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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 37 records · Page 2
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DOE OSTI2026

MatLib-1.2.2: Nuclear Material Properties Library

The U.S. Nuclear Regulatory Commission (NRC) uses the computer code Fuel Analysis under Steady-state and Transients (FAST) to model steady-state and transient fuel behavior to support regulatory decisions. FAST relies on a material properties library (MatLib) that contains the thermal and mechanical properties of the nuclear materials and coolants of interest to support the U.S. commercial nuclear industry. MatLib contains properties for a variety of nuclear fuels, cladding and other structural materials, gases, and coolants. In this document, material property correlations for the materials contained within MatLib are presented and discussed. When available, comparisons are made between the material property correlations and available data. Additionally, uncertainties are quantified on the material properties, which is then used by the NRC to support uncertainty quantification for best-estimate plus uncertainty safety evaluation reviews. This document describes MatLib-1.2.2, updated from MatLib-1.2.1 to include additional properties for metallic fuel. It is one of a series of documents on FAST; the other documents detail the models used by FAST as well as its integral assessment to experiments and commercial data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks

With the increased use of data-driven approaches and machine learning-based methods in material science, the importance of reliable uncertainty quantification (UQ) of the predicted variables for informed decision-making cannot be overstated. UQ in material property prediction poses unique challenges, including multi-scale and multi-physics nature of materials, intricate interactions between numerous factors, limited availability of large curated datasets, etc. In this work, we introduce a physics-informed Bayesian Neural Networks (BNNs) approach for UQ, which integrates knowledge from governing laws in materials to guide the models toward physically consistent predictions. To evaluate the approach, we present case studies for predicting the creep rupture life of steel alloys. Experimental validation with three datasets of creep tests demonstrates that this method produces point predictions and uncertainty estimations that are competitive or exceed the performance of conventional UQ methods such as Gaussian Process Regression. Additionally, we evaluate the suitability of employing UQ in an active learning scenario and report competitive performance. The most promising framework for creep life prediction is BNNs based on Markov Chain Monte Carlo approximation of the posterior distribution of network parameters, as it provided more reliable results in comparison to BNNs based on variational inference approximation or related NNs with probabilistic outputs.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Defining Computational Emissivity Uncertainty Over Large Temperature Scales Due to Surface Evolution

There is a dearth in the literature on how to capture the uncertainty generated by material surface evolution in thermal modeling. This leads to inadequate or highly variable uncertainty representations for material properties, specifically emissivity when minimal information is available. Inaccurate understandings of prediction uncertainties may lead decision makers to incorrect conclusions, so best engineering practices should be developed for this domain. In order to mitigate the aforementioned issues, this study explores different strategies to better capture the thermal uncertainty response of engineered systems exposed to fire environments via defensible emissivity uncertainty characterizations that can be easily adapted to a variety of use cases. Here, two unique formulations (one physics-informed and one mathematically based) are presented. The formulations and methodologies presented herein are not exhaustive but more so are a starting point and give the reader a basis for how to customize their uncertainty definitions for differing fire scenarios and materials. Lastly, the impact of using this approach versus other commonly used strategies and the usefulness of adding rigor to material surface evolution uncertainty is demonstrated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Effects of Photovoltaic Module Materials and Design on Module Deformation Under Load

Static structural finite element models of an aluminum-framed crystalline silicon (c-Si) photovoltaic (PV) module and a glass-glass thin film PV module were constructed and validated against experimental measurements of deflection under uniform pressure loading. Parametric analyses using Latin Hypercube Sampling (LHS) were performed to propagate simulation input uncertainties related to module material properties, dimensions, and manufacturing tolerances into expected uncertainties in simulated deflection predictions. This exercise verifies the applicability and validity of finite element modeling for predicting mechanical behavior of solar modules across architectures and enables computational models to be used with greater confidence in assessment of module mechanical stressors and design for reliability. Sensitivity analyses were also performed on the uncertainty quantification data sets using linear correlation coefficients to elucidate the key parameters influencing module deformation. This information has implications on which materials or parameters may be optimized to best increase module stiffness and reliability, whether the key optimization parameters change with module architecture or loading magnitudes, and whether parameters such as frame design and racking must be replicated in reduced-scale reliability studies to adequately capture full module mechanical behavior.

