high-mountain lakes, to which the model results are most relevant. Calculations indicate that [·OH] increases with increasing nitrate and decreasing IC, and conditions are also identified where [·OH] is directly proportional, inversely proportional or independent of DOM. Based on the model results it is possible to predict the half-life time, due to reaction with ·OH, of given dissolved compounds, including organic pollutants, from the water composition data

  13. Intelligent processing for thick composites

    Science.gov (United States)

    Shin, Daniel Dong-Ok

    2000-10-01

    Manufacturing thick composite parts are associated with adverse curing conditions such as large in-plane temperature gradient and exotherms. The condition is further aggravated because the manufacturer's cycle and the existing cure control systems do not adequately counter such affects. In response, the forecast-based thermal control system is developed to have better cure control for thick composites. Accurate cure kinetic model is crucial for correctly identifying the amount of heat generated for composite process simulation. A new technique for identifying cure parameters for Hercules AS4/3502 prepreg is presented by normalizing the DSC data. The cure kinetics is based on an autocatalytic model for the proposed method, which uses dynamic and isothermal DSC data to determine its parameters. Existing models are also used to determine kinetic parameters but rendered inadequate because of the material's temperature dependent final degree of cure. The model predictions determined from the new technique showed good agreement to both isothermal and dynamic DSC data. The final degree of cure was also in good agreement with experimental data. A realistic cure simulation model including bleeder ply analysis and compaction is validated with Hercules AS4/3501-6 based laminates. The nonsymmetrical temperature distribution resulting from the presence of bleeder plies agreed well to the model prediction. Some of the discrepancies in the predicted compaction behavior were attributed to inaccurate viscosity and permeability models. The temperature prediction was quite good for the 3cm laminate. The validated process simulation model along with cure kinetics model for AS4/3502 prepreg were integrated into the thermal control system. The 3cm Hercules AS4/3501-6 and AS4/3502 laminate were fabricated. The resulting cure cycles satisfied all imposed requirements by minimizing exotherms and temperature gradient. Although the duration of the cure cycles increased, such phenomena was

  14. Three-Phase Carbon Fiber Amine Functionalized Carbon Nanotubes Epoxy Composite: Processing, Characterisation, and Multiscale Modeling

    Directory of Open Access Journals (Sweden)

    Kamal Sharma

    2014-01-01

    Full Text Available The present paper discusses the key issues of carbon nanotube (CNT dispersion and effect of functionalisation on the mechanical properties of multiscale carbon epoxy composites. In this study, CNTs were added in epoxy matrix and further reinforced with carbon fibres. Predetermined amounts of optimally amine functionalised CNTs were dispersed in epoxy matrix, and unidirectional carbon fiber laminates were produced. The effect of the presence of CNTs (1.0 wt% in the resin was reflected by pronounced increase in Young’s modulus, inter-laminar shear strength, and flexural modulus by 51.46%, 39.62%, and 38.04%, respectively. However, 1.5 wt% CNT loading in epoxy resin decreased the overall properties of the three-phase composites. A combination of Halpin-Tsai equations and micromechanics modeling approach was also used to evaluate the mechanical properties of multiscale composites and the differences between the predicted and experimental values are reported. These multiscale composites are likely to be used for potential missile and aerospace structural applications.

  15. Predicting the uptake of Cs, Co, Ni, Eu, Th and U on argillaceous rocks using sorption models for illite

    International Nuclear Information System (INIS)

    Marques Fernandes, Maria; Vér, Nóra; Baeyens, Bart

    2015-01-01

    Highlights: • Contaminant retention in argillaceous rocks controlled by sorption on clay minerals. • Cs, Ni, Co, Eu, Th and UO 2 sorption isotherm measurements on Boda and Opalinus Clay. • Boda and Opalinus Clay exhibit different mineralogies and porewater compositions. • Blind predictions using quasi-mechanistic sorption models developed for illite. • Good agreement between measurements and blind predictions. - Abstract: Reliable predictions of radiocontaminant migration are a requirement for the establishment of radioactive waste repositories. Parametrization of the necessary sorption models seems to be, however, extremely challenging given the multi-mineralic composition of the lithosphere. In this study it is shown for two argillaceous rocks – Boda and Opalinus Clay relevant for the Hungarian and Swiss repository concepts, respectively – that this task can be substantially simplified by taking into account only the most sorptive mineral fraction, namely the 2:1 clay minerals illite and illite/smectite mixed layers. Two different models were required to blind predict the sorption isotherms of Cs, Co, Ni, Eu, Th and UO 2 measured on the two clay rock samples in a synthetic porewater. Cs sorption was modelled with the generalised Cs (GCs) sorption model and the sorption of the other cations with the 2site protolysis non electrostatic surface complexation and cation exchange (2SPNE SC/CE) model. The 2SPNE SC/CE model for illite was extended with surface complexation reactions on weak sites for Co, Ni, Eu, UO 2 and on strong sites for Eu-carbonato complexes. Complementary to the sorption measurements and modelling, extended X-ray absorption fine structure (EXAFS) spectroscopy was used to probe the retention mechanism of Ni on illite, Boda and Opalinus Clay at higher loadings. The reliable blind predictions of the selected metal cations, which are representative for monovalent alkaline metals, divalent transition metals, lanthanides, and trivalent

  16. Fingerprint verification prediction model in hand dermatitis.

    Science.gov (United States)

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

    2015-07-01

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

  17. Multi-Scale Modeling of an Integrated 3D Braided Composite with Applications to Helicopter Arm

    Science.gov (United States)

    Zhang, Diantang; Chen, Li; Sun, Ying; Zhang, Yifan; Qian, Kun

    2017-10-01

    A study is conducted with the aim of developing multi-scale analytical method for designing the composite helicopter arm with three-dimensional (3D) five-directional braided structure. Based on the analysis of 3D braided microstructure, the multi-scale finite element modeling is developed. Finite element analysis on the load capacity of 3D five-directional braided composites helicopter arm is carried out using the software ABAQUS/Standard. The influences of the braiding angle and loading condition on the stress and strain distribution of the helicopter arm are simulated. The results show that the proposed multi-scale method is capable of accurately predicting the mechanical properties of 3D braided composites, validated by the comparison the stress-strain curves of meso-scale RVCs. Furthermore, it is found that the braiding angle is an important factor affecting the mechanical properties of 3D five-directional braided composite helicopter arm. Based on the optimized structure parameters, the nearly net-shaped composite helicopter arm is fabricated using a novel resin transfer mould (RTM) process.

