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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Glass-Ceramic Material Modeling: Theory Implementation and Application.
Abstract not provided.
Materials model development and instrumented PV module testing to improve finite element modeling capabilities.
Abstract not provided.
Combined Eulerian and Lagrangian approaches in peridynamic material modeling.
Abstract not provided.
Validation and implementation of a viscoelastic material model for analysis of an instrumented photovoltaic module mechanical test.
Abstract not provided.
Considerations for the Identification of Elasto-Plastic Material Model Parameters
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Overhead longwave infrared hyperspectral material identification using radiometric models
Material detection algorithms used in hyperspectral data processing are computationally efficient but can produce relatively high numbers of false positives. Material identification performed as a secondary processing step on detected pixels can help separate true and false positives. This paper presents a material identification processing chain for longwave infrared hyperspectral data of solid materials collected from airborne platforms. The algorithms utilize unwhitened radiance data and an iterative algorithm that determines the temperature, humidity, and ozone of the atmospheric profile. Pixel unmixing is done using constrained linear regression and Bayesian Information Criteria for model selection. The resulting product includes an optimal atmospheric profile and full radiance material model that includes material temperature, abundance values, and several fit statistics. A logistic regression method utilizing all model parameters to improve identification is also presented. This paper details the processing chain and provides justification for the algorithms used. Several examples are provided using modeled data at different noise levels.
Overhead longwave infrared hyperspectral material identification using radiometric models
Material detection algorithms used in hyperspectral data processing are computationally efficient but can produce relatively high numbers of false positives. Material identification performed as a secondary processing step on detected pixels can help mitigate false positives. A material identification processing chain for longwave infrared hyperspectral data of solid materials collected from airborne platforms is presented. The algorithms utilize unwhitened radiance data and Nelder–Meade numerical optimization to estimate the temperature, humidity, and ozone levels of the atmospheric profile. Pixel unmixing is done using constrained linear regression and Bayesian information criteria for model selection. The resulting identification product includes an optimal atmospheric profile and a full radiance material model that includes material temperature, abundance values, and several fit statistics. A logistic regression method utilizing the model parameters to improve identification is also presented. Furthermore, several examples are provided using modeled data at several noise levels.
Probabilistic material degradation model for aerospace materials subjected to high temperature, mechanical and thermal fatigue, and creep
A probabilistic general material strength degradation model has been developed for structural components of aerospace propulsion systems subjected to diverse random effects. The model has been implemented in two FORTRAN programs, PROMISS (Probabilistic Material Strength Simulator) and PROMISC (Probabilistic Material Strength Calibrator). PROMISS calculates the random lifetime strength of an aerospace propulsion component due to as many as eighteen diverse random effects. Results are presented in the form of probability density functions and cumulative distribution functions of lifetime strength. PROMISC calibrates the model by calculating the values of empirical material constants.
Lessons Learned and Best Practices for Utilizing a Generalized Composite Impact Model
A material model which incorporates several key capabilities which have been identified as lacking in currently available composite impact models has been developed. The material model utilizes experimentally based tabulated input to define the evolution of plasticity and damage as opposed to specifying discrete input parameters (such as modulus and strength). It has been implemented into the commercially available transient dynamic finite element code LS-DYNA® as MAT_213. The model can simulate the nonlinear deformation, damage and failure that take place in a composite under dynamic loading conditions. As MAT_213 is now being used by a general user community that did not participate in the model development process, a number of issues have been identified that caused uncertainty in assembling a MAT_213 input deck and conducting a MAT_213 analysis. The overall goal of this effort is to develop a quantified set of lessons learned and best practices which will permit a new user to conduct useful MAT_213 simulations without needing to have detailed expert knowledge of the material model and its theoretical underpinnings.
Modal model: a material interface stability model
This presentation describes the modal model, governing equations, and some challenges.