14 SOLAR ENERGY↗
DOE OSTI2020

Hydraulic Properties Data Package for the E-Area Soils, Cementitious Materials, and Waste Zones–Update

Hydraulic property estimates for the soils, cementitious materials, and waste zones associated with the E-Area low-level radioactive waste disposal units have been provided to support the Performance Assessments (PA) for the E-Area Low-Level Waste Facility (LLWF). Nominal or “best estimate” hydraulic property values for use in the deterministic modeling are provided along with representations of the hydraulic property value uncertainty for use in sensitivity and uncertainty modeling. The hydraulic properties provided for each of the E-Area materials include: porosity ( η ), dry bulk density ( ρ b ), particle density (ρ p ), saturated hydraulic conductivity ( K sat ), characteristic curves (suction head, saturation, and relative permeability), and effective diffusion coefficient ( D e ). A representation of the uncertainty associated with each property, except for the characteristic curves, is provided for each material, except for the E-Area waste zones. These nominal parameter values for each of the E-Area soils, cementitious materials, and waste zones, and, where indicated, parameter uncertainty representations, are based upon the following in order of priority: Site-specific field data, Site-specific laboratory data, Similarity to material with site-specific field or laboratory data, and Literature data. Additionally, a methodology to represent long-term concrete degradation is provided. Finally, because much of the nominal hydraulic property values and uncertainty representations for the E-Area soils, cementitious materials, and waste zones are based on similarity to other materials or literature data, a methodology to prioritize additional work to better define these values and representations is outlined. Prioritization should be based on the importance of the material and/or property to the results of deterministic, sensitivity, and uncertainty modeling. This prioritization should be established through a process of sensitivity modeling. This report supersedes the previous hydraulic properties data package. Revision 1 contains clarifications, updates and corrections to text, figures and tables in a number of places in the report along with some minor editing.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Effects of Impact and Target Parameters on the Results of a Kinetic Impactor: Predictions for the Double Asteroid Redirection Test (DART) Mission

Abstract The Double Asteroid Redirection Test (DART) spacecraft will impact into the asteroid Dimorphos on 2022 September 26 as a test of the kinetic impactor technique for planetary defense. The efficiency of the deflection following a kinetic impactor can be represented using the momentum enhancement factor, β , which is dependent on factors such as impact geometry and the specific target material properties. Currently, very little is known about Dimorphos and its material properties, which introduces uncertainty in the results of the deflection efficiency observables, including crater formation, ejecta distribution, and β . The DART Impact Modeling Working Group (IWG) is responsible for using impact simulations to better understand the results of the DART impact. Pre-impact simulation studies also provide considerable insight into how different properties and impact scenarios affect momentum enhancement following a kinetic impact. This insight provides a basis for predicting the effects of the DART impact and the first understanding of how to interpret results following the encounter. Following the DART impact, the knowledge gained from these studies will inform the initial simulations that will recreate the impact conditions, including providing estimates for potential material properties of Dimorphos and β resulting from DART’s impact. This paper summarizes, at a high level, what has been learned from the IWG simulations and experiments in preparation for the DART impact. While unknown, estimates for reasonable potential material properties of Dimorphos provide predictions for β of 1–5, depending on end-member cases in the strength regime.

58 GEOSCIENCES↗
DOE OSTI2025

Uncertainty Quantification for Smooth Functional Data with Application to Material Properties

This document outlines a method for processing functional output (i.e., curves) for the ultimate purpose of sampling curves under specified input conditions for use in modeling and simulation uncertainty quantification (UQ) studies. A set of benchmark curves sufficiently representative of the relevant scenario(s) being simulated are provided to the process and formatted as described in Section 1. Principal Component Analysis (PCA) is utilized to discover the components of uncertainty in the benchmark curves and is outlined in Section 2. Section 3 describes the application of uncertainty quantification to the PCA results for the purpose of sampling curves to be used in UQ analysis. Section 4 applies these techniques to an example benchmark dataset. Concluding remarks are provided in the final section.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Stress evolution and creep deformation in solid-oxide electrolysis cell systems – Dynamic modeling and multi-objective optimization to maximize stack life and efficiency

