4th World Congress on Integrated Computational Materials Engineering (ICME 2017): Poster Session
Program Organizers: Paul Mason, Thermo-Calc Software Inc.; Michele Manuel, University of Florida; Alejandro Strachan, Purdue University; Ryan Glamm, Boeing Research and Technology; Georg J. Schmitz, Micress/Aachen; Amarendra Singh, IIT Kanpur; Charles Fisher, Naval Surface Warfare Center
Monday 5:00 PM
May 22, 2017
Room: Salon V, VI
Location: Ann Arbor Marriott Ypsilanti at Eagle Crest
P-3: A Diffusion Database for Multicomponent Cu Alloy: Yong Du1; Yuling Liu1; Shuhong Liu1; Dandan Liu2; Peng Deng1; Huixin Liu1; Qianhui Min1; 1Central South University; 2Central South University and Chinese Academy of Sciences
Owing to the excellent conductivity and outstanding plasticity, Cu alloy is widely used as a basis material. An accurate diffusion database is the key to realize the description of microstructure evolution of Cu alloy. The atomic mobility database for fcc phase in multicomponent Cu alloy was established via the DICTRA (DIffusion Controlled TRAnsformation) software package. The mobility parameters of fcc phase were constructed based on our new experimental diffusivity data, literature data and semi-empirical method. A case study for diffusivity in fcc Cu-Ni-Si alloys is demonstrated. By means of the solid-solid diffusion couples together with the electron probe microanalysis technique and Matano-Kirkaldy method, the composition dependence of ternary interdiffusion coefficients in Cu-rich fcc Cu-Ni-Sn alloy at 1073, 1023 and 973 K were measured. Based on the experimentally determined mole fraction interdiffusion coefficients as well as thermodynamic description of fcc phase, atomic mobilities of fcc phase in the Cu-Ni-Sn system were assessed by means of DICTRA. The newly obtained parameters were incorporated in the established database. The quality of the database was verified by comprehensive comparisons between various model-predicted diffusion properties and the experimental data. The general agreement validates the potential application of the present atomic mobility database to simulate the diffusion phenomenon in higher order systems.
P-8: Applications of Atomistic Simulations to Predict Phase Separation Diagram of Thermoplastic-toughened Epoxy Resins
: Chunyu Li1; Alejandro Strachan1; 1Purdue University
Thermoset polymers are widely used in aerospace and automotive industries as the main structural matrix for fiber composites because of their advantages in thermomechnical properties and easy and quick curing process. However their toughness, which is an important measure of material resistance to failure, is normally not satisfactory. Thus various methods have been utilized to modify epoxy resins in order to improve their toughness. One of the most common methods is the incorporation of a second phase, either rubbers or thermoplastics, into an epoxy resin. The final toughness of the toughed epoxy matrix depends on the phase morphology which is the result of reaction-induced phase separation in the curing process.A fundamental understanding of the reaction-induced phase separation is of great interests to the material community as well as industries. Molecular modeling is a powerful technique for getting such a understanding. Although there have been numerous efforts in studying phase separation of polymer blends by molecular modeling, few has been done on reaction-induced phase separations. In this presentation, we will report our recent efforts in predicting phase separation diagram by atomistic simulations for an epoxy system enhanced by thermoplastics. The procedure involves calculations of the solubility parameters, cohesive energy density and free energy of mixing for different compositions under different temperatures. Therefor, tremendous computer resources are required. The details, including algorithms and validations, will be presented in this talk. Our calculations indicate the phase separation diagram is predictable by atomistic simulations for reaction-induced phase separation.
P-10: Automating Thermodynamic Database Development with ESPEI: Brandon Bocklund1; Richard Otis1; Zi-Kui Liu1; 1Pennsylvania State University
There are currently several public repositories of materials data that are actively developed, but no solution exists to utilize this data for automated CALPHAD modeling at scale. ESPEI is an open-source software package being developed to automate the construction of thermodynamic databases from first-principles and thermochemical data. ESPEI is built upon the open-source Python package pycalphad, which solves the multi-component, multi-phase Gibbs energy minimization problem. The main advantages ESPEI has over traditional modeling techniques are the automated selection of a model for the system being assessed and the propagation of quantified uncertainty through the calculations. This presentation will show the progress in the development of ESPEI by demonstrating its use in the assessment of a real system. The challenges and future potential of ESPEI will be addressed.
P-11: Bayesian Optimization of Superalloy Designs: Joseph Licavoli1; Paul Sanders1; Santu Rana2; Sunil Gupta2; Vu Nguyen2; Svetha Venkatesh2; 1Michigan Technological University; 2Deakin University
Experiments, real or via computationally intense simulation, are expensive both in terms of time and cost. Bayesian optimization is a machine learning approach to efficiently find the extremum in expensive systems with a small number of experiments. An unknown function underlying the system is first approximated using a Gaussian process. This gives a probabilistic mechanism to record data from finished experiments and fill in unexplored regions. Second, a pay-off function encodes a risk-taking strategy to evaluate each unexplored region based on their potential to return a better experimental outcome. Finally, the location of the pay-off function maximum is sought and recommended for the next virtual experiment. The Gaussian process is then refined, a new pay-off function is computed and the setting for another virtual experiment is obtained. This is continued until an acceptable outcome is reached. Bayesian optimization was combined with the CALPHAD method to search for new eta strengthened and high entropy nickel superalloys. Various criteria including volume fractions of strengthening phases (either eta or gamma prime), volume fractions of detrimental phases, and the solvus of strengthening phases were balanced in a weighted objective function. Optimized alloys were produced using vacuum induction melting (VIM) and were fabricated into slabs for preliminary evaluations. X-ray diffraction was used to verify CALPHAD predictions. The alloys were then evaluated for their mechanical properties and the properties relative to commercial alloys are discussed.
P-12: Bioinspired Computational Design of Novel Materials and Structures: Iwona Jasiuk1; Christopher Kozuch1; Srikanth Raviprasad1; 1University of Illinois at Urbana-Champaign
Biological materials have excellent properties for their designated functions. General characteristics of biological materials are that they self-assemble and self-organize from atomic level into complex hierarchical, composite, often porous, and fluid filled structures. They are multifunctional, they adapt to environment and can often self-heal. We study various biological materials, ranging from hard mineralized tissues such as bone, nacre, enamel or turtle shell, to soft tissue such as cartilage, tendons, skin and others, to learn from nature and design new materials for various technological applications. We first present several cases illustrating how nature has optimized materials structures to yield superior and robust properties. One example is bone which has excellent structural characteristics: it is stiff, strong, tough and light. These highly desired properties for many structural applications are due to a composite and hierarchical structure of bone. Bone is a nanocomposite consisting of polymers and ceramics and pores. We conduct this study computationally, by using various tools from mathematics, physics, chemistry and engineering, such as topology optimization, micromechanics methods, and percolation. We explore concepts of hierarchy, fractality and other bioinspired structural designs to design new materials with superior mechanical, electrical and thermal performances, filling upper left corner in various Ashby property diagrams. The studied materials include lightweight yet stiff and strong materials, impact resistant materials, and electrically conductive polymers.
