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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Systems, devices, and methods for contact measurement and modulation of material properties

Materials and devices are provided for the sensing and manipulation of biomechanical and physiochemical properties of tissues or tissue surfaces. Examples of use include soft tissues such as skin or adipose tissues or more dense tissues such as muscle or heart or dense tissues such as bone. The materials and devices provide for in vivo measurements of biomechanical properties at the tissue surface, e.g. near surface regions of the epidermis or dermis or underlying structures. The devices can be non-invasive and/or non-destructive to the material and, especially for the biomaterials, can be biocompatible and/or biodegradable. The materials and devices can use ultrathin, stretchable networks of mechanical actuators and sensors constructed with nanoribbons of piezoelectric materials.

Dagdeviren, Canan↗
DOE OSTI2024

Spacecraft surface charging as a function of material properties

Spacecraft material behavior plays a very important role in space missions. Spacecraft immersed in plasma get charged by absorbing plasma particles and by emitting electrons from spacecraft surfaces via photoelectron and secondary electron emission. Spacecraft charging depends heavily on material properties such as work function, secondary electron yield, dielectric constant, and electric conductivity among other. Material properties are typically assumed to be static in charging models. However, it is well known that this is not the case in space. This makes spacecraft charging predictions very challenging. Material properties are well characterized before the spacecraft is put in orbit through characterization in the lab under clean conditions. However, once in space, material properties change due to the harsh and very dynamic space environment. We present a new capability to predict material behavior in space from first-principles modeling. The ongoing effort seeks to couple material models, density functional theory (DFT) and molecular dynamic (MD) codes, with environment models, plasma kinetic codes. This preliminary study will show results of surface charging as a function of material work function, dielectric constant, and conductivity.

36 MATERIALS SCIENCE↗
DOE OSTI2020

NSUF Rad-AFM: Nanoscale Material Property Measurements of Radioactive Materials

Many advances in our understanding of radiation induced damage mechanisms are due to imaging techniques including transmission electron microscopy and scanning electron microscopy. However, the effect of these radiation induced defects characterized on the atomic to micrometer scale are often interpreted by changes in bulk material properties measured in the hundreds of microns to cm scale. In order to bridge this gap, additional tools need to be developed for use with radioactive materials. To this end a new Asylum Infinity multimodal atomic force microscope (AFM) has been added to the library of Nuclear Science User Facility (NSUF) instruments for use on radioactive materials. This instrument is housed in the Radiochemical Processing Laboratory (RPL) which is a Department of Energy Hazard Category II Non-Reactor Nuclear Facility operated by Battelle at Pacific Northwest National Laboratory. This allows for the handling, sample preparation, and characterization of highly radioactive materials providing a unique capability to understand fundamental properties of nuclear materials and the effects of radiation induced damage.

Riechers, Shawn L.↗
DOE OSTI2023

Machine Learning for First Principles Calculations of Material Properties for Ferromagnetic Materials

The investigation of finite temperature properties using Monte-Carlo (MC) methods requires a large number of evaluations of the system’s Hamiltonian to sample the phase space needed to obtain physical observables as function of temperature. DFT calculations can provide accurate evaluations of the energies, but they are too computationally expensive for routine simulations. To circumvent this problem, machine-learning (ML) based surrogate models have been developed and implemented on high-performance computing (HPC) architectures. In this paper, we describe two ML methods (linear mixing model and HydraGNN) as surrogates for first principles density functional theory (DFT) calculations with classical MC simulations. These two surrogate models are used to learn the dependence of target physical properties from complex compositions and interactions of their constituents. We present the predictive performance of these two surrogate models with respect to their complexity while avoiding the danger of overfitting the model. An important aspect of our approach is the periodic retraining with newly generated first principles data based on the progressive exploration of the system’s phase space by the MC simulation. The numerical results show that HydraGNN model attains superior predictive performance compared to the linear mixing model for magnetic alloy materials.

