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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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NASA NTRS1997

Parameterizing Grid-Averaged Longwave Fluxes for Inhomogeneous Marine Boundary Layer Clouds

This paper examines the relative impacts on grid-averaged longwave flux transmittance (emittance) for Marine Boundary Layer (MBL) cloud fields arising from horizontal variability of optical depth tau and cloud sides, First, using fields of Landsat-inferred tau and a Monte Carlo photon transport algorithm, it is demonstrated that mean all-sky transmittances for 3D variable MBL clouds can be computed accurately by the conventional method of linearly weighting clear and cloudy transmittances by their respective sky fractions. Then, the approximations of decoupling cloud and radiative properties and assuming independent columns are shown to be adequate for computation of mean flux transmittance. Since real clouds have nonzero geometric thicknesses, cloud fractions A'(sub c) presented to isotropic beams usually exceed the more familiar vertically projected cloud fractions A(sub c). It is shown, however, that when A(sub c)less than or equal to 0.9, biases for all-sky transmittance stemming from use of A(sub c) as opposed to A'(sub c) are roughly 2-5 times smaller than, and opposite in sign to, biases due to neglect of horizontal variability of tau. By neglecting variable tau, all-sky transmittances are underestimated often by more than 0.1 for A(sub c) near 0.75 and this translates into relative errors that can exceed 40% (corresponding errors for all-sky emittance are about 20% for most values of A(sub c). Thus, priority should be given to development of General Circulation Model (GCM) parameterizations that account for the effects of horizontal variations in unresolved tau, effects of cloud sides are of secondary importance. On this note, an efficient stochastic model for computing grid-averaged cloudy-sky flux transmittances is furnished that assumes that distributions of tau, for regions comparable in size to GCM grid cells, can be described adequately by gamma distribution functions. While the plane-parallel, homogeneous model underestimates cloud transmittance by about an order of magnitude when 3D variable cloud transmittances are less than or equal to 0.2 and by approx. 20% to 100% otherwise, the stochastic model reduces these biases often by more than 80%.

Barker, Howard W.↗
DOE OSTI2025

A physiologically based pharmacokinetic (PBPK) model to align dosimetry of the isobutyl metabolic series in rats and humans

Here, we developed a physiologically based pharmacokinetic (PBPK) model in rats and humans for the isobutyl metabolic series including isobutyl acetate, isobutanol, isobutyraldehyde, and isobutyric acid. Chemical manufactures routinely use these compounds as solvents, for chemical synthesis, as potential biofuels, de-icing fluids, and additives for food and/or fragrance in consumer products. Human exposure to isobutyl compounds can occur through inhalation or oral routes. We previously developed a PBPK model for the propyl metabolic series and utilized it as a framework to create the isobutyl PBPK model due to the chemical similarities between the two series. To support model development, we measured in vitro metabolism of isobutyl acetate in rat and human blood and liver S9 fractions. Compared to rats, humans demonstrated faster isobutyl acetate hydrolysis in liver S9 fractions, while the hydrolysis rates in blood were similar between the two species. We used concentrations of isobutyl compounds measured in air and blood from rats exposed to isobutyl acetate and isobutanol as well as other published data to further parameterize the model. Following exposure to either isobutyl acetate or isobutanol, we observed isobutanol concentrations highest among the isobutyl compounds in the blood of rats. Overall, the model accurately predicts measured time course concentrations of isobutyl acetate, isobutanol, and isobutyric acid in available data in rats and humans. Sensitivity analyses identified alveolar ventilation rates, isobutyl metabolism rates, and cardiac output as the most sensitive parameters affecting concentrations of isobutyl compounds in blood. The isobutyl PBPK model enables comparisons of internal dose metrics across various isobutyl compound exposures and species and allows for calculation of equivalent external exposures that result in the same dose metric. Regulators can employ this PBPK model to predict and align internal dose metrics of isobutyl compounds for risk assessment purposes.