Here, this study develops a thermal stress model of solid-oxide electrolysis cells (SOECs) including a model for creep strain and failure probability that is integrated with a dynamic plant-wide model of a hydrogen production process. Uncertainties in key material properties of the cell are quantified to assess their impact on stress profile variability. The oxygen electrode is found to have about 10 times higher failure probability compared to the fuel electrode. The study shows that if the stack operation is not optimized, cycling operation would lead to stress build-up eventually leading to catastrophic failure. A dynamic optimization problem is set up for obtaining the optimal operational profile considering a variable hydrogen production rate. Due to the tradeoff between the efficiency and stress build-up, the dynamic optimization problem is multi-objective. It is observed that the optimizer can considerably reduce the stress build-up (i.e., can increase the stack life) albeit at the cost of a lower efficiency thus exhibiting strong tradeoffs between capital and operating costs. For example, if the stack would be replaced in 0.5 yr, specific energy requirement would be 48.5 kWh/kg H 2 while for a stack replacement time of about 6 yr, the specific energy requirement rises by about 4.2 %.

SOEC↗
DOE OSTI2020

Probabilistic modeling and prediction of out-of-plane unidirectional composite lamina properties

Computational simulation provides an efficient means to predict the behavior of customized hybrid material configurations using validated, physics-based models. One limitation to this approach is the quality and quantity of available data to characterize the many constituent input properties. Therefore, a systematic approach to identify the most influential parameters on the hybrid behavior and quantify the corresponding uncertainty in predictive capabilities is required. Here, an approach using Bayesian multimodel inference and imprecise global sensitivity analysis is presented to investigate the effects of sparse constituent data on the prediction of composite material properties. The methodology allows the identification, using quantified uncertainties, of the most influential constituent material parameters for specified homogenized properties. This sensitivity analysis further enables a dimension reduction when assessing the influence of uncertainties on material properties and can be used to inform testing programs of the constituent properties that require additional testing/data collection in order to minimize uncertainty in macro-scale composite properties. The methodology is specifically demonstrated on the prediction and sensitivity analysis of out-of-plane mechanical properties of a unidirectional lamina.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Review of Parameter Ranges for Uncertainty Estimation for Decomposing Carbon Fiber Epoxy Composites

Carbon fiber epoxy composites are increasingly used in systems requiring a material that is both strong and light weight, as in airplanes, cars, and pressure vessels. In fire environments, carbon fiber epoxy composites are a fuel source subject to oxidation. This literature review seeks to provide material properties as well as uncertainty bounds for those properties for computational models of decomposing carbon fiber epoxy composites. The goal is to guide analysts when measurements are lacking and increase credibility of uncertainty quantification ranges.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Progress on Demonstration of a MOOSE-Based Coupled Capability for Hot Channel Factors in Fast Reactors

Hot channel factors (HCFs) are computed values that account for the impact on predicted peak fuel, cladding, and coolant temperatures due to uncertainties in the as-built reactor’s material properties and geometry as well as uncertainties due to modeling approximations. Reduction in computed HCF values via reduction or elimination of modeling approximations may translate to significant economic savings if the reactor power can be raised due to the extra temperature margin gained. While limited historical datasets exist for sodium-cooled fast reactors (SFRs), there are no available HCF data for lead-cooled fast reactors (LFRs) outside of work generated previously within NEAMS. The computation of HCFs involves insights from reactor physics, thermal fluids and heat conduction calculations to determine how the peak temperatures respond to various uncertainties in the design. Due to the significant advantages for multi-physics coupling offered by the MOOSE framework, Griffin (MOOSE-based reactor physics code), MOOSE Heat Conduction Module, and Cardinal (MOOSE-wrapped multi-physics application which includes the NekRS thermal fluids code) are being coupled together using the MOOSE MultiApp System to develop a highfidelity multi-physics modeling capability for HCF simulations. This high-fidelity coupling workflow may also be beneficial for other fast reactor applications in the future. In previous work, Griffin and NekRS were individually assessed to ensure the necessary capabilities were in place. This work describes initial efforts to couple the codes (including folding in the MOOSE Heat Conduction Module) and determining the workflow for the perturbed calculations which will leverage the Stochastic Tools Module (STM). To our knowledge, this is the first coupling of Griffin and NekRS as well as the first exploratory use of Stochastic Tools Module for Cardinal. In this report, the neutronics code Griffin, the heat conduction solver in MOOSE, and the MOOSE-wrapped application containing NekRS (Cardinal) are linked together to demonstrate the coupled capability. Griffin and Cardinal are linked dynamically by specifying shared libraries. Different coupling hierarchies are tested for selecting the most appropriate coupling strategy. A coupling scheme is selected based on the efficiency of calculation and ease of data communication. Multiple tests are performed to choose suitable mesh structure, model configurations, scheme setup and boundary conditions to avoid loss of energy due to data interpolation between different modules or weak imposition of fluxes in finite element codes. Computational experiments are performed to study the tolerance control of each type of iteration to avoid false convergence. The coupled capability is demonstrated in both single pin and 7-pin models based on LFR materials and geometry. The study finds that the use of too large a time step size in the heat conduction module can lead to temperature oscillation even though the heat conduction equation does not have a time-derivative kernel, but only the time-dependent boundary condition. A 7-pin model without duct region achieved good convergence in the coupled calculation while a 7 pin model with duct region experienced data communication issues which need to be resolved.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Clarifying trust of materials property predictions using neural networks with distribution-specific uncertainty quantification