P-13: Bridging the Gap between Bulk Properties and Confined Behavior using Finite Element Analysis: David Linder1; John Ågren1; Annika Borgenstam1; 1KTH - Royal Institute of Technology
Theoretically and empirically based models of materials properties are crucial tools in development of new materials; however, these models are often restricted to certain systems due to assumptions or fitting parameters. When expanding a model into alternative systems it is therefore necessary to have sufficient experimental data. When working with composite or highly confined materials, such as layered structures or coatings, this can be problematic as most available data is on bulk materials. The present work displays the potential of using Finite Element Method (FEM) simulations as a tool to understand experimental observations and expand existing models to new systems using only bulk properties of the constituent phases. The present work focuses on the effect of geometrical constraints on the indentation behavior of elasto-plastic materials as an example on how FEM may be used to better understand experimental observations in composite or layered materials. The results may also be integrated into phenomenological models, expanding their application range.
P-14: Coexistence of Security, Flexibility and Productivity for the Materials Integration System: Kaita Ito1; Satoshi Minamoto2; Takuya Kadohira2; Makoto Watanabe2; Masahiko Demura1; 1The University of Tokyo; 2National Institute for Materials Science
Acceptance of software and experimental data from wide variety of sources is important for sustainable growth of the Materials Integration (MI) system. At this point, information security i.e. information leak prevention is essential for protection of intellectual property of the developer of software and the owner of data. However, guarantee of sufficiently high level of information security is difficult for researchers of materials science and engineering who are developing software but not not always an expert on information technologies. Therefore, security assurance must be equipped in the back-end of the MI system. Meanwhile, flexible accessibility and connectivity are also important for quick development of software and workflow in the MI system. To this end, MI-API (application program interface) is developing for unification of the I/O procedure of software and data. However, at least until the MI system become popular, the most of conventional software is not developed with MI-API. Therefore, unique I/O specification of software is wrapped so that it may be compatible with MI-API and to work as a module in the MI system. This wrapped software keeps the maintainability for original author of the software because the internal software itself is not touched. In this report, the concept, implementation and some specific examples of the workflow subsystem in the MI system is introduced. The MI system with modularized software and unified API enables the coexistence of security, flexibility and productivity for development of new materials.
P-18: CSUDDCC2: An Updated Diffusion Database for Cemented Carbides: Yong Du1; 1Central South University
Cemented carbides are widely used in industry as cutting tools, wear parts, as result of the high hardness and good toughness. A reliable diffusion database is critical to simulate microstructure evolution of gradient cemented carbides and cellular cemented carbides, which have better performance and longer service lifetime than traditional cemented carbides. In 2014, we established version one of CSUDDCC1: a diffusion database for multicomponent cemented carbides [Weibin Zhang, Yong Du, et al., Int. J. Refract Met Hard Mater., 43,164-180 (2014)]. In this work, a description for the updated diffusion database CSUDDCC2 is presented. The atomic mobility database for fcc and liquid in C-W-Co-Fe-Ni-Cr-V-Ti-Ta-Nb-Zr-Mo-Al-N cemented carbides was established based on our new experimental data, literature data, first-principles calculation and theoretical assessment via the DICTRA (Diffusion Controlled TRAnsformation) software package. The atomic mobility parameters in liquid are theoretically calculated by the newly modiﬁed Sutherland equation, and the atomic mobility parameters in fcc phase are optimized by the diffusivities measured in the present work and from the literature. The mobility parameters for self-diffusion and impurity diffusion in metastable fcc structure were determined through a semi-empirical method or first-principles calculations. Comprehensive comparisons between calculated and measured diffusivities indicate that most of the experimental data can be well reproduced by the currently obtained atomic mobilities. Combining the thermodynamic database for cemented carbides, the diffusion database has been used to simulate the microstructure evolution during sintering of gradient cemented carbides and cellular cemented carbides. The simulated microstructure agrees reasonably with the experimentally observed one.
P-21: Development of Nb-Bearing Cast Austenitic Steels for Exhaust Components of Gasoline Engines: Hailong Zhao1; Yinhui Zhang2; Carlos Engler-Pinto3; Larry Godlewski3; Jacob Zindel3; Mei Li3; Qiang Feng2; 1University of Science and Technology Beijing / Ford Motor Company; 2University of Science and Technology Beijing; 3Ford Motor Company
To comply with the stricter fuel consumption regulations, exhaust components for automotive gasoline engines are required to withstand exhaust gas temperatures as high as 1050°C. Thus, it is urgent to develop advanced cast steels with higher heat-resistance. Our previous studies indicated the properties of these alloys could be strengthened efficiently by minor additions of C and N, which were also expected to improve their economic competitiveness. Then a series of Nb-bearing cast austenitic steels with variations of N/C ratio were designed through the complementary CALPHAD and experimental approaches. Three microstructural models were established based on the morphology of primary Nb(C,N), which changed from "Chinese-script" to mixed flake-blocky and faceted-blocky with increasing the N/C ratio. The creep, fatigue and oxidation behavior of three types of model alloys were investigated in the temperature range of 600°C and 1000°C. All best properties occurred in alloys with “Chinese-script” Nb(C,N), which could effectively strengthen grain boundaries and interdendritic regions. The precipitation and coarsening of Cr-rich phases (residual δ-ferrite and (Cr,Fe)23C6) were found to degrade creep and fatigue properties. Moreover, the fatigue mechanism was sensitive to testing temperatures, associated with the formation of subgrains at elevated temperatures, while the creep mechanism was sensitive to some microstructural features. Oxidation resistance was dependent on the as-cast microstructure and Si content. It is suggested that the development of cast austenitic steels could be achieved through the formation of “Chinese-script” Nb(C,N) and the elimination of Cr-rich phases as well as the prevention of carbide degeneration, based on ICME alloy design.