Many Body Methods from Chemistry to Physics: Novel Computational Techniques for Materials-Specific Modelling: A Computational Materials Science and Chemistry Network
Understanding the behavior of interacting electrons in molecules and solids so that one can predict new superconductors, catalysts, light harvesters, energy and battery materials and optimize existing ones is the ``quantum many-body problem’’. This is one of the scientific grand challenges of the 21 st century. A complete solution to the problem has been proven to be exponentially hard, meaning that straightforward numerical approaches fail. New insights and new methods are needed to provide accurate yet feasible approximate solutions. This CMSCN project brought together chemists and physicists to combine insights from the two disciplines to develop innovative new approaches. Outcomes included the Density Matrix Embedding method, a new, computationally inexpensive and extremely accurate approach that may enable first principles treatment of superconducting and magnetic properties of strongly correlated materials, new techniques for existing methods including an Adaptively Truncated Hilbert Space approach that will vastly expand the capabilities of the dynamical mean field method, a self-energy embedding theory and a new memory-function based approach to the calculations of the behavior of driven systems. The methods developed under this project are now being applied to improve our understanding of superconductivity, to calculate novel topological properties of materials and to characterize and improve the properties of nanoscale devices.
Low-Velocity Impact Analyses of Composite Panels Using Cohesive Zone Modeling and Continuum Damage Mechanics
A parametric study was conducted to gage the sensitivity of simulations of low-velocity impacts on laminated composite panels to various finite element model and material model features. The material model used in this parametric study was previously verified and validated for conducting progressive damage analyses of post-buckled composite structures subjected to quasi-static loading conditions. Based on the results of this parametric study, updates have been made to a progressive damage analysis material model and the best practices for its application. Experimental results are compared to computational results obtained with and without the updates to the models. Both thermoset and thermoplastic fiber-reinforced materials are considered. The resulting enhancements to the material model are presented in detail.
Low-Velocity Impact Analyses of Composite Panels Using Cohesive Zone Modeling and Continuum Damage Mechanics
A parametric study was conducted to gage the sensitivity of simulations of low-velocity impacts on laminated composite panels to various finite element model and material model features. The material model used in this parametric study was previously verified and validated for conducting progressive damage analyses of post-buckled composite structures subjected to quasi-static loading conditions. Based on the results of this parametric study, updates have been made to a progressive damage analysis material model and the best practices for its application. Experimental results are compared to computational results obtained with and without the updates to the models. Both thermoset and thermoplastic fiber-reinforced materials are considered. The resulting enhancements to the material model are presented in detail.
High Resolution Mapping of Orientation and Strain Gradients in Metals by Synchrotron 3D X-ray Laue Microdiffraction
Synchrotron 3D X-ray Laue microdiffraction, available at beamline 34-ID-E at Advanced Photon Source in Argonne National Laboratory, is a powerful tool for 3D non-destructive mapping of local orientations and strains at sub-micron scale in the bulk. With this technique, it is possible to study local residual stresses developed during manufacturing or while in service due to interactions between, for example, different phases and/or grains with different orientations in materials containing multiple or single phase(s). Such information is essential for understanding mechanical properties and designing advanced materials, but is largely non-existent in the current generation of materials models. In the present paper, the principle and experimental set-up of the 3D microdiffraction are introduced, followed by a description of a method for quantification of the local plastic deformation based on high-angular-resolution orientation maps. The quantification of local residual stresses in two model materials, ductile cast iron (two phases) and partially recrystallized pure nickel (single phase), using 3D microdiffraction will then be presented. The results show that 3D microdiffraction is important for understanding the origin of local residual stresses and to relate them to the microstructural evolution. Finally, the limitations of the 3D microdiffraction on the current generation synchrotron source and new possibilities after the synchrotron upgrade are discussed.
Unit-Sphere Multiaxial Stochastic-Strength Model Applied to Anisotropic and Composite Materials
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.
Flux-based three-dimensional electrodynamic modeling approach to superconducting circuits and materials
Modeling the behavior of superconducting electronic circuits containing Josephson junctions is crucial for the design of superconducting information processors and devices. In this paper, we introduce DEC-QED, a computational approach for modeling the electrodynamics of superconducting electronic circuits containing Josephson junctions in arbitrary three-dimensional electromagnetic environments. DEC-QED captures the nonlinear response and induced currents in BCS superconductors and accurately captures phenomena such as the Meissner effect, flux quantization, and Josephson effects. Using a spatial coarse-graining formulation based on discrete exterior calculus (DEC), DEC-QED can accurately simulate transient and long-time dynamics in superconductors. Finally, the expression of the entire electrodynamic problem in terms of the gauge-invariant flux field and charges makes the resulting classical field theory suitable for second quantization.