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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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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

FEA-aided investigation of the effective thermal conductivity in a medium with embedded spheres

For multiple applications in nuclear energy, the ability to accurately represent material behavior with a simplified model is important to facilitate practical engineering-scale simulations. Here, we focus on the homogenized thermal response of a medium containing spherical inclusions, similar to a fuel form (compact or pebble) containing (TRISO) particles. A review on effective thermal conductivity (ETC) modeling is performed considering a random distribution of mono-sized spherical inclusions in a continuous matrix, with a primary focus on the analytical models. Finite element simulations are performed to evaluate each analytical model in varying material conditions. The model predictions are compared with the expected results obtained from the finite element predictions in addition to the Wiener and Hashin-Shtrikman bounds. Lastly, we included a practical discussion on the homogenization applied to a TRISO fuel pebble.

42 ENGINEERING↗
DOE OSTI2021

Thermal-Mechanical Elastic-Plastic and Ductile Failure Model Calibrations for 304L Stainless Steel Alloy

Numerical simulations of metallic structures undergoing rapid loading into the plastic range require material models that accurately represent the response. In general, the material response can be seen as having four interrelated parts: the baseline response under slow loading, the effect of strain rate, the conversion of plastic work into heat and the effect of temperature. In essence, the material behaves in a thermal-mechanical manner if the loading is fast enough so when heat is generated by plastic deformation it raises the temperature and therefore influences the mechanical response. In these cases, appropriate models that can capture the aspects listed above are necessary. The material of interest here is 304L stainless steel, and the objective of this work is to calibrate thermal-mechanical models: one for the constitutive behavior and another for failure. The work was accomplished by first designing and conducting a material test program to provide data for the calibration of the models. The test program included uniaxial tension tests conducted at room temperature, 150 and 300 C and at strain rates between 10 –4 and 10 3 1/s. It also included notched tension and shear-dominated compression hat tests specifically designed to calibrate the failure model. All test specimens were extracted from a single piece of plate to maintain consistency. The constitutive model adopted was a modular $J_2$ plasticity model with isotropic hardening that included rate and temperature dependence. A criterion for failure initiation based on a critical value of equivalent plastic strain fitted the failure data appropriately and was adopted. Possible ranges of the values of the parameters of the models were determined partially on historical data from calibrations of the same alloy from other lots and are given here. The calibration of the parameters of the models were based on finite element simulations of the various material tests using relatively ne meshes and hexahedral elements. When using the model in structural finite element calculations, however, element formulations and sizes different from those in the calibration are likely to be used. A brief investigation demonstrated that the failure initiation predictions can be particularly sensitive to the element selection and provided an initial guide to compensate for the effect of element size in a specific example.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Complex Oxides under Simulated Electric Field: Determinants of Defect Polarization in AB O 3 Perovskites

Abstract Polarization of ionic and electronic defects in response to high electric fields plays an essential role in determining properties of materials in applications such as memristive devices. However, isolating the polarization response of individual defects has been challenging for both models and measurements. Here the authors quantify the nonlinear dielectric response of neutral oxygen vacancies, comprised of strongly localized electrons at an oxygen vacancy site, in perovskite oxides of the form AB O 3 . Their approach implements a computationally efficient local Hubbard U correction in density functional theory simulations. These calculations indicate that the electric dipole moment of this defect is correlated positively with the lattice volume, which they varied by elastic strain and by A‐site cation species. In addition, the dipole of the neutral oxygen vacancy under electric field increases with increasing reducibility of the B‐site cation. The predicted relationship among point defect polarization, mechanical strain, and transition metal chemistry provides insights for the properties of memristive materials and devices under high electric fields.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Evolution of anisotropy with saturation and its implications for the elastoplastic responses of clay rocks

Many clay rocks have distinct bedding planes. Experimental studies have shown that their mechanical properties evolve with the degree of saturation (DOS), often with higher stiffness and strength after drying. For transversely isotropic rocks, the effects of saturation can differ between the bed-normal (BN) and bed-parallel (BP) directions, which gives rise to saturation-dependent stiffness and strength anisotropy. Accurate prediction of the mechanical behavior of clay rocks under partially saturated conditions requires numerical models that can capture the evolving elastic and plastic anisotropy with DOS. In this study, we present an anisotropy framework for coupled solid deformation-fluid flow in unsaturated elastoplastic media. Here we incorporate saturation-dependent strength anisotropy into an anisotropic modified Cam-Clay (MCC) model and consider the evolving anisotropy in both the elastic and plastic responses. The model was calibrated using experimental data from triaxial tests to demonstrate its capability in capturing strength anisotropy at various levels of saturation. Through numerical simulations, we demonstrate the role of evolving stiffness and strength anisotropy in the mechanical behavior of clay rocks. Plane strain simulations of triaxial compression tests were also conducted to demonstrate the impacts of material anisotropy and DOS on the mechanical and fluid flow responses.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Improving Additive Manufactured Component Performance through Multi-Scale Microstructure Simulation and Process Optimization