P-23: Facilitating ICME through Platformization: B Gautham1; Sreedhar Reddy1; Prasenjit Das1; Chetan Malhotra1; 1TRDDC, TCS Research, Tata Consultancy Services
Integrated Computational Materials Engineering (ICME) is poised to change the way we conduct engineering design in the future where product engineering will be carried out in close association with materials and manufacturing engineering. This is already being manifested in newer technologies such as additive manufacturing and composite materials where the boundaries between product and process and material are sufficiently blurred. In order to successfully leverage ICME, we need a platform that should allow for the seamless integration of product design with process design and materials design and should allow for all three to be investigated, analyzed and optimized simultaneously to be able to obtain the right material for the right product to be manufactured in the right way. It should also provide for a unified and flexible language for expressing the problem domain and allow for the integration of modeling and simulation tools, product and materials databases as well as machine learning, data analysis and optimization algorithms into the design process. Most importantly, such a platform should be context aware and knowledge enabled. It should provide a strong semantic basis for expressing and capturing knowledge related to the problem domain and a means to reason with this knowledge in a context-sensitive manner to provide context appropriate guidance to the designer during the design process. The current paper proposes a basic structure for such a platform and how it is being realized as TCS-PREMAP.
P-25: First Principles Study of Grain Boundary Segregation and Embrittlement of Sp-elements on bcc Fe: Kazuma Ito1; Hideaki Sawada1; 1Nippon Steel & Sumitomo Metal Corporation
P,S,Sn and Sb have been well known as elements that segregate at grain boundaries and embrittle grain boundaries in Iron. However, the mechanisms have not been well clarified so far. In particular, although the mechanisms of segregation and embrittlement were discussed based on atomic-size effect or chemical effect, it has not been clear that how much these factors affect the segregation and embrittlement, respectively. In this study, we calculated the segregation energies and embrittling energies of P,S,Sn and Sb at sigma 3(111)/ tilt Fe grain boundary and evaluated the atomic size effects and chemical effects on these energies quantitatively by means of first-principles calculation. The results of segregation energies showed that chemical effects strongly enhance the segregation of P,S,Sn and Sb and atomic size effects cause the difference of the most stable segregation site between P,S and Sn,Sb. It is indicated by our calculation of embrittling energies that chemical effect on embrittlling energy increases for the elements with more p electrons, for example, from Sn to Sb or from P to S and atomic size effect on the energy increases for the elements with larger principal quantum number, for example, from P to Sb.
P-27: High Throughput Determination of Melting Temperatures of Molecular Systems: Ka-Ming Tam1; Nicholas Walker1; Brian Novak1; Dorel Moldovan1; Mark Jarrell1; 1Louisiana State University
Determining the melting temperature remains an important challenge in the simulation of molecular systems. The conventional method based on stabilizing the coexistence of liquid and solid requires rather large system sizes. The continuous growth of computing power has come to a point where the coexistence method can be routinely done for systems described by classical force fields. However, this remains problematic for ab-initio simulations as they are often restricted to small systems of a couple hundreds of atoms. Given this limitation, it is clear that methods which can extract the thermodynamic melting point from small finite size systems is crucial especially when a reliable classical force field is not readily available. The first order transition, in which melting is a prominent example, has been studied in the context of statistical physics models. We employ some of these techniques to predict the melting point. A key concept of understanding the phase transition is in the energy distribution. We studied the energy distribution of molecular systems by calculating the ratios of different order cumulants. They show behaviors as expected for the first order transition and thus finite size scaling can be used to extract the transition temperature. In contrast to the conventional coexistence method, large system sizes are not necessary. The prediction can be systematically improved by better sampling of the energy distribution, which allows efficient high throughput parallel calculations and thus suitable for use in workflows used to optimize materials properties.
P-28: Image-based 3D Modeling of Aggregated Grains in Aluminium Alloy from High-resolution Synchrotron Radiation CT: Masakazu Kobayashi1; Yoshitaka Yabumi1; Hiroyuki Toda2; Osamu Kuwazru3; Hiromi Miura1; 1Toyohashi University of Technology; 2Kyushu University; 3Fukui University
To understand inhomogeneous deformation of grain microstructure is one of important issue for metallic material design, because the development of inhomogeneity concerns the origins of yielding, fracture and recrystallization. In this study, 3D plastic strains during tensile deformation and grains shapes have been investigated in aluminium alloy by using synchrotron radiation CT. Furthermore, image-based grain microstructure models for crystal-plasticity finite element analysis have been developed to understand effects of neighbor crystallographic orientation and grain shapes on inhomogeneous deformation.
P-29: Integrated Computational Materials Engineering Development and Application in Materials Qualification: Guofeng Chen1; 1CT, Siemens Ltd., China
The Materials Qualification (MQ) is defined as “The process of establishing that a given material is of sufficient quality. This generally involves testing, analysis, and establishment or confirmation of standards or requirements”. In this paper, the applications of MQ are specifically exemplified for the materials development and application through Integrated Computational Materials Engineering (ICME) process, and some methodologies have been described to further improve the understanding of MQ technology execution and application in the new discipline of materials science and engineering.
P-30: Integrating microstructure-based properties into structural optimization of cast metal and injection moulded polymeric components: Jakob Olofsson1; Kent Salomonsson1; Joel Johansson1; 1Jönköping University, School of Engineering
In outdoor power products as well as in the automotive area, the request for lightweight structures increases constantly. To identify high performing load bearing structures with low weight, low density materials (as glass fibre reinforced polymers (GFRP), aluminium and magnesium) as well as with high strength and density, as cast irons, needs to be considered. While all these materials have their separate microstructural characteristics to consider on multiple-scale levels, the industrial virtual product development process needs to be able to treat all these alternatives and production routes using a common simulation strategy, and integrate multiple aspects of design, structural analyses, manufacturing and multi-scale models into an integrated computational optimization approach.The current work aims to introduce a newly developed geometry optimization procedure that has been developed and implemented for GFRP as well as cast materials. An essential part of the approach is the modelling of microstructural features found on sub-scale levels and their effect on the local material behaviour and performance. By applying knowledge-based engineering in an ICME approach, an integrated and automated multi-objective optimization scheme has been established and implemented. For GFRP, the injection moulding process and its effect on glass-fibre orientation and material performance is modelled. For cast materials, casting process simulation including solidification, microstructure modelling and heterogeneous material characterization is applied. In the present contribution, the approach is outlined and discussed.