It is critical that machine learning (ML) model predictions be trustworthy for high-throughput catalyst discovery approaches. Uncertainty quantification (UQ) methods allow estimation of the trustworthiness of an ML model, but these methods have not been well explored in the field of heterogeneous catalysis. Herein, we investigate different UQ methods applied to a crystal graph convolutional neural network to predict adsorption energies of molecules on alloys from the Open Catalyst 2020 dataset, the largest existing heterogeneous catalyst dataset. We apply three UQ methods to the adsorption energy predictions, namely k-fold ensembling, Monte Carlo dropout, and evidential regression. The effectiveness of each UQ method is assessed based on accuracy, sharpness, dispersion, calibration, and tightness. Evidential regression is demonstrated to be a powerful approach for rapidly obtaining tunable, competitively trustworthy UQ estimates for heterogeneous catalysis applications when using neural networks. Recalibration of model uncertainties is shown to be essential in practical screening applications of catalysts using uncertainties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Fuel performance uncertainty quantification and sensitivity analysis in the presence of epistemic and aleatoric sources of uncertainties

Fuel performance modeling and simulation includes many uncertain parameters from models to boundary conditions, manufacturing parameters and material properties. These parameters exhibit large uncertainties and can have an epistemic or aleatoric nature, something that renders fuel performance code-to-code and code-to-measurements comparisons for complex phenomena such as the pellet cladding mechanical interaction (PCMI) very challenging. Additionally, PCMI and other complex phenomena found in fuel performance modeling and simulation induce strong discontinuities and non-linearities that can render difficult to extract meaningful conclusions form uncertainty quantification (UQ) and sensitivity analysis (SA) studies. In this work, we develop and apply a consistent treatment of epistemic and aleatoric uncertainties for both UQ and SA in fuel performance calculations and use historical benchmark-quality measurement data to demonstrate it. More specifically, the developed methodology is applied to the OECD/NEA Multi-physics Pellet Cladding Mechanical Interaction Validation benchmark. A cold ramp test leading to PCMI is modeled. Two measured quantities of interest are considered: the cladding axial elongation during the irradiations and the cladding outer diameter after the cold ramp. The fuel performance code used to perform the simulation is FAST. The developed methodology involves various steps including a Morris screening to decrease the number of uncertain inputs, a nested loop approach for propagating the epistemic and aleatoric sources of uncertainties, and a global SA using Sobol indices. The obtained results indicate that the fuel and cladding thermal conductivities as well as the cladding outer diameter uncertainties are the three inputs having the largest impact on the measured quantities. More importantly, it was found that the epistemic uncertainties can have a significant impact on the measured quantities and can affect the outcome of the global sensitivity analysis.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

MatLib-1.2: Nuclear Material Properties Library

The U.S. Nuclear Regulatory Commission (NRC) uses the computer code Fuel Analysis under Steady-state and Transients (FAST) to model steady-state and transient fuel behavior to support regulatory decisions. FAST relies on a material properties library (MatLib) that contains the thermal and mechanical properties of the nuclear materials and coolants of interest to support the US commercial nuclear industry. MatLib contains properties for a variety of nuclear fuels, cladding and other structural materials, gases, and coolants. In this document, material property correlations for the materials contained within MatLib are presented and discussed. When available, comparisons are made between the material property correlations and available data. Additionally, uncertainties are quantified on the material proper- ties, which is then used by the NRC to support uncertainty quantification for best-estimate plus uncertainty safety evaluation reviews. This document describes MatLib-1.2, which was updated from MatLib-1.1 to include cleanup of the code for streamlined calls to MatLib from the main FAST source code, and to enable easier readability of the code by developers that maintain and enhance the FAST code. The results of MatLib did not change between MatLib-1.1 and MatLib-1.2; however, the source code changes warranted incrementing the minor numbering of the software version number. The changes may result in faster execution times of FAST-1.2 as compared to FAST-1.1. This document is one of a series of documents on FAST; the other documents detail the models used by FAST as well as its integral assessment to experiments and commercial data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Flow boiling transient critical heat flux tests with stainless steel and FeCrAl: Transient correlation implementation, model calibration, and sensitivity analysis