The purpose of this project was to utilize computational tools to understand the relationships between processing, microstructure, and properties for additively manufactured (AM) aluminum alloys for automotive applications, and to provide an engineering solution for helping to optimize process conditions. The project leverages ORNL developments in computational modeling, including AM process modeling, phase-field based microstructure evolution predictions, and data analytics techniques for mapping process conditions to material outcomes. The project utilized an Al-Cu-Mn-Zr alloy as a model material for studying formation of defects and microstructural features in response to variations in process conditions. Based on both pre-existing experimental data and simulation results, statistical process maps were constructed to identify regions of process space with minimal defect formation and advantageous microstructures and properties. The software tools used for this purpose were successful disseminated to GM, who were able to successful compile the relevant HPC codes within their own computing ecosystem and perform initial calculations to reproduce ORNL results.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Sensitivity Study of Multiscale and Phenomenological Elasto-Viscoplastic Grade 91 Material Models for Component-Scale Response

Many advanced nuclear reactor concepts currently being developed are targeting higher operating temperatures relative to the current fleet of light water nuclear reactors, for efficiency gains and other operational considerations. The design of high temperature structural components with reliable long-term operational performance will depend on material models that accurately capture the inelastic deformation mechanisms active in these environments. In this work, we perform a detailed parameter sensitivity analysis of two unified elasto-viscoplastic Grade 91 material models capable of capturing long term high temperature creep deformation. The first model is a phenomelogical material model from the Nuclear Engineering Material Library (NEML) developed at Argonne National Lab. The NEML model parameters and their uncertainty were fit to a range of Grade 91 experimental data using Bayesian Markov Chain Monte Carlo analysis. The second model is a LAROMance data-driven surrogate material model developed at Los Alamos National Lab. The LAROMance model is fit to a large database of responses produced by a mechanistic crystal plasticity based polycrystal model. Parameters for the LAROMance surrogate material model reflect the pedigree of the Grade 91 microstructure. Both material models have been integrated into the Grizzly code, based on the open-source MOOSE multiphysics simulation framework, to simulate both the progression of aging mechanisms and the effects of that aging on nuclear power plant structures. Grizzly is used analyze a three-dimensional Grade 91 piping system to compare the long-term inelastic response predicted by these two fundamentally different models and assess the sensitivity of the material model input parameters on this quantity of interest.

42 ENGINEERING↗
DOE OSTI2020

Hardening in Au-Ag nanoboxes from stacking fault-dislocation interactions

Porous, nano-architected metals with dimensions down to ~10 nm are predicted to have extraordinarily high strength and stiffness per weight, but have been challenging to fabricate and test experimentally. Here, we use colloidal synthesis to make ~140 nm length and ~15 nm wall thickness hollow Au-Ag nanoboxes with smooth and rough surfaces. In situ scanning electron microscope and transmission electron microscope testing of the smooth and rough nanoboxes show them to yield at 130 ± 45 MPa and 96 ± 31 MPa respectively, with significant strain hardening. A higher strain hardening rate is seen in rough nanoboxes than smooth nanoboxes. Finite element modeling is used to show that the structure of the nanoboxes is not responsible for the hardening behavior suggesting that material mechanisms are the source of observed hardening. Molecular dynamics simulations indicate that hardening is a result of interactions between dislocations and the associated increase in dislocation density.