P-31: JARVIS: High-throughput Classical and Quantum Calculation Database for Materials: Kamal Choudhary1; Francesca Tavazza1; 1National Institute of Standards and Technology
JARVIS is classical and quantum calculation for material properties. The goal of the classical part (JARVIS-FF) is to evaluate and compare the materials properties using interatomic potential through an easy to use web-interface. At present JARVIS-FF consists of more than 3800 Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) calculations (>16000 sub-calculations) using existing interatomic potentials for elastic, energetics, surface, vacancy formation energies and phonon properties of materials. The goal of the quantum part is to make density functional theory (DFT) calculation database for 2D-materials, thermoelectrics and solar-cell materials. The calculated properties of interest are structural, vibronic, electronic band-structure, optical, thermoelectric and elastic properties. At present JARVIS-DFT consists of more than 873 Vienna Ab-Initio Simulation Package (VASP) calculations (>25000 sub-calculations). Both JARVIS-FF and JARVIS-DFT are still evolving. All the input files used in the JARVIS-FF and JARVIS-DFT are available for public use to enhance data reproducibility. Statistical learning is used to find trends in the data. The JARVIS-FF is available at http://www.ctcms.nist.gov/~knc6/periodic.html and the JARVIS-DFT is available at http://www.ctcms.nist.gov/~knc6/JVASP.html . JARVIS is a part of Materials Genome Initiative (MGI) at NIST.
P-32: Local Structure Analysis via Voronoi Topology: Emanuel Lazar1; Jian Han1; David Srolovitz1; 1University of Pennsylvania
A major challenge in atomistic simulation studies of defects in crystalline materials is their automated identification and characterization. Simulations today may contain billions of atoms, and identifying even simple vacancies can be difficult in highly perturbed systems, requiring artificial modification of data through quenching. We introduce an efficient and versatile technique, based on Voronoi cell topology, that enables auto- mated visualization, characterization, and analysis of complex defect structure, even at temperatures near melting.
P-33: Materials Integration System for Prediction of the Performance for Inhomogeneous Structural Materials
: Satoshi Minamoto1; Takuya Kadohira1; Kaita Ito2; Makoto Watanabe1; Masahiko Demura2; 1National Institute for Materials Science; 2The University of Tokyo
A project of the Structural Materials for Innovation (SM4I) as a subject of the Strategic of Innovation Program (SIP) has started since 2014 as 5 years project. Here we aim to reduce time of development of new materials by developing an integrated system for materials science (Materials Integration (MI) system). To this end, not only combination of various software for materials science, but also applying machine learning technique, data science and experimental data stored for many decades are crucial to treat various problems. Normally structural materials are inhomogeneous, then uncertainty is not negligible to understand the performance of the materials, while the uncertainty makes difficult to find appropriate pathways of the calculations and get robust answer. Then an integrated environment for materials science connecting many kinds of analysis technique is required to accelerate the speed of materials development by understanding the performance of the materials, such as fatigue life limit, crack propagation, toughness and so on. Furthermore finding and describing a relation among vocabularies enable us to connect prediction models more flexibly with less effort. In the presentation, features of the MI system will be shown and we discuss a possibility of the MI system.
P-34: Mechanical Properties of Novel Architectured Foams: Diab Abueidda1; Rashid Abu Al-Rub2; Iwona Jasiuk1; 1University of Illinois at Urbana Champaign; 2Masdar Institute of Science and Technology
Exploring new lightweight yet strong materials is of great scientific and industrial interest. Therefore, researchers have used different methods to develop such materials with the purpose of filling the upper left corner region of Ashby strength/density and stiffness/density charts. One should take into consideration how the cellular materials are interconnected. Structures possessing joints between their elements will have stress concentration which may lead to earlier collapse of the structures. In this paper, we created 3D cellular materials that are based on mathematical surfaces called triply periodic minimal surfaces (TPMS). Their structures do not possess any joints in order to minimize the effect of stress concentration. Additive manufacturing is used to construct the cellular materials, with a density below 50 kg/m3. We study their mechanical behavior through a series of computations and experiments. Finite element method is used to investigate the mechanical response of such cellular materials. Two finite deformation elastic/hyperelastic-viscoplastic constitutive models that are calibrated based on the mechanical response of the base material are used in the computational study of the TPMS-foams for validation and further analysis. They are Arruda-Boyce (AB) model and parallel network (PN) model. The AB model includes an initial linear elastic behavior whilst the adopted PN model includes an initial hyperelastic behavior. Both models include yielding and viscoplastic deformation. Then, these models are used in the finite element analysis to computationally study of the mechanical behavior of the cellular materials. Various TPMS architectures and densities are considered.
P-35; Microstructurally Short Fatigue Crack: Modeling Grain Boundary Effect and Crack Growth Rates: Shardul Panwar1; Veera Sundararaghavan1; 1University of Michigan
Microstructurally short fatigue cracks play an important role in determining fatigue life of metal alloys. Thus, understanding the underline mechanism behind these cracks is important for accurate prediction of the fatigue behavior of these materials. We have developed a semi-analytical method, which is based on Bilby, Cottrell, and Swinden dislocation theory, to predict microstructurally short fatigue crack growth. In this theory, the plastic zone in front of the crack tip is represented as a continuous distribution of dislocations. In our approach, we assume that the stress produced by these dislocations is a function of the crack sliding displacement. When a microstructurally short crack reaches a grain boundary, it can either be blocked or retarded. To model this behavior, we have used a recently developed phenomenological grain boundary model. We show examples that compare our model's predictions with experimental results.
P-38: Model Uncertainty Quantification of Calibration Data Volume in bcc Fe Crystal Plasticity: Aaron Tallman1; Laura Swiler2; Yan Wang1; David McDowell1; 1Georgia Institute of Technology; 2Sandia National Laboratories
Scientific models and simulations generally have structure that is bound to hypotheses. The degree to which these hypotheses are testable is an important concern in the endeavor to improve upon existing models. A description of the testability of model structure is approached by examining the process of model calibration. A Crystal Plasticity model is used to simulate the temperature dependent single crystal yielding of bcc Fe. The model is calibrated with experimental data on the orientation and temperature dependent single crystal yield strength of bcc Fe. The calibration is performed incrementally. Separately, the model is calibrated with pseudodata generated from one calibration of the same model. In both calibrations, the model form uncertainty is described using surrogate models to interpolate the sum of squared errors (SSE) across calibration parameter space. The variation in SSE as data is incrementally included is used to inform decisions on the data requirements of the model.