Eisenbach, Markus↗
DOE OSTI2026

Coupling Microstructural Evolution Simulations to Material Property Degradation Predictions for Plasma-Facing Materials

Reliable material performance is required for plasma-facing material (PFM) candidates. Previous research has shown that plasma and neutron radiation exposure induces microstructural changes in PFMs; changes in thermal and electrical conductivities and in material hardening and embrittlement were also observed after neutron irradiation. These material property changes will negatively impact the performance of the PFMs in a fusion reactor. Despite the well-known connection between material microstructure, properties, and performance, there is a need for validated modeling capabilities connecting PFM property degradation with microstructural evolution under fusion-relevant conditions. We are developing a simulation capability to couple plasma-induced microstructural evolution to material property degradation. Our approach relies on deliberate mapping between individual simulation models and experimental characterization for validation. The open-source Multiphysics Object-Oriented Simulation Environment (MOOSE) software was used for this simulation capability development. A MOOSE phase-field model was coupled with the cluster dynamics code, Xolotl, to predict microstructural evolution. Microstructure characterization techniques, including scanning electron microscopy (SEM), transmission electron microscopy (TEM), and laser scanning confocal microscopy (LSCM) are used to validate these microstructural evolution simulations. Calculation of thermal and electrical conductivities with first principles simulations was performed for bulk material and for grain boundaries; these results are used within MOOSE models to calculate effective thermal and electrical conductivities as a function of grain characteristics. Thermoreflectance and four-probe techniques were employed to measure the thermal and electrical conductivities, respectively. A MOOSE crystal plasticity model was adapted to predict microstructure-sensitive deformation behavior, and X-ray diffraction (XRD) was used to collect bulk dislocation density data for validation. After individual simulation validation, these models are coupled to predict material property changes resulting from plasma exposure. We focused here on an experimental design to emphasize the separate effects of moderate thermal loads and plasma exposure using tungsten. Annealing of tungsten was performed under a protective environment for temperatures ranging from 500 C to 1500 C. The plasma exposure was completed in the Tritium Plasma Experiment at Idaho National Laboratory under a deuterium flux of 1e22 D/m^2-s. This incremental approach is employed to build confidence in the modeling capability: separate-effects tests ensure that the models capture key mechanisms from single environmental conditions before predicting PFM property degradation under combined loads. We will show our early results from coupling these simulation models to predict PFM property changes from microstructural evolution. Comparisons of the simulation results with preliminary validation data will be discussed.

36 - MATERIALS SCIENCE↗
DOE OSTI2019

A Brief Note About Scaling Material Properties

Often tables of material properties are computed for a specific chemical or isotopic composition. When we use those pre-computed tables to model real experiments, there can be differences between the as-built material compositions and the pre-computed tables. This document describes several ways that we can scale these tables to account for this. We typically scale every lookup we make into the table in an attempt to account for dynamic changes in material composition over the coarse of the calculation. Even if the compositions are not changing dynamically in the calculation, these same scalings can be used to adjust the material properties from a near by composition to the as-built composition.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Adaptive Interface-PINNs (AdaI-PINNs) for inverse problems: Determining material properties for heterogeneous systems

Here, we determine spatially varying discontinuous material properties using a domain-decomposition based physics-informed neural networks (PINNs) framework named the Adaptive Interface-PINNs or AdaI-PINNs (Roy et al., 2024). We propose the use of distinct neural networks for the field variables and material properties within each material, utilizing adaptive activation functions. While the neural networks across different materials share the same weights and biases, their activation functions are uniquely tailored using a hyperparameter that influences the slope of the activation function. The proposed framework is tested on several one-dimensional and two-dimensional benchmark examples, and its performance is compared with conventional PINNs and existing domain-decomposition PINNs frameworks, namely, the Multi-domain physics-informed neural network (M-PINN), and the eXtended physics-informed neural networks (XPINNs). The results demonstrate that the proposed approach can determine randomly distributed discontinuous material properties with an L 2 error of $\mathscr{O}$ (10 -3 ) for the material property and the root-mean-square error of $\mathscr{O}$ (10 -3 ) for the primary variable while the other approaches yield errors that are approximately two orders of magnitude larger (that is, $\mathscr{O}$ (10 -1 )). Moreover, the spatial distribution of material properties obtained using the proposed framework is in close agreement with the true distribution, whereas the other approaches fare much worse. Additionally, the proposed approach is approximately 40% faster than its competitors, indicating its potential as a robust alternative for solving inverse problems in heterogeneous materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Common workflows for computing material properties using different quantum engines