  18. Mechanistic model to predict colostrum intake based on deuterium oxide dilution technique data and impact of gestation and prefarrowing diets on piglet intake and sow yield of colostrum

    DEFF Research Database (Denmark)

    Theil, Peter Kappel; Flummer, Christine; Hurley, W L

    2014-01-01

    The aims of the present study were to quantify colostrum intake (CI) of piglets using the D2O dilution technique, to develop a mechanistic model to predict CI, to compare these data with CI predicted by a previous empirical predictive model developed for bottle-fed piglets, and to study how...... composition of diets fed to gestating sows affected piglet CI, sow colostrum yield (CY), and colostrum composition. In total, 240 piglets from 40 litters were enriched with D2O. The CI measured by D2O from birth until 24 h after the birth of first-born piglet was on average 443 g (SD 151). Based on measured...... CI, a mechanistic model to predict CI was developed using piglet characteristics (24-h weight gain [WG; g], BW at birth [BWB; kg], and duration of CI [D; min]: CI, g = –106 + 2.26 WG + 200 BWB + 0.111 D – 1,414 WG/D + 0.0182 WG/BWB (R2 = 0.944). This model was used to predict the CI for all colostrum...

  19. Photon transitions between baryons in a new hadron scheme. [Composite model, Dirac spinor

    Energy Technology Data Exchange (ETDEWEB)

    Sugimoto, H [Kanazawa Inst. of Tech. (Japan); Toya, M

    1976-05-01

    Photon transitions from the ground state baryon to the nonstrange excited one with L=0, 1 and 2 are investigated. The discussion is based on a new scheme of hadron composite model which takes account of the lower component of the Dirac spinor of constituent particles even in a hadron rest system. There appear generally four independent model amplitudes. Each process ..gamma..+p..-->..B/sub 8/* is described in terms of an individual model amplitude. This helps us to explain the characteristic features of experiment and solve the troubles found in the nonrelativistic scheme. Both magnitudes and signs of predicted amplitudes are shown to be in good agreement with experimental data. From this comparison the specific features of the model amplitudes are found. Discussion is made for higher excited baryons.

  20. Finding Furfural Hydrogenation Catalysts via Predictive Modelling.

    Science.gov (United States)

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

    2010-09-10

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

  1. 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...

  2. Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

    Directory of Open Access Journals (Sweden)

    Peek Andrew S

    2007-06-01

    Full Text Available Abstract Background RNA interference (RNAi is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM approach was used to quantitatively model RNA interference activities. Results Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (N-grams and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative. Conclusion The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall t-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid

  3. Multiscale Modeling of Ceramic Matrix Composites

    Science.gov (United States)

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

    2015-01-01

    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.

  4. Improved lumped models for transient combined convective and radiative cooling of multi-layer composite slabs

    International Nuclear Information System (INIS)

    An Chen; Su Jian

    2011-01-01

    Improved lumped parameter models were developed for the transient heat conduction in multi-layer composite slabs subjected to combined convective and radiative cooling. The improved lumped models were obtained through two-point Hermite approximations for integrals. Transient combined convective and radiative cooling of three-layer composite slabs was analyzed to illustrate the applicability of the proposed lumped models, with respect to different values of the Biot numbers, the radiation-conduction parameter, the dimensionless thermal contact resistances, the dimensionless thickness, and the dimensionless thermal conductivity. It was shown by comparison with numerical solution of the original distributed parameter model that the higher order lumped model (H 1,1 /H 0,0 approximation) yielded significant improvement of average temperature prediction over the classical lumped model. In addition, the higher order (H 1,1 /H 0,0 ) model was applied to analyze the transient heat conduction problem of steel-concrete-steel sandwich plates. - Highlights: → Improved lumped models for convective-radiative cooling of multi-layer slabs were developed. → Two-point Hermite approximations for integrals were employed. → Significant improvement over classical lumped model was achieved. → The model can be applied to high Biot number and high radiation-conduction parameter. → Transient heat conduction in steel-concrete-steel sandwich pipes was analyzed as an example.

  5. Micromechanical Modeling of Fiber-Reinforced Composites with Statistically Equivalent Random Fiber Distribution

    Directory of Open Access Journals (Sweden)

    Wenzhi Wang

    2016-07-01

    Full Text Available Modeling the random fiber distribution of a fiber-reinforced composite is of great importance for studying the progressive failure behavior of the material on the micro scale. In this paper, we develop a new algorithm for generating random representative volume elements (RVEs with statistical equivalent fiber distribution against the actual material microstructure. The realistic statistical data is utilized as inputs of the new method, which is archived through implementation of the probability equations. Extensive statistical analysis is conducted to examine the capability of the proposed method and to compare it with existing methods. It is found that the proposed method presents a good match with experimental results in all aspects including the nearest neighbor distance, nearest neighbor orientation, Ripley’s K function, and the radial distribution function. Finite element analysis is presented to predict the effective elastic properties of a carbon/epoxy composite, to validate the generated random representative volume elements, and to provide insights of the effect of fiber distribution on the elastic properties. The present algorithm is shown to be highly accurate and can be used to generate statistically equivalent RVEs for not only fiber-reinforced composites but also other materials such as foam materials and particle-reinforced composites.