P-39: Modeling AC Electrical Conductivity of Polymer Nanocomposites
: Pouyan Karimi1; Sohan Kale1; Iwona Jasiuk1; Martin Ostoja-Starzewski1; 1University of Illinois at Urbana-Champaign
Polymer nanocomposites have emerged as an important class of materials finding a wide range of conductive, semiconductive, and static dissipative applications. The complex ac impedance of carbon nanocomposites, taking into account the tunneling conductivity between nano-fillers has been studied. Real and imaginary parts of the complex conductivity vs. the frequency and the ﬁller fraction are presented for three-dimensional systems. A code has been developed to investigate the relation between microstructure and frequency-dependent electrical properties of nanocomposites including permittivity and conductivity using RC type simulation analysis. Our work focused on finding the admittance vs. frequency for equivalent circuit. Resistor and capacitor values are deduced from a random microstructure. These results are compared with experimental data obtained on a nanocomposite material composed of electrically conductive ﬁllers dispersed in an insulating matrix, a good agreement was found between the simulated and experimental results. It is evident that this simulation largely succeeds in explaining the experimental data, such as the complex impedance and its frequency dependence. The results for frequency-dependent permittivity and conductivity can be used as an input to calculate the shielding effectiveness of polymer nanocomposites. In addition, we are exploring the possibility to model a strongly heterogeneous material, based on the microstructure, using an improvement of an RC type model by considering a temperature dependence of the conductivity.
P-40: Molecular Dynamics Study of Nanoparticles Sintering in Laser Assisted Deposition Process: Ji-Hyeon Song1; Sung-Hoon Ahn2; Yan Wang1; 1Georgia Institute of Technology; 2Seoul National University
Understanding the laser effect on sintering is important for particle based additive manufacturing. How temperature and morphology of particles change with laser irradiation is a primary issue determining the quality of deposition results. Yet, they are very difficult to be directly measured by experiments. In this research, the sintering of nanoparticles in a laser assisted deposition process is simulated using molecular dynamics. In this system, nanoparticles at the aerosol state are heated by laser below the melting point. The dynamics of thermal property and morphological change of particles during the sintering are predicted. Interaction between particles and the surrounding environment is simulated. The effects of controllable process parameters such as laser intensity, aerosol pressure, and particle sizes are studied. Process parameters are optimized for the purpose of process planning. Simulations are also compared with experimental results.
P-41: Multiscale Materials Modeling, 3D Materials Science, Representative Volume Elements and More: Selected Advances and their Relevance to ICME Frameworks: Dennis Dimiduk1; Mike Groeber2; Michael Uchic2; 1BlueQuartz Software, LLC; 2Air Force Research Laboratory
Research on multiscale materials modeling has proceeded in earnest for about 40 years. Further, 3-dimensional materials science methods (3d-MSE) have been emerging for nearly two decades. Yet, there is little penetration of the tools, techniques and methods into industrial design, manufacturing and materials engineering. Here we selectively examine materials and structures multiscale modeling and 3d-MSE development from the aspects of i) experimental techniques/instruments; ii) data processing and segmentation; iii) data analysis, quantification and representation; iv) 3d structures and model building; and, v) predictive simulations. These examinations are conducted across scales of hierarchical materials structure using selected examples. From the assessment we suggest that more unified and holistic approaches are needed, cutting across engineering disciplines, to achieve widespread use of these methods within the design-materials and manufacturing enterprise. We also conclude that costs and the slow-to-develop knowledge base of skilled practitioners also present obstacles to adoption.
P-42: Multiscale Uncertainty Propagation in Molecular Dynamics: Anh Tran1; Yan Wang1; 1Georgia Institute of Technology
Modeling the propagation of uncertainty with respect to time in molecular dynamics (MD) simulation is important to understand the system behavior in details. In this work, the evolution of aleatory and epistemic uncertainty associated with thermodyanmics properties as the quantities of interests (QoIs) is modeled at two time scales. At the short time scale, the probability density distributions (PDFs) for QoIs are evolved at each step of MD simulation. At the long time scale, a stochastic dynamics model is proposed to propagate PDFs more efficiently with an upscaling scheme. Examples of viscosity and thermoconductivity based on Green-Kubo approach are demonstrated.
P-43: Nano Simulation Study of Mechanical Property Parameter for Microstructure-based Multiscale Simulation: Kazuki Mori1; Mototeru Oba1; Sukeharu Nomoto1; Akinori Yamanaka2; 1ITOCHU Techno-Solutions Corporation; 2Tokyo University of Agriculture and Technology
We have proposed the microstructure-based multiscale simulation of hot-rolling process of a duplex stainless steel using multi-phase-field and finite element methods. In the multiscale simulation, it is key to use accurate elastic constants of the constituent phases in the steel. However, because the elastic constants depend on chemical composition of the steel and temperature, it is difficult to obtain the elastic constants for multicomponent steels from database and databook. In the previous our work [S. Nomoto, et al., Integrating Materials and Manufacturing Innovation, submitted], the elastic constants of the constituent phases in the duplex stainless steel (Fe-Cr-Ni-C alloy) was calculated by first principle calculation and molecular dynamics simulation. The calculated elastic constants were good agreement with experimental ones. However, these results were obtained from 11 configurations of Fe, Cr, and Ni atoms without much considering local dispersion of Cr and Ni atoms. In this study, the elastic constants were calculated from 10000 atomic configurations by molecular dynamics simulation. As a result, the dispersion of the calculated elastic constants showed the Gaussian distribution. The factor of the Gaussian distribution of the elastic constants was revealed by the Radial Distribution Function.
P-44: On the Deformation Mechanisms of Three-Dimensional Core-Shell Nanoporous Metals: Lijie He1; Haomin Liu1; Bin Ding1; Niaz Abdulrahim1; 1University of Rochester
Nanoporous (NP) metallic structure shows brittleness under tensile loading despite of its ductile constituent ligaments. the brittleness of the NP structure is mostly attributed to the morphology of one dimensional nanoligaments randomly distributed in a three dimensional network structure. Preliminary experiments have revealed that electroplating a shell on the NP structure can significantly increase the hardness of the system. This study focuses on the effect of adding a shell to both a single ligament and a 3-D NP structure. We investigate topological and morphological parameters including surface and interface effects, ligament and pore sizes, orientation and loading direction, shell-layer thickness and surface chemistry on the overall mechanical behavior of NP structure and determine the corresponding deformation mechanisms. Our goal is to determine how these parameters should be tuned to achieve better ductility in NP metallic structures.