42 ENGINEERING↗
DOE OSTI2023

Testing of an Optical Fiber--Based Gamma Thermometer in the High Flux Isotope Reactor Gamma Irradiation Facility

This report describes the design, thermal modeling, and gamma irradiation testing of an optical fiber–based gamma thermometer (OFBGT), which was irradiated in the High Flux Isotope Reactor (HFIR) Gamma Irradiation Facility (GIF). OFBGTs are a promising technology for application in nuclear reactors because they can provide a distributed measurement of gamma ray heating rate, unlike thermocouple-based gamma thermometers, which are fixed in-core sensors that can be used in boiling water reactors to calibrate local power range monitors. OFBGTs measure gamma ray heating rate by measuring the temperature difference between a pair of optical fibers; one fiber is in thermal contact with a heat sink (usually the reactor coolant), and the other is in thermal contact with a thermally isolated mass. The device can be calibrated with a heating wire within the thermal mass. The OFBGT that was designed and fabricated at Oak Ridge National Laboratory can measure distributed gamma ray heating rate over an effective measurement length of 61 cm, and the outer diameter of the sensor is 12.7 mm, giving the prototypical sensor design a relatively small footprint. The sensor housing is backfilled with Ar to ensure a well-predicted thermal response that is not affected by humidity or chemical interactions during operation. For calibration, the sensor design uses a Ni–Cr wire, which can be supplied with currents from 0 to 1 A to capture the wide range of potential gamma ray heating rates expected in HFIR’s spent fuel elements. The OFBGT was thermally modeled analytically and numerically; both models account for temperature-dependent thermal conductivities of the materials and show good agreement. The thermal response of the sensor inside spent HFIR fuel elements was simulated for times up to 1 year after discharge of the fuel element. Out-of-pile open-air tests indicated that the steady-state response of the sensor matches modeled results within experimental uncertainty. Calibration tests were performed using electrical heating in the HFIR spent fuel pool, above the fuel elements, to establish a relationship between the difference in spectral shift measured by optical fibers located inside and outside the OFBGT and the applied electrical heating. Subsequently, the OFBGT was placed within the the spent fuel element from HFIR cycle 501 to measure the spatial profile of the gamma heating rates. Results showed good agreement between the theoretical and measured gamma dose rate profiles, with maximum deviations of ~10% or less.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Applications of Nickelate perovskites for neuromorphic computing from electronic structure and Machine Learning

While the limit of Moore's law is presently being reached with current microelectronic technologies, we need to develop new paradigms that overcome this limitation. In that respect, neuromorphic computing is a concept that emulates the neural behavior and response of the human brain, and it has been recognized as a promising alternative approach. In this research project, we will perform multi-fidelity scale bridging to explore the potential use of materials with metal to insulator transition for neuromorphic applications. In particular, rare earth nickelates are promising for such purposes, as the transition in these materials is quite sensitive to a broad set of different external stimuli. Our multi-fidelity approach will bridge the high-fidelity electronic structure calculations with classical potentials. We will bridge dynamical mean field theory with a classical atomistic representation via a deep learning force field. The neural network is trained with energies, charges, and forces obtained by accurate electronic structure theories based on Dynamical Mean Field Theory. The configurational space is generated from known crystal phases, ab initio molecular dynamics with exchange-correlation functionals corrected with the Hubbard model, disordered phases with different concentrations of oxygen vacancies, and nonsymmetrical positions and induced strain by grain interfaces or contact with a substrate. Strategies to train the model with a reduced number of training examples are obtained from active learning methods, and new structures for improving the learning process are generated by using machine learning autoencoders. This classical potential will be validated through a diversity of electronic structure methods and represents an important step to combine the flexibility and accuracy of first-principles with the speed of classical potentials. The generated multi-fidelity surrogate model will be used to understand the role of strain, oxygen vacancies, proton doping, the variation of the crystal phase, substrate effects, vibrational effects as the octahedral rotation, grain boundaries and defect effects on the response of a Metal to Insulator Transition (MIT) in correlated materials. Long time and large-scale simulations will help understand the role of different stimuli to control the hysteresis of the MIT, as it has been experimentally suggested. Selected configurations will be analyzed with higher-level theories to provide an accurate electronic description and to study how the orbitals and charges are rearranged under different conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Spectral Effects in Albedo and Rearside Irradiance Measurment for Bifacial Performance Estimation: Preprint

Albedo data are essential for accurate prediction of bifacial PV module performance. However, spectral response mismatch between PV modules and irradiance sensors used in albedometers can limit the accuracy of performance predictions. In order to provide quantitative assessment of this effect, we investigate via simulation the differences in spectrally responsive albedo measured with thermopile pyranometers and crystallinesilicon PV reference cells in comparison to a representative crystalline-silicon bifacial PV module for nine different representative ground surface materials. Calculations are performed using simulated solar spectra together with catalogued spectral reflectivity data distributed with the SMARTS simulation software. For the specific materials considered, the results show that albedo measurement using thermopile pyranometers could over or under-estimate the ground-reflected radiation usable by a bifacial PV module by up to 10%, versus only approximately 4% total range of variation for a PV reference cell.