  6. Experimental and theoretical assessment of flexural properties of hybrid natural fibre composites

    DEFF Research Database (Denmark)

    Raghavalu Thirumalai, Durai Prabhakaran; Toftegaard, Helmuth Langmaack; Markussen, Christen Malte

    2014-01-01

    The concept of hybridization of natural fibre composites with synthetic fibres is attracting increasing scientific attention. The present study addresses the flexural properties of hybrid flax/glass/epoxy composites to demonstrate the potential benefits of hybridization. The study covers both...... experimental and theoretical assessments. Composite laminates with different hybrid fibre mixing ratios and different layer configurations were manufactured, and their volumetric composition and flexural properties were measured. The relationship between volume fractions in the composites is shown to be well...... predicted as a function of the hybrid fibre mixing ratio. The flexural modulus of the composites is theoretically assessed by using micromechanical models and laminate theory. The model predictions are compared with the experimentally determined flexural properties. Both approaches show that the flexural...

  7. 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 ...

  8. Heat transfer models for predicting Salmonella enteritidis in shell eggs through supply chain distribution.

    Science.gov (United States)

    Almonacid, S; Simpson, R; Teixeira, A

    2007-11-01

    Egg and egg preparations are important vehicles for Salmonella enteritidis infections. The influence of time-temperature becomes important when the presence of this organism is found in commercial shell eggs. A computer-aided mathematical model was validated to estimate surface and interior temperature of shell eggs under variable ambient and refrigerated storage temperature. A risk assessment of S. enteritidis based on the use of this model, coupled with S. enteritidis kinetics, has already been reported in a companion paper published earlier in JFS. The model considered the actual geometry and composition of shell eggs and was solved by numerical techniques (finite differences and finite elements). Parameters of interest such as local (h) and global (U) heat transfer coefficient, thermal conductivity, and apparent volumetric specific heat were estimated by an inverse procedure from experimental temperature measurement. In order to assess the error in predicting microbial population growth, theoretical and experimental temperatures were applied to a S. enteritidis growth model taken from the literature. Errors between values of microbial population growth calculated from model predicted compared with experimentally measured temperatures were satisfactorily low: 1.1% and 0.8% for the finite difference and finite element model, respectively.

  9. Thermophysical characterization tools and numerical models for high temperature thermo-structural composite materials

    International Nuclear Information System (INIS)

    Lorrette, Ch.

    2007-04-01

    This work is an original contribution to the study of the thermo-structural composite materials thermal behaviour. It aims to develop a methodology with a new experimental device for thermal characterization adapted to this type of material and to model the heat transfer by conduction within these heterogeneous media. The first part deals with prediction of the thermal effective conductivity of stratified composite materials in the three space directions. For that, a multi scale model using a rigorous morphology analysis of the structure and the elementary properties is proposed and implemented. The second part deals with the thermal characterization at high temperature. It shows how to estimate simultaneously the thermal effusiveness and the thermal conductivity. The present method is based on the observation of the heating from a plane sample submitted to a continuous excitation generated by Joule Effect. Heat transfer is modelled with the quadrupole formalism, temperature is here measured on two sides of the sample. The development of both resistive probes for excitation and linear probes for temperature measurements enables the thermal properties measured up to 1000 C. Finally, some experimental and numerical application examples lead to review the obtained results. (author)

  10. Prediction skill of rainstorm events over India in the TIGGE weather prediction models

    Science.gov (United States)

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

    2017-12-01

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

  11. Predicting the glass transition temperature and viscosity of secondary organic material using molecular composition

    Directory of Open Access Journals (Sweden)

    W.-S. W. DeRieux

    2018-05-01

    Full Text Available Secondary organic aerosol (SOA accounts for a large fraction of submicron particles in the atmosphere. SOA can occur in amorphous solid or semi-solid phase states depending on chemical composition, relative humidity (RH, and temperature. The phase transition between amorphous solid and semi-solid states occurs at the glass transition temperature (Tg. We have recently developed a method to estimate Tg of pure compounds containing carbon, hydrogen, and oxygen atoms (CHO compounds with molar mass less than 450 g mol−1 based on their molar mass and atomic O : C ratio. In this study, we refine and extend this method for CH and CHO compounds with molar mass up to ∼ 1100 g mol−1 using the number of carbon, hydrogen, and oxygen atoms. We predict viscosity from the Tg-scaled Arrhenius plot of fragility (viscosity vs. Tg∕T as a function of the fragility parameter D. We compiled D values of organic compounds from the literature and found that D approaches a lower limit of ∼ 10 (±1.7 as the molar mass increases. We estimated the viscosity of α-pinene and isoprene SOA as a function of RH by accounting for the hygroscopic growth of SOA and applying the Gordon–Taylor mixing rule, reproducing previously published experimental measurements very well. Sensitivity studies were conducted to evaluate impacts of Tg, D, the hygroscopicity parameter (κ, and the Gordon–Taylor constant on viscosity predictions. The viscosity of toluene SOA was predicted using the elemental composition obtained by high-resolution mass spectrometry (HRMS, resulting in a good agreement with the measured viscosity. We also estimated the viscosity of biomass burning particles using the chemical composition measured by HRMS with two different ionization techniques: electrospray ionization (ESI and atmospheric pressure photoionization (APPI. Due to differences in detected organic compounds and signal intensity, predicted viscosities at low RH based on ESI and

  12. Predicting climate-induced range shifts: model differences and model reliability.

    Science.gov (United States)

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

    2006-01-01

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

  13. [Predicting value of 2014 European guidelines risk prediction model for sudden cardiac death (HCM Risk-SCD) in Chinese patients with hypertrophic cardiomyopathy].