P-45: Ontology Project for Knowledge-driven Optimization for ICME Approach: Piotr Macioł1; Łukasz Rauch1; Andrzej Macioł1; 1AGH-University of Science and Technology
Development of new materials, products and technologies with ICME approach requires challenging computations, controlled by optimization algorithms. One of the possible ways of decreasing of computational time is “knowledge-driven optimization” – an optimization is controlled not only by a numerical algorithm, but also with a Knowledge Based System. This justifies development of a common language, able to cover communication between numerical models without sophisticated translators. There are several formalisms of knowledge description. In general, more strict formalisms lead to more difficult definition of knowledge but higher efficiency of reasoning. The most of reasoning systems are based on first order logic and knowledge defined with Horn clauses (IF-THEN rules). However, transformation of practical knowledge involved in ICME processes onto the form of such clauses is difficult, mainly due to uncertainty and incompleteness of data, structured data (ranged, tabular and others) as well as a large amount of technological knowledge. Furthermore, management of a coherent set of rules is a complex process itself and must be done by knowledge engineer, not domain experts. We present an approach to develop an environment of knowledge management, combining Semantic Web approach, first order logic based reasoning systems, fuzzy sets and fuzzy logic. In our approach the OWL-based ontology is used to define the language of communication between computer programs (including numerical models and databases). Furthermore, the ontology is used to control consistency of knowledge. The exemplary multiscale problem is described, including alternative technological paths. OWL based ontology and rules controlling optimization process are also included.
P-46: Optimization of High Entropy Alloys: Paul Jablonski1; Michael Gao1; John Sears1; Jeffrey Hawk1; 1US Department of Energy
High entropy alloys (HEA) have a unique positioning within the alloy world. By incorporating a number of elements in high proportion they have high configurational entropy which leads to interesting and useful properties such as enhanced oxidation resistance and strength. Traditionally, researchers have relied on a simple calculation to determine this configurational entropy which results in equiatomic compositions. Here an alternate approach is used where CALPHAD method is used to calculate the optimum (highest) entropy for a system of elements. This approach results in a non-equiatomic alloy formulation. In this research we compare the optimized formulations to equiatomic ones for the same system of elements. These alloys are made as large-scale ingots in our laboratory using detailed process control. Our preferred manufacturing approach is to employ induction melting to combine the constituents followed by a computationally optimized homogenization treatment to eliminate the segregation that occurs during solidification. At this point the ingot is prepared for hot working via forging and rolling. The resulting structure is fully wrought and comparable to alloy manufacture on the commercial scale. Alloy design, microstructure, tensile properties and creep behavior will be presented as comparisons between the formulations. A discussion regarding alternative approaches to material fabrication and the impact on resulting properties will also be presented.
P-47: Predicting Mechanical Properties and Material Transient Behavior of a Metastable Austenitic Stainless Steel by a Mechanism-based Simulation Model Considering Prehistory Effects: Martina Zimmermann1; Philipp-Malte Hilgendorff2; Andrei Grigorescu2; Claus-Peter Fritzen2; Hans-Jürgen Christ2; 1TU Dresden; 2Universität Siegen
Reliability of components strongly depends on material microstructures which in turn are significantly influenced by manufacturing processes applied. In this respect, metastable austenitic stainless steels are of particular interest, since they allow for an adjustment of the local mechanical properties by taking advantage of the phenomenon of a deformation-induced phase transformation. In the present study experimental analyses on the static&cyclic strength of AISI 304 in different pre-deformed conditions were extended by modelling and simulation approaches. Two-dimensional microstructures consisting of a representative number of grains were modelled using the boundary element method and plastic deformation within the microstructure was considered by mechanism-based approaches. As such, cyclic plastic deformation in shear bands and deformation-induced martensitic phase transformation were defined and implemented. Simulation results were directly compared to the observed deformation evolution on the real specimen surfaces. Cyclic softening and hardening behavior mirrored by the resonance behavior of the specimens during fatigue testing was compared to the predicted change in damping behavior due to the transient behavior on the basis of the simulation model. Good agreement of results confirms the model assumptions and allowed for assigning certain deformation mechanisms to the specific change of transient resonant behavior. The outcome of the study can be used for future computational materials engineering in two different ways – a mechanism based approach to adjust mechanical properties prior to the design of new forming processes and a basis for structural health monitoring (SHM) techniques measuring vibrations during operation and relating them to the transient material behavior.
P-48: Predicting Processing-Structure-Property Relationships in PAN Based Carbon Fibers Using Molecular Dynamics Simulations
: Saaketh Desai1; Alejandro Strachan1; 1Purdue University
Carbon fibers are an important class of materials, their high tensile strength and stiffness combined with their low density and chemical reactivity enable advanced composites for a wide range of applications. Commercial fibers can approach the modulus of ideal graphite but at the expense of strength and even high-strength fibers (with reduced stiffness) do not surpass 10% of the ideal strength. The development of high-strength and high stiffness fibers could be accelerated if processing-microstructure-property relationships were available; unfortunately these relationships remain empirical and we lack predictive tools. We will present a new molecular model to simulate the carbonization process of PAN based precursors and the development of microstructure in carbon fibers, and the predicted structural features are in agreement with experiments. We then characterize the mechanical properties of the predicted atomistic models using reactive molecular dynamics. The simulations enable us to extract quantitative processing-structure-property predictions that can provide insight to experimentalists working on the design of next-generation fibers.
P-49: Prediction of Continuous Cooling Trasformation Curves for Steels from Database: Makoto Watanabe1; Takuya Kadohira1; Satoshi Minamoto1; Susumu Tsukamoto1; Tadashi Kasuya1; Junya Inoue1; 1National Institute for Materials Science
Continuous Cooling Transformation (CCT) Curves represent phase transformation behavior depending on cooling rate. Thus CCT diagrams are significantly important to understand microstructure evolution and to estimate mechanical properties of steel welded parts. However, microstructure evolution and phase transformation behavior of steels is quite complicate, and predictions of CCT curves are still a challenging research subject. In National Institute for Materials Science (NIMS) in Japan, we have the database of steel CCT curves which has been obtained through reliable and well organized experiments. In this work, several attempts to predict CCT diagrams have been performed by utilizing the NIMS database and basic machine learning techniques and the summarized results will be presented in the presentation. Although there are still large errors between the predication and the experimental data for some steels, it has demonstrated high possibility to predict CCT curves from the reliable data base and appropriate analysis techniques.