In this study, steady-state and transient internal-flow critical heat flux (CHF) experiments were carried out under two flow conditions, at atmospheric pressure, on 316 stainless steel (316-SS) and iron-chromium-aluminum (FeCrAl) tubes. Slow power transients at a low mass flow (300 kg/m 2 - s) with very low subcooling (X e =-0.0054) generated premature CHFs, which were prevented by faster power transients and high mass flow (1000 kg/m 2 - s). The measured transient CHFs increased linearly with increasing power transients, compared with the steady-state CHF. Yet, the wall superheat at the CHFs decreased with faster power transients. Transient CHF correlations highlighting heterogeneous spontaneous nucleation were calibrated to the measured CHFs and compared with other existing correlations. Transient CHF multipliers were acquired from pool and flow boiling empirical CHF correlations that were generated. The multipliers were applied to the RELAP5-3D nuclear system code to analyze the discrepancies between the measured data and the predicted CHF and post-CHF behavior, which improved peak cladding temperature predictions by 24.6%. A variance-based global sensitivity study perturbing the experimental uncertainties and cladding material thermal properties showed the diminishing influence of flow boiling heat transfer with increasing power transients, highlighting the significance of the volumetric heat capacity for the cladding integrity during transients. Transient CHF correlations were applied to the most limiting design basis accident: a hot-zero-power reactivity-initiated accident on a generic pressurized water reactor RELAP5-3D model.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Design for Additive Manufacturing: Exploring Remelt Strategies to Tailor Density and Microstructure

The potential advantages of AM (e.g. weight reduction, novel geometries) are well understood within a systems context. However, adoption of AM at the system level has been slow due to the relative uncertainty in the final material properties, which leaves capabilities and/or performance gains unrealized. Utilizing remelt strategies it may be possible to expand the available process window to provide densities and microstructures beyond what is capable with standard scan strategies. This work explored remelting strategies for 316L stainless steel to tailor grain size and increase density. Twelve scan strategies were explored experimentally and computationally to understand the limitations of remelt strategies and the robustness of the current simulation package. Results show tailoring of grain size, density, and texture is achievable through remelting and several key lessons learned were made to improve the texture evaluation through simulation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Development of integrated neutronics and thermal analysis capabilities to support design and optimization of fusion engineering demonstration facility systems and blanket design (Final Scientific/Technical Report)

A fusion engineering demonstration facility would be the first step in the commercial fusion plant developmental pathway that aims to be an engineering demonstration of tritium self-sufficiency. One of the primary requirements for tritium self-sufficiency, as well as to ensure low plant tritium release to the external environment, is to minimize the tritium loss through the internal components, which requires accurate predictions of the tritium behavior for a wide range of materials and system conditions. This grant addressed the lack of a comprehensive model which accounts for particular conditions, such as the significance of temperature fields and neutronics information, and the systematic uncertainty quantification of associated material properties that impact tritium generation, utilization, and loss mechanisms in the blanket.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Development of integrated neutronics and thermal analysis capabilities to support design and optimization of fusion engineering demonstration facility systems and blanket design (Final Scientific/Technical Report)

A fusion engineering demonstration facility would be the first step in the commercial fusion plant developmental pathway that aims to be an engineering demonstration of tritium self- sufficiency. One of the primary requirements for tritium self-sufficiency, as well as to ensure low plant tritium release to the external environment, is to minimize the tritium loss through the internal components, which requires accurate predictions of the tritium behavior for a wide range of materials and system conditions. This grant addressed the lack of a comprehensive model which accounts for particular conditions, such as the significance of temperature fields and neutronics information, and the systematic uncertainty quantification of associated material properties that impact tritium generation, utilization, and loss mechanisms in the blanket.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