The prediction of material properties based on density-functional theory has become routinely common, thanks, in part, to the steady increase in the number and robustness of available simulation packages. This plurality of codes and methods is both a boon and a burden. While providing great opportunities for cross-verification, these packages adopt different methods, algorithms, and paradigms, making it challenging to choose, master, and efficiently use them. We demonstrate how developing common interfaces for workflows that automatically compute material properties greatly simplifies interoperability and cross-verification. We introduce design rules for reusable, code-agnostic, workflow interfaces to compute well-defined material properties, which we implement for eleven quantum engines and use to compute various material properties. Each implementation encodes carefully selected simulation parameters and workflow logic, making the implementer’s expertise of the quantum engine directly available to non-experts. All workflows are made available as open-source and full reproducibility of the workflows is guaranteed through the use of the AiiDA infrastructure.

36 MATERIALS SCIENCE↗
DOE OSTI2020

AGC 3 Irradiated Material Properties Analysis

This report documents the analysis of the irradiated material property data from the Advanced Graphite Creep (AGC)-3 graphite specimens. This is the third in a series of six irradiation test trains planned as part of the AGC experiment to fully characterize the neutron irradiation effects and radiation creep behavior of current nuclear graphite grades. The AGC-3 capsule was irradiated in the Idaho National Laboratory Advanced Test Reactor at a nominal temperature of 800°C, beginning with irradiation Cycle 152B on November 28, 2012 and ending with Cycle 155B on April 12, 2014, with a total received dose range of 0.9–3.7 dpa. Larger creep and control specimens located more centrally in the capsule received a dose of 1.0 – 3.7 dpa. AGC-3 was designed to provide irradiation conditions similar to AGC-1 and AGC-2 capsules (similar graphite grades tested, specimen dimensions, mechanical loading conditions) but at a different nominal irradiation temperature of 800°C. AGC-3 was irradiated for a short duration to provide material property values at lower dose levels. AGC-4 will have a longer duration and provide material property values at higher dose levels. After irradiation, material property and dimensional strain measurements were conducted on all AGC-3 specimens (from 11 nuclear graphite grades) using the same equipment and approved standards as were conducted before irradiation. The specimen loading configuration for all graphite grades within AGC-3 followed a similar pattern as earlier AGC capsules to provide easy future comparison of all irradiated material property data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Deciphering chemical order/disorder and material properties at the single-atom level

Perfect crystals are rare in nature. Real materials often contain crystal defects and chemical order/disorder such as grain boundaries, dislocations, interfaces, surface reconstructions and point defects. Such disruption in periodicity strongly affects material properties and functionality. Despite rapid development of quantitative material characterization methods, correlating three-dimensional (3D) atomic arrangements of chemical order/disorder and crystal defects with material properties remains a challenge. On a parallel front, quantum mechanics calculations such as density functional theory (DFT) have progressed from the modelling of ideal bulk systems to modelling ‘real’ materials with dopants, dislocations, grain boundaries and interfaces; but these calculations rely heavily on average atomic models extracted from crystallography. To improve the predictive power of first-principles calculations, there is a pressing need to use atomic coordinates of real systems beyond average crystallographic measurements. Here we determine the 3D coordinates of 6,569 iron and 16,627 platinum atoms in an iron-platinum nanoparticle, and correlate chemical order/disorder and crystal defects with material properties at the single-atom level. We identify rich structural variety with unprecedented 3D detail including atomic composition, grain boundaries, anti-phase boundaries, anti-site point defects and swap defects. We show that the experimentally measured coordinates and chemical species with 22 picometre precision can be used as direct input for DFT calculations of material properties such as atomic spin and orbital magnetic moments and local magnetocrystalline anisotropy. The work presented here combines 3D atomic structure determination of crystal defects with DFT calculations, which is expected to advance our understanding of structure–property relationships at the fundamental level.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Moving closer to experimental level materials property prediction using AI

Abstract While experiments and DFT-computations have been the primary means for understanding the chemical and physical properties of crystalline materials, experiments are expensive and DFT-computations are time-consuming and have significant discrepancies against experiments. Currently, predictive modeling based on DFT-computations have provided a rapid screening method for materials candidates for further DFT-computations and experiments; however, such models inherit the large discrepancies from the DFT-based training data. Here, we demonstrate how AI can be leveraged together with DFT to compute materials properties more accurately than DFT itself by focusing on the critical materials science task of predicting “formation energy of a material given its structure and composition”. On an experimental hold-out test set containing 137 entries, AI can predict formation energy from materials structure and composition with a mean absolute error (MAE) of 0.064 eV/atom; comparing this against DFT-computations, we find that AI can significantly outperform DFT computations for the same task (discrepancies of $$>0.076$$ > 0.076 eV/atom) for the first time.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Assessment of Material Properties for TRISO Fuel Particles in PARFUME