P-50: Predictive Simulations of Polymers: Lorena Alzate Vargas1; Chunyu Li1; Michael Fortunato2; Alejandro Strachan1; Coray Colina2; 1Purdue University; 2University of Florida
Applications of polymers have spread into numerous industrial and technological fields. Current optimization and certification of polymer is mainly based on experimental tests leading to lengthy and costly cycles. Predictive simulations have the potential to contribute to a more effective approach to material design and certification. In this talk, we will discuss how to conduct molecular dynamics simulations to predict polymer properties. Detailed procedures from building polymer chains or monomers from the cloud to the polymerization of different systems will be presented. The effects of molecular force field, atomic charge assignment, annealing and deformation conditions on thermo-mechanical properties of these polymers will also be systematically analyzed, specifically we focus in the prediction of glass transition temperature for PMMA structures and the effect of the building procedure implemented with Polymer Modeler and PySIMM, the forcefield: pcff and DREIDING, in which we have noticed higher predictions of Tg (about 40 K) for the second generation forcefield pcff, and the molecular structure obtaining that systems with large chains show higher predicted values compared to small chains. We are expecting that atomistic simulations can provide quantitative predictions for a wide range of properties for polymers.
P-52: Simulation Analysis of Co-continuous Ceramic Composite Dynamic Mechanical Performance and Optimization Design: Hongmei Zhang1; 1Beijing Institute of Technology
Dynamic mechanical performance of co-continuous SiC3D/Al composites is simulated with a realistic three-dimensional(3D) model which is constructed using the proposed generation-based optimization method in this paper. Then an optimization design of different infill materials, volume fractions and distribution characters is proposed. The influences of infill material, volume fraction and core distribution on the dynamic behavior of composite are investigated systematically. The results indicate that the SiC3D/Al composites have the best dynamic behavior, and the failure stress raises significantly along with the increasing of SiC volume fraction. But in the unloading stage, the composites appear a sustaining compressive capacity when the SiC volume fraction is lower. Remaining mass rate and failure contour are studied to research the failure process. The failure stress is also influenced by the distribution characters significantly, and the composites have an optimum structure when C equals 0.02.
P-53: Statistical Tools for Quantifying Grain Boundary Crystallography-Property Relationships: Srikanth Patala1; 1North Carolina State University
Grain boundaries (GBs) influence a wide array of physical properties in polycrystalline materials and play an important role in governing microstructural evolution under extreme environments. While the importance of interfaces is well documented, their properties are among the least understood of all the defect types present in engineering material systems. This is due to the vast configurational space of interfaces, resulting in a diverse range of structures and properties. In this talk, I will introduce a novel computational technique for computing the lowest-energy grain boundary structures in the full crystallographic phase-space (misorientations and boundary-plane orientations) of grain boundaries. This high-throughput simulation technique will be integrated with a Bayesian statistical framework for developing predictive five-parameter grain boundary crystallography-energy functions in an efficient manner. The statistical framework will be presented in the context of fcc metals but may be easily extended to materials systems with bcc and hcp crystal structures. These techniques are expected to play an important role in the analysis of grain boundary crystallography-structure-property relationships as they may be extended to the quantification of complex properties, such as diffusivity, conductivity, corrosion resistance, yield strength and defect-interface interactions.
P-54: Strategies and Scenarios Regarding the Engineering and Management of Diagnosis, Maintenance and Reliability of Oil Pipelines: Anurag Jha1; Nirmal Singh1; 1ISM DHANBAD
The engineering research & the selection and the management application of the most appropriate diagnostic and maintenance methods for oil pipelines, so that to ensure a high level of reliability and second, the structural integrity assessment of domestic oil pipelines presenting metal loss imperfections and defects or deviation from circularity.It presents the nominal and characteristic quantities of oil pipelines the main factors that determine the behavior under load of oil pipelines materials and the materials used for manufacture, the factors and processes leading to degradation of oil pipelines and appearance of anomalies(imperfections and defects) and also the categories and criteria for classification of typical oil pipelines imperfections and defects,like local deformation,cracks or metal loss.It also analyses the modern methods,procedures and means of diagnostic techniques used for oil pipelines imperfections and defects investigation,detection and identification,with emphasis on nondestructive control type,under continuous in-situ monitoring of specific operation parameters.Includes analysis of all unprovoked and provoked damages occurred in the period between 2000 and 2008 years to a domestic oil pipeline according to various parameters based on entries in the Oil Pipeline Record Sheet and the interpretation of results.The following aspects: the construction and functional features of the existing experimental research stands for the mechanical behavior of pipes with and without anomalies on the tubing and designing an experimental program for determining the bursting pressure of the pipes with metal loss anomalies.
P-55: Study of Transient Behavior of Slag Layer in Bottom Purged Ladle: A CFD Approach: Vishnu Mantripragada1; Sabita Sarkar1; 1Indian Institute of Technology Madras
Purging of argon gas in the molten metal bath is a process, which is regularly involved in secondary steel making operations. The injected gas imparts momentum to the liquid metal which induces high turbulence in the molten metal and helps in homogenization of the bath composition and temperature, and facilitates the slag-metal interactions. In this study, a computational fluid dynamics (CFD) based numerical investigation is carried out on an argon gas stirred ladle to study the flow and interface behavior in a secondary steel making ladle. A transient, three phase coupled level-set volume of fluid (CLSVOF) model is employed to track the slag-metal, gas-metal and slag-gas interfaces. The transient behavior of slag layer deformation and open eye formation is studied for different slag layer to metal bath height ratios at various argon gas flow rates.
P-56: Tensor Random Fields for Stochastic Mechanics: Martin Ostoja-Starzewski1; Anatoliy Malyarenko2; 1University of Illinois; 2Mälardalen University
Given that most material microstructures are randomly heterogeneous and non-deterministic, stochastic methods are required for solution of initial-boundary value problems. These methods involve stochastic partial differential equations (SPDE) followed by stochastic finite elements (SFE), and stochastic finite differences (SFD). To proceed, tensor random fields (TRF) of material properties are always needed as input. Note here that most SFE and SFD models and simulations typically rely on simplistic scalar random field assumptions to represent the TRFs of conductivity and elasticity, which effectively are inconsistent with micromechanics and do not account for possible anisotropies. For example, instead of simulating a 4th-rank stiffness tensor field, conventional SFE in Uncertainty Quantification typically employ a scalar random field of Young's modulus along with a constant Poisson ratio or a random field of two Lamé constants. These observations motivate the thrust of this paper: to review the recently developed techniques for explicit representation and simulation of 2nd- and 4th-rank TRFs with the most generally admissible correlation structures. Our models are wide-sense stationary (i.e. spatially statistically homogeneous) and wide-sense isotropic. The representation of a 2nd-rank (and 4th-rank) TRF involves 5 (resp., 29) scalar functions, which cover all the classes of anisotropy; spectral expansions are also available. The scale-dependent, one- and two-point statistics may be calibrated using the Hill-Mandel homogenization condition.