Albedo↗
DOE OSTI2022

Virtual diffraction simulations using the quasi-coarse-grained dynamics method to understand and interpret plasticity contributions during in situ shock experiments

The dynamic deformation response of metallic materials has contributions from dislocations, deformation twinning, and plastic deformation. The current state-of-art techniques can detail the complex mechanistic history of deformation modes under shock loading in real-time using in situ X-ray diffraction (XRD). However, the capability of these experiments to unravel plasticity contributions is challenging due to limitations in interpreting results and the lack of validation from atomistic simulations. Molecular dynamics (MD) simulations can successfully capture various deformation modes in metals and complement experiments using simulated diffractograms at various stages of evolution. However, the difference in length and time scales of MD simulations and experiments is a substantial obstacle in completing and interpreting in situ diffractograms. Therefore, the existing modeling methods require various approximations to model defect evolution and interaction at the mesoscales. However, while using approximations to correlate the peak broadening behavior to the density of dislocations or the shifts/splitting due to the presence of twins, the interpretations of the plasticity contributions from diffractograms are non-trivial, especially when multiple modes of deformation may be operating. This viewpoint discusses combining a mesoscale modeling method called quasi-coarse-grained dynamics and virtual XRD to characterize the plasticity contributions in BCC metals from slip, twinning, and phase transformation behavior. Furthermore, the combined approach shows promise in bridging the mesoscale gap between the capabilities of atomic-scale simulations and in situ experiments to characterize the dynamic deformation of materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Micropolar deep material network

This study extends the Deep Material Network (DMN), a physics-informed machine learning framework, to predict the homogenized mechanical response of composite materials with micropolar (Cosserat-type) constitutive behavior. This extension incorporates microstructure-dependent size effects, enabling accurate, efficient, and size-aware predictions for composites with complex internal architectures. While traditional, direct numerical simulation micropolar models effectively capture size effects by introducing extra local degrees of freedom, they bring significant computational challenges, particularly for multiscale analyses relevant to engineering applications. The micropolar DMN developed in this paper achieves high accuracy while significantly reducing computation time compared to micropolar direct numerical simulations. This advancement enables multiscale analyses and parameter studies that were previously impractical, such as high-cycle fatigue simulations and comprehensive investigations of internal length scale effects notably in size-dependent plastic response and the optimization of lattice structures. By uniting microstructure-sensitive modeling, physics-driven learning, and scalable surrogate modeling, the micropolar DMN paves the way for accelerated material design, large-scale parametric studies, and the reliable incorporation of size-dependent effects across a wide range of engineering applications, including optimization and next-generation composite design.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Improving dynamic collision frequencies: Impacts on dynamic structure factors and stopping powers in warm dense matter

Simulations and diagnostics of high-energy-density plasmas and warm dense matter rely on models of material response properties, both static and dynamic (frequency-dependent). Here, in this work, we systematically investigate variations in dynamic electron–ion collision frequencies ν(ω) in warm dense matter using data from a self-consistent-field average-atom model. We show that including the full quantum density of states, strong collisions, and inelastic collisions lead to significant changes in ν(ω). These changes result in red shifts and broadening of the plasmon peak in the dynamic structure factor, an effect observable in x-ray Thomson scattering spectra, and modify stopping powers around the Bragg peak. These changes improve the agreement of computationally efficient average-atom models with first-principles time-dependent density functional theory in warm dense aluminum, carbon, and deuterium.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Thermal-Mechanical Elastic-Plastic and Ductile Failure Model Calibrations for 6061-T651 Aluminum Alloy from Plate

Numerical simulations of metallic structures undergoing rapid loading into the plastic range require material models that accurately represent the response. In general, the material response can be seen as having four interrelated parts: the baseline response under slow loading, the effect of strain rate, the conversion of plastic work into heat and the effect of temperature. In essence, the material behaves in a thermal-mechanical manner if the loading is fast enough so when heat is generated by plastic deformation it raises the temperature and therefore influences the mechanical response. In these cases, appropriate models that can capture the aspects listed above are necessary. The matters of interest here are the elastic-plastic response and ductile failure behavior of 6061-T651 aluminum alloy under the conditions described above. The work was accomplished by first designing and conducting a material test program to provide data for the calibration of a modular $J_2$ plasticity model with isotropic hardening as well as a ductile failure model. Both included modules that accounted for temperature and strain rate dependence. The models were coupled with an adiabatic heating module to calculate the temperature rise due to the conversion of plastic work to heat. The test program included uniaxial tension tests conducted at room temperature, 150 and 300 C and at strain rates between 10 –4 and 10 3 1/s as well as four geometries of notched tension specimens and two tests on specimens with shear-dominated deformations. The test data collected allowed the calibration of both the plasticity and the ductile failure models. Most test specimens were extracted from a single piece of plate to maintain consistency. Notched tension tests came from a possibly different plate, but from the same lot. When using the model in structural finite element calculations, element formulations and sizes different from those used to model the test specimens in the calibration are likely to be used. A brief investigation demonstrated that the failure model can be particularly sensitive to the element selection and provided an initial guide to compensate in a specific example.