The purpose of this study is to identify the material properties that have the largest impact on the failure probability of tristructural isotropic (TRISO)-coated fuel particles under irradiation. For the most part, these material properties were obtained from experimental data of historical TRISO fuel development programs in Europe and the United States. The adequacy of these historical constitutive material properties is being evaluated as part of the Advanced Gas Reactor Fuel Development and Qualification Program. The TRISO fuel performance modeling code PARFUME was used to assess the material properties that have the main influence on the probability of failure of the silicon carbide (SiC) layer of TRISO fuel particles. Although some of these material properties exhibit a large variability according to published data, only a few of them have a significant impact on the SiC failure probability, namely the irradiation-induced creep and dimensional change of the inner pyrolytic carbon layer.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Estimating Material Properties Under Extreme Conditions by Using Bayesian Model Calibration with Functional Outputs

Summary Dynamic material properties experiments provide access to the most extreme temperatures and pressures attainable in a laboratory setting; the data from these experiments are often used to improve our understanding of material models at these extreme conditions. We apply Bayesian model calibration to dynamic material property applications where the experimental output is a function: velocity over time. This framework can accommodate more uncertainties and facilitate analysis of new types of experiments relative to techniques traditionally used to analyse dynamic material experiments. However, implementation of Bayesian model calibration requires more sophisticated statistical techniques, because of the functional nature of the output as well as parameter and model discrepancy identifiability. We propose a novel Bayesian model calibration process to simplify and improve the estimation of the material property calibration parameters. Specifically, we propose scaling the likelihood function by an effective sample size rather than modelling the auto-correlation function to accommodate the functional output. Additionally, we propose sensitivity analyses by using the notion of 'modularization' to assess the effect of experiment-specific nuisance input parameters on estimates of the physical parameters. The Bayesian model calibration framework proposed is applied to dynamic compression of tantalum to extreme pressures, and we conclude that the procedure results in simple, fast and valid inferences on the material properties for tantalum.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Material Properties of Non-Irradiated Zircaloy 4 in Support of ASME Code Acceptance for Pressure Vessel Design

A program underway at Oak Ridge National Laboratory (ORNL) to support private industry design of a system for production of 99Mo includes review of Zircaloy-4 (Zry-4) properties as a potential material for a primary vessel in the system. Because Zry-4 is not a material specified in the ASME Boiler and Pressure Vessel Code, a review has been performed to obtain the necessary data in the non-irradiated condition from the literature and from additional testing of Zry-4 to enable creation of tables of material properties required for ASME Code Section III Article ND-3000 design. A strategy for an in-vessel surveillance program is also recommended for early detection of potential corrosion and irradiation degradation issues during operational life of the vessel. Additionally, the ASME Code Section II, Part D was reviewed to ascertain the requirements for proposing a new material for Code approval. A plate of Zry-4 procured by ORNL was used to obtain tensile properties that were used as the basis for determining the maximum allowable stresses applicable to the vessel design. The materials properties of primary interest for ASME Code application are tensile properties, fracture toughness, fatigue, and five specific physical properties required by the Code for approval of a new material. The literature review revealed that all reported experimental data showed tensile strengths to substantially exceed the minimum specifications in ASTM B352. Sufficient fracture toughness and fatigue data are available to conclude that Zry-4 has sufficient fracture and fatigue resistance in the unirradiated condition for vessel design and operation. Of the physical properties reviewed, only thermal conductivity revealed a discrepancy that is recommended for further review. A surveillance program plan has been developed that is probably the maximum that would need to be implemented for the specific vessel design considered. Once adequate non-irradiated and irradiated data are available from preproduction testing of the Zry-4 and weld metal and HAZ materials, the program could be reduced to reflect that either initial properties are very good and/or the effects of embrittlement appear to be minimal. However, the overall design of a surveillance program is, of course, also dependent on the ASME Code design class for the pressure vessel.

Nanstad, Randy↗