    Science.gov (United States)

    Li, W X; Liu, L W; Wang, J; Zuo, L; Yang, F; Kang, N; Lei, C H

    2017-12-24

    Objective: To evaluate the predicting value of the 2014 European Society of Cardiology (ESC) guidelines risk prediction model for sudden cardiac death (HCM Risk-SCD) in Chinese patients with hypertrophic cardiomyopathy (HCM), and to explore the predictors of adverse cardiovascular events in Chinese HCM patients. Methods: The study population consisted of a consecutive 207 HCM patients admitted in our center from October 2014 to October 2016. All patients were followed up to March 2017. The 5-year SCD probability of each patient was estimated using HCM Risk-SCD model based on electrocardiogram, echocardiography and cardiac magnetic resonance (CMR) examination results. The primary, second, and composite endpoints were recorded. The primary endpoint included SCD and appropriate ICD therapy, identical to the HCM Risk-SCD endpoint. The second endpoint included acute myocardial infarction, hospitalization for heart failure, thrombus embolism and end-stage HCM. The composite endpoint was either the primary or the second endpoint. Patients were divided into the 3 categories according to 5-year SCD probability assessed by HCM Risk-SCD model: low risk grouprisk group ≥4% torisk group≥6%. Results: (1) Prevalence of endpoints: All 207 HCM patients completed the follow-up (350 (230, 547) days). During follow-up, 8 (3.86%) patients reached the primary endpoints (3 cases of SCD, 3 cases of survival after defibrillation, and 2 cases of appropriate ICD discharge); 21 (10.14%) patients reached the second endpoints (1 case of acute myocardial infarction, 16 cases of heart failure hospitalization, 2 cases of thromboembolism, and 2 cases of end-stage HCM). (2) Predicting value of HCM Risk-SCD model: Patients with primary endpoints had higher prevalence of syncope and intermediate-high risk of 5-year SCD, as compared to those without primary endpoints (both Pvalue of HCM Risk-SCD model: The low risk group included 122 patients (59%), the intermediate risk group 42 (20%), and the

  14. Predictive Modeling of a Paradigm Mechanical Cooling Tower Model: II. Optimal Best-Estimate Results with Reduced Predicted Uncertainties

    Directory of Open Access Journals (Sweden)

    Ruixian Fang

    2016-09-01

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

  15. Progress in Space Weather Modeling and Observations Needed to Improve the Operational NAIRAS Model Aircraft Radiation Exposure Predictions

    Science.gov (United States)

    Mertens, C. J.; Kress, B. T.; Wiltberger, M. J.; Tobiska, W.; Xu, X.

    2011-12-01

    The Nowcast of Atmospheric Ionizing Radiation for Aviation Safety (NAIRAS) is a prototype operational model for predicting commercial aircraft radiation exposure from galactic and solar cosmic rays. NAIRAS predictions are currently streaming live from the project's public website, and the exposure rate nowcast is also available on the SpaceWx smartphone app for iPhone, IPad, and Android. Cosmic rays are the primary source of human exposure to high linear energy transfer radiation at aircraft altitudes, which increases the risk of cancer and other adverse health effects. Thus, the NAIRAS model addresses an important national need with broad societal, public health and economic benefits. The processes responsible for the variability in the solar wind, interplanetary magnetic field, solar energetic particle spectrum, and the dynamical response of the magnetosphere to these space environment inputs, strongly influence the composition and energy distribution of the atmospheric ionizing radiation field. During the development of the NAIRAS model, new science questions were identified that must be addressed in order to obtain a more reliable and robust operational model of atmospheric radiation exposure. Addressing these science questions require improvements in both space weather modeling and observations. The focus of this talk is to present these science questions, the proposed methodologies for addressing these science questions, and the anticipated improvements to the operational predictions of atmospheric radiation exposure. The overarching goal of this work is to provide a decision support tool for the aviation industry that will enable an optimal balance to be achieved between minimizing health risks to passengers and aircrew while simultaneously minimizing costs to the airline companies.

  16. Model predictive control classical, robust and stochastic

    CERN Document Server

    Kouvaritakis, Basil

    2016-01-01

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

  17. Analysis of composition and microstructural uniformity of hybrid glass/carbon fibre composites

    Energy Technology Data Exchange (ETDEWEB)

    Beauson, J.; Markussen, C.M.; Madsen, Bo

    2013-09-01

    In hybrid fibre composites, the intermixing of the two types of fibres imposes challenges to obtain materials with a well-defined and uniform microstructure. In the present paper, the composition and the microstructural uniformity of hybrid glass/carbon fibre composites mixed at the fibre bundle level are investigated. The different levels of compositions in the composites are defined and experimentally determined. The composite volume fractions are determined using an image analysis based procedure. The global fibre volume fractions are determined using a gravimetrical based method. The local fibre volume fractions are determined using volumetric calculations. A model is presented to predict the interrelation of volume fractions in hybrid fibre composites. The microstructural uniformity of the composites is analysed by the determined variation in composite volume fractions. Two analytical methods, a standard deviation based method and a fast Fourier transform method, are used to quantify the difference in microstructural uniformity between composites, and to detect and quantify any repeating pattern in the composite microstructure. (Author)

  18. Model predictive Controller for Mobile Robot

    OpenAIRE

    Alireza Rezaee

    2017-01-01

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

  19. Deep Predictive Models in Interactive Music

    OpenAIRE

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

    2018-01-01

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

  20. Prediction of extubation outcome in preterm infants by composite extubation indices.

    Science.gov (United States)

    Dimitriou, Gabriel; Fouzas, Sotirios; Vervenioti, Aggeliki; Tzifas, Sotirios; Mantagos, Stefanos