36 MATERIALS SCIENCE↗
DOE OSTI2022

SuperScreen: An open-source package for simulating the magnetic response of two-dimensional superconducting devices

Quantitative understanding of the spatial distribution of magnetic fields and Meissner screening currents in two-dimensional (2D) superconductors and mesoscopic thin film superconducting devices is critical to interpreting the results of magnetic measurements of such systems. Here, we introduce SuperScreen, an open-source Python package for simulating the response of 2D superconductors to trapped flux and applied time-independent or quasi-DC magnetic fields for any value of the effective magnetic penetration depth, Λ. Given an applied magnetic field, SuperScreen solves the 2D London equation using an efficient matrix inversion method to obtain the Meissner currents and magnetic fields in and around structures composed of one or more superconducting thin films of arbitrary geometry with spatially nonuniform magnetic penetration depth. SuperScreen can be used to model screening effects and calculate self- and mutual-inductance in superconducting devices, and simulate the magnetic response of inhomogeneous 2D superconductors.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The cosmic muon and detector simulation framework of the extreme energy events (EEE) experiment

Abstract This paper describes the simulation framework of the extreme energy events (EEE) experiment. EEE is a network of cosmic muon trackers, each made of three multi-gap resistive plate chambers (MRPC), able to precisely measure the absolute muon crossing time and the muon integrated angular flux at the ground level. The response of a single MRPC and the combination of three chambers have been implemented in a GEANT4-based framework (GEMC) to study the telescope response. The detector geometry, as well as details about the surrounding materials and the location of the telescopes have been included in the simulations in order to realistically reproduce the experimental set-up of each telescope. A model based on the latest parametrization of the cosmic muon flux has been used to generate single muon events. After validating the framework by comparing simulations to selected EEE telescope data, it has been used to determine detector parameters not accessible by analysing experimental data only, such as detection efficiency, angular and spatial resolution.

Abbrescia, M.↗
DOE OSTI2021

Inelastic peridynamic model for molecular crystal particles

The peridynamic theory of solid mechanics is applied to modeling the deformation and fracture of micrometer-sized particles made of organic crystalline material. A new peridynamic material model is proposed to reproduce the elastic–plastic response, creep, and fracture that are observed in experiments. The model is implemented in a three-dimensional, meshless Lagrangian simulation code. In the small deformation, elastic regime, the model agrees well with classical Hertzian contact analysis for a sphere compressed between rigid plates. Under higher load, material and geometrical nonlinearity is predicted, leading to fracture. Finally, the material parameters for the energetic material CL-20 are evaluated from nanoindentation test data on the cyclic compression and failure of micrometer-sized grains.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Machine-Learning-Based Multiscale Methods for 3D Modelling of Granular Materials by Incorporating History-Dependent State Variables

Over the past decades, the prevalence of machine learning (ML) methods has made the development of ML-based constitutive models for granular materials undoubtedly a popular subject. Numerous studies have been made to feature the loading path or history-dependent stress-strain response of granular media using neural networks. In this work, a novel finite element method (FEM)–ML multiscale approach was developed by incorporating internal variables to improve the simulation accuracy of 3D history-dependent granular materials for the first time. To this end, a surrogate constitutive model based on the single-step-based multi-layer perceptron (MLP) neural network was used to replace representative volume element (RVE) simulations conducted by the discrete element method (DEM) in the multiscale FEM–DEM approach. Although the prediction principle of the MLP aligns with the FEM algorithm, artificially added internal variables are required to differentiate the loading history. To address this issue, history variables associated with the Frobenius norm are proposed to be fed into the MLP coupled with the strain tensor to extract the history-dependent behaviour of granular assemblies. The developed FEM–ML approach was demonstrated in 3D conventional triaxial compression (CTC) simulations. Compared to the multiscale FEM–DEM approach, the proposed FEM–ML method exhibits a significantly improved computational efficiency.

granular materials↗