P-58: The NIST Interatomic Potentials Repository in the Era of the MGI: Zachary Trautt1; Lucas Hale1; Chandler Becker1; Yechan Choi2; 1National Institute of Standards and Technology; 2Montgomery College
The Materials Genome Initiative (MGI) seeks to significantly decrease the cost and time of development of new materials, and atomistic/molecular simulation is one area where there is still significant opportunity. While the NIST Interatomic Potentials Repository (IPR) hosts numerous interatomic potentials (force fields), the IPR is not the only place to obtain interatomic potentials, and it can be confusing to determine where models are located and which ones are most relevant. To help users locate relevant information at NIST and other sites, the focus of the IPR is shifting from primarily storing and publishing developer-submitted potentials to: (i) registering potentials and related resources at other locations to enable greater discovery, (ii) carefully computing material properties, fully documented, to help enable users to select an appropriate potential for their use case, and (iii) creating and sharing high throughput property evaluation tools which can operate on local resources instead of requiring external connectivity. A broad overview is given of current status of this effort and future plans within the project.
P-60: Uncertainty Quantification for ICME of Composites: Loujaine Mehrez1; Ziad Ghauch1; Roger Ghanem1; Venkat Aitharaju2; William Rodgers2; Jacob Fish3; Colin McAuliffe3; 1University of Southern California; 2General Motors Company; 3Altair Engineering, Inc.
Reliable predictive modeling and design for complex systems made of heterogeneous materials can be achieved through the successful coupling of material modeling tools, uncertainty quantification tools, and assimilation tools of available experimental data at a multitude of scales. This work aims at achieving this objective. It is concerned with the construction of predictive probabilistic constitutive models for composite materials that are inferred from data collected at multiple length scales and which are suitable for both prediction and design purposes. Polynomial chaos expansions are well adapted for such tasks and are used to build multiscale probabilistic constitutive models. Specifically, explicit functional relationships are constructed of the macroscopic constitutive properties of the composites with input quantities from the finer scales. These constructions are (i) incorporated in probabilistic experimental calibration as well as propagation of uncertainties, (ii) used to discover the statistical dependencies among inter-scale or/and intra-scale homogenized properties and input quantities, (iii) used to deduce the sensitivities of constitutive homogenized properties at each scale with the input quantities and also with the homogenized properties from the finer scales, (iv) integrated in the design process of complex materials involving manufacturing process, physical and experimental constraints, and appropriate account of variation given the specific nature of heterogeneous materials and modeling errors. The proposed integrated computational modeling process is demonstrated for the design of non-crimp fiber composites under multiscale and multiphysics constraints associated with damage accumulation and manufacturing.
P-61: Uncertainty Quantification in First Principles based Phase Diagram Calculations: Liang Tian1; Anirudh Natarajan2; Anton Van der Ven2; Brian Puchala1; 1University of Michigan; 2University of California, Santa Barbara
The construction of first principles based alloy phase diagrams via lattice-model Monte Carlo calculations using cluster expansion effective Hamiltonians has become increasingly common place. Calculated phase diagrams are very useful for qualitative understanding and can be very effective in guiding and interpreting experiments, but the uncertainties associated with the predictions are generally poorly quantified. Using the software package CASM (github.com/prisms-center/CASMcode) we have developed robust methods for convergence of Monte Carlo calculations, automated phase transition detection, and automated fitting of free energies. We used these methods, applied to the Mg-Cd binary alloy system, to investigate the uncertainty in the calculated free energies and phase diagram due to density functional theory (DFT) calculation errors, cluster expansion basis set, coefficient fitting method, and Monte Carlo calculation convergence. We consider how these tools and results can be used as part of the solution to the challenge of uncertainty quantification in first principles based phase diagram calculations.
P-62: Using Artificial Neural Networks in Microstructure Evolution Prediction of Two-Phase Titanium Alloys. Integrated Computational Materials Engineering (ICME) Approach on the Base of Deform 2D/3D Software: Anton Ektov1; J.H. Kim2; 1VSMPO-AVISMA Corp.; 2Hanbat National University
The microstructural evolution of titanium alloys under isothermal and non-isothermal hot forging conditions was predicted using multilayer-multineuron artificial neural networks with feed-forward and back-propagation technique (FFBP ANN). All models were incorporated into finite element (FE) simulation software (DEFORM-3D). A representable Ti-database of properties was collected during analyses of huge amount of laboratory electronic protocols of mechanical properties. For the period since 2005 year all e-protocols being stored on the corporate in-house server was automatically parsed via VBA macro-coding. Ad-hoc programming techniques was created for automated treatment of unordered and sparse experimental tables of laboratory test data. The input parameters for ANN model were the alloy chemical composition and the various multistage heat-treatment routes, and the output parameter was the Ultimate Tensile Strength (UTS), Reduction of Area (RA), Elongation, Impact Strength and Fracture Tougthness (K1C). The goal of ANN training is to adequately predict mechanical properties of the wrought alpha/betaTi-alloys as a function of heat treatment and alloy chemistry. Resulting ANN models were coupled with the FE simulation (DEFORM-3D) in order to predict the variation of phase volume fraction during isothermal and non-isothermal forging. To validate the predicted results from the models, Ti-6Al-4V alloy was hot-worked at various conditions and then the resulting microstructures were compared with simulated data. Comparisons between model predictions and experimental data indicated that the joined ANN models and self-consistent analytic/pseudo-analytic models are in good agreement with experiment. Integrated Computational Materials Engineering (ICME) approach was created during developing and implementing of ANN.
P-63: Using the MOOSE Framework to Predict the Coevolution of Microstructure and Physical Properties in Materials Under Harsh Conditions: Michael Tonks1; Daniel Schwen2; Pritam Chakraborty2; 1Pennsylvania State University; 2Idaho National Laboratory
Materials engineering is facilitated when we can predict the physical properties that result in a material from a given microstructure. With this capability, we can design initial microstructures that provide the performance needed for a given application. However, this is complicated in materials under harsh conditions because the microstructure evolves throughout its lifetime, degrading the physical properties. However, computational tools that predict the coevolution of the microstructure and properties can be used to ensure proper performance even during microstructure evolution. In this presentation we summarize the capabilities of the Multiphysics Object Oriented Simulation Environment (MOOSE) for modeling the coevolution of microstructure and properties.