    2011-11-01

    To determine whether composite extubation indices can predict extubation outcome in preterm infants. Prospective observational study. Level III neonatal intensive care unit. Fifty-six preterm infants cared for in the neonatal intensive care unit of a tertiary teaching hospital during 2007 and 2008. None. The study consisted of two parts. In the first part, different extubation indices were evaluated in a group of 28 neonates (derivation group). These indices included the diaphragmatic pressure-time index, the respiratory muscle pressure-time index, the maximal transdiaphragmatic pressure, the maximal inspiratory pressure, the airway pressure generated 100 milliseconds after an occlusion/maximal transdiaphragmatic pressure ratio, the airway pressure generated 100 milliseconds after an occlusion/maximal inspiratory pressure ratio, the tidal volume, and the respiratory rate to tidal volume ratio. After exploratory analysis, the best performing indices and the optimal threshold values to predict extubation outcome were selected. In the second part of the study, these indices were validated at the predetermined threshold values in an additional group of 28 preterm neonates (validation group). Four infants (14.3%) in the derivation group and four in the validation group (14.3%) failed extubation. Receiver operator characteristic curve analysis revealed that a diaphragmatic pressure-time index of ≤0.12, a respiratory muscle pressure-time index ≤0.10, a airway pressure generated 100 milliseconds after an occlusion/maximal transdiaphragmatic pressure of ≤0.14, and a airway pressure generated 100 milliseconds after an occlusion/maximal inspiratory pressure of ≤0.09 were the most accurate predictors of extubation outcome in the derivation group. In the validation group, a diaphragmatic pressure-time index of ≤0.12 and a respiratory muscle pressure-time index of ≤0.10 both had zero false-positive results, predicting with accuracy successful extubation. Composite

  1. Risk prediction model: Statistical and artificial neural network approach

    Science.gov (United States)

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

    2017-04-01

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

  2. Evaluation of CASP8 model quality predictions

    KAUST Repository

    Cozzetto, Domenico

    2009-01-01

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

  3. Changes in mangrove species assemblages and future prediction of the Bangladesh Sundarbans using Markov chain model and cellular automata.

    Science.gov (United States)

    Mukhopadhyay, Anirban; Mondal, Parimal; Barik, Jyotiskona; Chowdhury, S M; Ghosh, Tuhin; Hazra, Sugata

    2015-06-01

    The composition and assemblage of mangroves in the Bangladesh Sundarbans are changing systematically in response to several environmental factors. In order to understand the impact of the changing environmental conditions on the mangrove forest, species composition maps for the years 1985, 1995 and 2005 were studied. In the present study, 1985 and 1995 species zonation maps were considered as base data and the cellular automata-Markov chain model was run to predict the species zonation for the year 2005. The model output was validated against the actual dataset for 2005 and calibrated. Finally, using the model, mangrove species zonation maps for the years 2025, 2055 and 2105 have been prepared. The model was run with the assumption that the continuation of the current tempo and mode of drivers of environmental factors (temperature, rainfall, salinity change) of the last two decades will remain the same in the next few decades. Present findings show that the area distribution of the following species assemblages like Goran (Ceriops), Sundari (Heritiera), Passur (Xylocarpus), and Baen (Avicennia) would decrease in the descending order, whereas the area distribution of Gewa (Excoecaria), Keora (Sonneratia) and Kankra (Bruguiera) dominated assemblages would increase. The spatial distribution of projected mangrove species assemblages shows that more salt tolerant species will dominate in the future; which may be used as a proxy to predict the increase of salinity and its spatial variation in Sundarbans. Considering the present rate of loss of forest land, 17% of the total mangrove cover is predicted to be lost by the year 2105 with a significant loss of fresh water loving mangroves and related ecosystem services. This paper describes a unique approach to assess future changes in species composition and future forest zonation in mangroves under the 'business as usual' scenario of climate change.

  4. Predictive models of moth development

    Science.gov (United States)

    Degree-day models link ambient temperature to insect life-stages, making such models valuable tools in integrated pest management. These models increase management efficacy by predicting pest phenology. In Wisconsin, the top insect pest of cranberry production is the cranberry fruitworm, Acrobasis v...

  5. Model Prediction Control For Water Management Using Adaptive Prediction Accuracy

    NARCIS (Netherlands)

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

    2014-01-01

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

  6. Prediction of microsegregation and pitting corrosion resistance of austenitic stainless steel welds by modelling

    Energy Technology Data Exchange (ETDEWEB)

    Vilpas, M. [VTT Manufacturing Technology, Espoo (Finland). Materials and Structural Integrity

    1999-07-01

    The present study focuses on the ability of several computer models to accurately predict the solidification, microsegregation and pitting corrosion resistance of austenitic stainless steel weld metals. Emphasis was given to modelling the effect of welding speed on solute redistribution and ultimately to the prediction of weld pitting corrosion resistance. Calculations were experimentally verified by applying autogenous GTA- and laser processes over the welding speed range of 0.1 to 5 m/min for several austenitic stainless steel grades. Analytical and computer aided models were applied and linked together for modelling the solidification behaviour of welds. The combined use of macroscopic and microscopic modelling is a unique feature of this work. This procedure made it possible to demonstrate the effect of weld pool shape and the resulting solidification parameters on microsegregation and pitting corrosion resistance. Microscopic models were also used separately to study the role of welding speed and solidification mode in the development of microsegregation and pitting corrosion resistance. These investigations demonstrate that the macroscopic model can be implemented to predict solidification parameters that agree well with experimentally measured values. The linked macro-micro modelling was also able to accurately predict segregation profiles and CPT-temperatures obtained from experiments. The macro-micro simulations clearly showed the major roles of weld composition and welding speed in determining segregation and pitting corrosion resistance while the effect of weld shape variations remained negligible. The microscopic dendrite tip and interdendritic models were applied to welds with good agreement with measured segregation profiles. Simulations predicted that weld inhomogeneity can be substantially decreased with increasing welding speed resulting in a corresponding improvement in the weld pitting corrosion resistance. In the case of primary austenitic

  7. Predicting water main failures using Bayesian model averaging and survival modelling approach

    International Nuclear Information System (INIS)

    Kabir, Golam; Tesfamariam, Solomon; Sadiq, Rehan

    2015-01-01

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

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

    Science.gov (United States)

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

    2015-09-01

    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

  9. Combinatory Models for Predicting the Effective Thermal Conductivity of Frozen and Unfrozen Food Materials

    Directory of Open Access Journals (Sweden)

    K. S. Reddy

    2010-01-01

    Full Text Available 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. The models are compared with the experimental data of various foods at the initial freezing temperature. The effective thermal conductivity estimated by the Maxwell-Eucken-I + Present model shows good agreement with the experimental data with a minimum average deviation of ±8.66% and maximum deviation of ±42.76% of Series + Present Model. The combined models have advantages over other empirical and semiempirical models.

  10. Composite Higgs Models and the tt-bar H Channel

    International Nuclear Information System (INIS)

    Carmona, A.; Chala, M.; Santiago, J.

    2012-01-01

    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)

  11. Dynamic shear-lag model for understanding the role of matrix in energy dissipation in fiber-reinforced composites.

    Science.gov (United States)

    Liu, Junjie; Zhu, Wenqing; Yu, Zhongliang; Wei, Xiaoding

    2018-07-01

    Lightweight and high impact performance composite design is a big challenge for scientists and engineers. Inspired from well-known biological materials, e.g., the bones, spider silk, and claws of mantis shrimp, artificial composites have been synthesized for engineering applications. Presently, the design of ballistic resistant composites mainly emphasizes the utilization of light and high-strength fibers, whereas the contribution from matrix materials receives less attention. However, recent ballistic experiments on fiber-reinforced composites challenge our common sense. The use of matrix with "low-grade" properties enhances effectively the impact performance. In this study, we establish a dynamic shear-lag model to explore the energy dissipation through viscous matrix materials in fiber-reinforced composites and the associations of energy dissipation characteristics with the properties and geometries of constituents. The model suggests that an enhancement in energy dissipation before the material integrity is lost can be achieved by tuning the shear modulus and viscosity of a matrix. Furthermore, our model implies that an appropriately designed staggered microstructure, adopted by many natural composites, can repeatedly activate the energy dissipation process and thus improve dramatically the impact performance. This model demonstrates the role of matrix in energy dissipation, and stimulates new advanced material design concepts for ballistic applications. Biological composites found in nature often possess exceptional mechanical properties that man-made materials haven't be able to achieve. For example, it is predicted that a pencil thick spider silk thread can stop a flying Boeing airplane. Here, by proposing a dynamic shear-lag model, we investigate the relationships between the impact performance of a composite with the dimensions and properties of its constituents. Our analysis suggests that the impact performance of fiber-reinforced composites could improve

  12. 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.

    2014-01-01

    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

  13. Compositional Modelling of Stochastic Hybrid Systems

    NARCIS (Netherlands)

    Strubbe, S.N.

    2005-01-01

    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

  14. Testing the predictive power of nuclear mass models

    International Nuclear Information System (INIS)

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

    2008-01-01

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

  15. Comparison of Prediction-Error-Modelling Criteria

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay

    2007-01-01

    Single and multi-step prediction-error-methods based on the maximum likelihood and least squares criteria are compared. The prediction-error methods studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model, which is a r...

  16. Thermo-hydroforming of a fiber-reinforced thermoplastic composites considering fiber orientations

    Science.gov (United States)

    Ahn, Hyunchul; Kuuttila, Nicholas Eric; Pourboghrat, Farhang

    2018-05-01

    The Thermoplastic woven composites were formed using a composite thermal hydroforming process, utilizing heated and pressurized fluid, similar to sheet metal forming. This study focuses on the modification of 300-ton pressure formation and predicts its behavior. Spectra Shield SR-3136 is used in this study and material properties are measured by experiments. The behavior of fiber-reinforced thermoplastic polymer composites (FRTP) was modeled using the Preferred Fiber Orientation (PFO) model and validated by comparing numerical analysis with experimental results. The thermo-hydroforming process has shown good results in the ability to form deep drawn parts with reduced wrinkles. Numerical analysis was performed using the PFO model and implemented as commercial finite element software ABAQUS / Explicit. The user subroutine (VUMAT) was used for the material properties of the thermoplastic composite layer. This model is suitable for working with multiple layers of composite laminates. Model parameters have been updated to work with cohesive zone model to calculate the interfacial properties between each composite layer. The results of the numerical modeling showed a good correlation with the molding experiment on the forming shape. Numerical results were also compared with experimental results on punch force-displacement curves for deformed geometry and forming processes of the composite layer. Overall, the shape of the deformed FRTP, including the distribution of wrinkles, was accurately predicted as shown in this study.

  17. Foundation Settlement Prediction Based on a Novel NGM Model

    Directory of Open Access Journals (Sweden)

    Peng-Yu Chen

    2014-01-01

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

  18. Prediction of Milk Quality Parameters Using Vibrational Spectroscopy and Chemometrics

    DEFF Research Database (Denmark)

    Eskildsen, Carl Emil Aae

    fatty acids, protein fractions and coagulation properties from Fourier transform infrared measurements. This thesis shows how such predictions are trapped in a cage of covariance with major milk constituents like total fat and protein content. The prediction models for detailed milk composition...... are not based on causal relationships and this may seriously compromise calibration robustness. It is not recommended to implement indirect models for detailed milk composition in milk recording or breeding programs as such model are providing information on, for example, total protein rather than the specific...... protein fractions. If Fourier transform infrared based models on detailed milk composition are to be implemented in, for example, breeding programs it is recommended to decompose, for example, the individual fatty acids into functional groups, such as methyl, methylene, olefinic and carboxylic groups...

  19. A predictive model of natural gas mixture combustion in internal combustion engines

    Directory of Open Access Journals (Sweden)

    Henry Espinoza

    2007-05-01

    Full Text Available This study shows the development of a predictive natural gas mixture combustion model for conventional com-bustion (ignition engines. The model was based on resolving two areas; one having unburned combustion mixture and another having combustion products. Energy and matter conservation equations were solved for each crankshaft turn angle for each area. Nonlinear differential equations for each phase’s energy (considering compression, combustion and expansion were solved by applying the fourth-order Runge-Kutta method. The model also enabled studying different natural gas components’ composition and evaluating combustion in the presence of dry and humid air. Validation results are shown with experimental data, demonstrating the software’s precision and accuracy in the results so produced. The results showed cylinder pressure, unburned and burned mixture temperature, burned mass fraction and combustion reaction heat for the engine being modelled using a natural gas mixture.

  20. Predicting the Mineral Composition of Dust Aerosols. Part 2; Model Evaluation and Identification of Key Processes with Observations

    Science.gov (United States)

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

    2015-01-01

    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.

  1. Molar volume of eutectic solvents as a function of molar composition and temperature☆

    Institute of Scientific and Technical Information of China (English)

    Farouq S. Mjalli

    2016-01-01

    The conventional Rackett model for predicting liquid molar volume has been modified to cater for the effect of molar composition of the Deep Eutectic Solvents (DES). The experimental molar volume data for a group of commonly used DES has been used for optimizing the improved model. The data involved different molar compositions of each DES. The validation of the new model was performed on another set of DESs. The average relative deviation of the model on the training and validation datasets was approximately 0.1%while the Rackett model gave a relative deviation of more than 1.6%. The modified model deals with variations in DES molar com-position and temperature in a more consistent way than the original Rackett model which exhibits monotonic performance degradation as temperature moves away from reference conditions. Having the composition of the DES as a model variable enhances the practical utilization of the predicting model in diverse design and process simulation applications.

  2. Electrostatic ion thrusters - towards predictive modeling

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-02-15

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

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

    National Research Council Canada - National Science Library

    Zhou, Eric; Mollenhauer, David; Iarve, Endel

    2007-01-01

    ... 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...

  4. 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

  5. Predictive Validation of an Influenza Spread Model

    Science.gov (United States)

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

    2013-01-01

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

  6. Modeling and characterization of through-the-thickness properties of 3D woven composites

    Science.gov (United States)

    Hartranft, Dru; Pravizi-Majidi, Azar; Chou, Tsu-Wei

    1995-01-01

    The through-the-thickness properties of three-dimensionally (3D) woven carbon/epoxy composites have been studied. The investigation aimed at the evaluation and development of test methodologies for the property characterization in the thickness direction, and the establishment of fiber architectures were studied: layer-to-layer Angle Interlock, through-the-thickness Orthogonal woven preform with surface pile was also designed and manufactured for the fabrication of tensile test coupons with integrated grips. All the preforms were infiltrated by the resin transfer molding technique. The microstructures of the composites were characterized along the warp and fill (weft) directions to determine the degree of yarn undulations, yarn cross-sectional shapes, and microstructural dimensions. These parameters were correlated to the fiber architecture. Specimens were designed and tested for the direct measurement of the through-the-thickness tensile, compressive and shear properties of the composites. Design optimization was conducted through the analysis of the stress fields within the specimen coupled with experimental verification. The experimentally-derived elastic properties in the thickness direction compared well with analytical predictions obtained from a volume averaging model.

  7. 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...

  8. NOx PREDICTION FOR FBC BOILERS USING EMPIRICAL MODELS

    Directory of Open Access Journals (Sweden)

    Jiří Štefanica

    2014-02-01

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

  9. Prediction of membrane transport proteins and their substrate specificities using primary sequence information.

    Directory of Open Access Journals (Sweden)

    Nitish K Mishra

    Full Text Available Membrane transport proteins (transporters move hydrophilic substrates across hydrophobic membranes and play vital roles in most cellular functions. Transporters represent a diverse group of proteins that differ in topology, energy coupling mechanism, and substrate specificity as well as sequence similarity. Among the functional annotations of transporters, information about their transporting substrates is especially important. The experimental identification and characterization of transporters is currently costly and time-consuming. The development of robust bioinformatics-based methods for the prediction of membrane transport proteins and their substrate specificities is therefore an important and urgent task.Support vector machine (SVM-based computational models, which comprehensively utilize integrative protein sequence features such as amino acid composition, dipeptide composition, physico-chemical composition, biochemical composition, and position-specific scoring matrices (PSSM, were developed to predict the substrate specificity of seven transporter classes: amino acid, anion, cation, electron, protein/mRNA, sugar, and other transporters. An additional model to differentiate transporters from non-transporters was also developed. Among the developed models, the biochemical composition and PSSM hybrid model outperformed other models and achieved an overall average prediction accuracy of 76.69% with a Mathews correlation coefficient (MCC of 0.49 and a receiver operating characteristic area under the curve (AUC of 0.833 on our main dataset. This model also achieved an overall average prediction accuracy of 78.88% and MCC of 0.41 on an independent dataset.Our analyses suggest that evolutionary information (i.e., the PSSM and the AAIndex are key features for the substrate specificity prediction of transport proteins. In comparison, similarity-based methods such as BLAST, PSI-BLAST, and hidden Markov models do not provide accurate predictions

  10. Composite fabrication via resin transfer molding technology

    Energy Technology Data Exchange (ETDEWEB)

    Jamison, G.M.; Domeier, L.A.

    1996-04-01

    The IMPReS (Integrated Modeling and Processing of Resin-based Structures) Program was funded in FY95 to consolidate, evaluate and enhance Sandia`s capabilities in the design and fabrication of composite structures. A key driver of this and related programs was the need for more agile product development processes and for model based design and fabrication tools across all of Sandia`s material technologies. A team of polymer, composite and modeling personnel was assembled to benchmark Sandia`s existing expertise in this area relative to industrial and academic programs and to initiate the tasks required to meet Sandia`s future needs. RTM (Resin Transfer Molding) was selected as the focus composite fabrication technology due to its versatility and growing use in industry. Modeling efforts focused on the prediction of composite mechanical properties and failure/damage mechanisms and also on the uncured resin flow processes typical of RTM. Appropriate molds and test composites were fabricated and model validation studies begun. This report summarizes and archives the modeling and fabrication studies carried out under IMPReS and evaluates the status of composite technology within Sandia. It should provide a complete and convenient baseline for future composite technology efforts within Sandia.

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

    International Nuclear Information System (INIS)

    Chen Yonghong; Zhang Dafa; Peng Guichu; Wang Yuemin

    2009-01-01

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

  12. Sweat loss prediction using a multi-model approach.

    Science.gov (United States)

    Xu, Xiaojiang; Santee, William R

    2011-07-01

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

  13. Development of constitutive model for composites exhibiting time dependent properties

    International Nuclear Information System (INIS)

    Pupure, L; Joffe, R; Varna, J; Nyström, B

    2013-01-01

    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

  14. A Theoretical Study on Quantitative Prediction and Evaluation of Thermal Residual Stresses in Metal Matrix Composite (Case 1 : Two-Dimensional In-Plane Fiber Distribution)

    International Nuclear Information System (INIS)

    Lee, Joon Hyun; Son, Bong Jin

    1997-01-01

    Although discontinuously reinforced metal matrix composite(MMC) is one of the most promising materials for applications of aerospace, automotive industries, the thermal residual stresses developed in the MMC due to the mismatch in coefficients of thermal expansion between the matrix and the fiber under a temperature change has been pointed out as one of the serious problem in practical applications. There are very limited nondestructive techniques to measure the residual stress of composite materials. However, many difficulties have been reported in their applications. Therefore it is important to establish analytical model to evaluate the thermal residual stress of MMC for practical engineering application. In this study, an elastic model is developed to predict the average thermal residual stresses in the matrix and fiber of a misoriented short fiber composite. The thermal residual stresses are induced by the mismatch in the coefficient of the thermal expansion of the matrix and fiber when the composite is subjected to a uniform temperature change. The model considers two-dimensional in-plane fiber misorientation. The analytical formulation of the model is based on Eshelby's equivalent inclusion method and is unique in that it is able to account for interactions among fibers. This model is more general than past models to investigate the effect of parameters which might influence thermal residual stress in composites. The present model is to investigate the effects of fiber volume fraction, distribution type, distribution cut-off angle, and aspect ratio on thermal residual stress for in-plane fiber misorientation. Fiber volume fraction, aspect ratio, and distribution cut-off angle are shown to have more significant effects on the magnitude of the thermal residual stresses than fiber distribution type for in-plane misorientation

  15. On flavour and naturalness of composite Higgs models

    International Nuclear Information System (INIS)

    Matsedonskyi, Oleksii

    2015-01-01

    We analyse the interplay of the constraints imposed on flavour-symmetric Composite Higgs models by Naturalness considerations and the constraints derived from Flavour Physics and Electroweak Precision Tests. Our analysis is based on the Effective Field Theory which describes the Higgs as a pseudo-Nambu-Goldstone boson and also includes the composite fermionic resonances. Within this approach one is able to identify the directions in the parameter space where the U(3)-symmetric flavour models can pass the current experimental constraints, without conflicting with the light Higgs mass. We also derive the general features of the U(2)-symmetric models required by the experimental bounds, in case of elementary and totally composite t R . An effect in the Zb-barb coupling, which can potentially allow for sizable deviations in Z→b-barb decay parameters without modifying flavour physics observables, is identified. We also present the analysis of the mixed scenario, where the top quark mass is generated due to Partial Compositeness while the light quark masses are Technicolor-like.

  16. Structural Acoustic Physics Based Modeling of Curved Composite Shells

    Science.gov (United States)

    2017-09-19

    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 ELEMENT...study was to use physics -based modeling (PBM) to investigate wave propagations through curved shells that are subjected to acoustic excitation. An

  17. Finding Furfural Hydrogenation Catalysts via Predictive Modelling

    Science.gov (United States)

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

    2010-01-01

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

  18. Alcator C-Mod predictive modeling

    International Nuclear Information System (INIS)

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

    2001-01-01

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

  19. Clinical Predictive Modeling Development and Deployment through FHIR Web Services.

    Science.gov (United States)

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

    2015-01-01

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

  20. Tapered composite likelihood for spatial max-stable models

    KAUST Repository

    Sang, Huiyan

    2014-05-01

    Spatial extreme value analysis is useful to environmental studies, in which extreme value phenomena are of interest and meaningful spatial patterns can be discerned. Max-stable process models are able to describe such phenomena. This class of models is asymptotically justified to characterize the spatial dependence among extremes. However, likelihood inference is challenging for such models because their corresponding joint likelihood is unavailable and only bivariate or trivariate distributions are known. In this paper, we propose a tapered composite likelihood approach by utilizing lower dimensional marginal likelihoods for inference on parameters of various max-stable process models. We consider a weighting strategy based on a "taper range" to exclude distant pairs or triples. The "optimal taper range" is selected to maximize various measures of the Godambe information associated with the tapered composite likelihood function. This method substantially reduces the computational cost and improves the efficiency over equally weighted composite likelihood estimators. We illustrate its utility with simulation experiments and an analysis of rainfall data in Switzerland.