2-methyl-1-propanol↗
NASA NTRS2010

A Modeling and Verification Study of Summer Precipitation Systems Using NASA Surface Initialization Datasets

One of the most challenging weather forecast problems in the southeastern U.S. is daily summertime pulse-type convection. During the summer, atmospheric flow and forcing are generally weak in this region; thus, convection typically initiates in response to local forcing along sea/lake breezes, and other discontinuities often related to horizontal gradients in surface heating rates. Numerical simulations of pulse convection usually have low skill, even in local predictions at high resolution, due to the inherent chaotic nature of these precipitation systems. Forecast errors can arise from assumptions within parameterization schemes, model resolution limitations, and uncertainties in both the initial state of the atmosphere and land surface variables such as soil moisture and temperature. For this study, it is hypothesized that high-resolution, consistent representations of surface properties such as soil moisture, soil temperature, and sea surface temperature (SST) are necessary to better simulate the interactions between the surface and atmosphere, and ultimately improve predictions of summertime pulse convection. This paper describes a sensitivity experiment using the Weather Research and Forecasting (WRF) model. Interpolated land and ocean surface fields from a large-scale model are replaced with high-resolution datasets provided by unique NASA assets in an experimental simulation: the Land Information System (LIS) and Moderate Resolution Imaging Spectroradiometer (MODIS) SSTs. The LIS is run in an offline mode for several years at the same grid resolution as the WRF model to provide compatible land surface initial conditions in an equilibrium state. The MODIS SSTs provide detailed analyses of SSTs over the oceans and large lakes compared to current operational products. The WRF model runs initialized with the LIS+MODIS datasets result in a reduction in the overprediction of rainfall areas; however, the skill is almost equally as low in both experiments using traditional verification methodologies. Output from object-based verification within NCAR s Meteorological Evaluation Tools reveals that the WRF runs initialized with LIS+MODIS data consistently generated precipitation objects that better matched observed precipitation objects, especially at higher precipitation intensities. The LIS+MODIS runs produced on average a 4% increase in matched precipitation areas and a simultaneous 4% decrease in unmatched areas during three months of daily simulations.

Jonathan L. Case↗
DOE OSTI2026

ARM Data-Oriented Metrics and Diagnostics Package for Climate Model Evaluation

A Python-based metrics and diagnostics package is currently being developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Infrastructure Team at Lawrence Livermore National Laboratory (LLNL) to facilitate the use of long-term, high-frequency measurements from the ARM Facility in evaluating the regional climate simulation of clouds, radiation, and precipitation. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The Coupled Model Intercomparison Project (CMIP) model data sets are also included in the package to enable model intercomparison as demonstrated in Zhang et al. (2017). The mean of the CMIP model can serve as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, and radiative fluxes, with plan to extend to more fields, such as aerosol and microphysics properties. Process-oriented diagnostics focusing on individual cloud- and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. The version 1.0 package is designed based on data collected at ARM’s Southern Great Plains (SGP) Research Facility, with the plan to extend to other ARM sites. The metrics and diagnostics package is currently built upon standard Python libraries and additional Python packages developed by DOE (such as CDMS and CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first present the input data, which constitutes the core content of the metrics and diagnostics package in section 2, and a user's guide documenting the workflow/structure of the version 1.0 codes, and including step-by-step instruction for running the package in section 3.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

Parameterizing mesoscale and large-scale ice clouds in general circulation models

The paper discusses GCM parameterizations for two types of ice clouds: (1) ice clouds formed by large-scale lifting, often of limited vertical extent but usually of large-scale horizontal extent; and (2) ice clouds formed as anvils in convective systems, often of moderate vertical extent but of mesoscale size horizontally. It is shown that the former type of clouds can be parameterized with reference to an equilibrium between ice generation by deposition from vapor, and ice removal by crystal settling. The same mechanisms operate in the mesoscale clouds, but the ice content in these cases is considered to be more closely linked to the moisture supplied to the anvil by cumulus towers. It is shown that a GCM can simulate widespread ice clouds of both types.

Donner, Leo J.↗
DOE OSTI2024

A Diffusion‐Based Uncertainty Quantification Method to Advance E3SM Land Model Calibration

Abstract Calibrating land surface models and accurately quantifying their uncertainty are crucial for improving the reliability of simulations of complex environmental processes. This, in turn, advances our predictive understanding of ecosystems and supports climate‐resilient decision‐making. Traditional calibration methods, however, face challenges of high computational costs and difficulties in accurately quantifying parameter uncertainties. To address these issues, we develop a diffusion‐based uncertainty quantification (DBUQ) method. Unlike conventional generative diffusion methods, which are computationally expensive and memory‐intensive, DBUQ innovates by formulating a parameterized generative model and approximates this model through supervised learning, which enables quick generation of parameter posterior samples to quantify its uncertainty. DBUQ is effective, efficient, and general‐purpose, making it suitable for site‐specific ecosystem model calibration and broadly applicable for parameter uncertainty quantification across various earth system models. In this study, we applied DBUQ to calibrate the Energy Exascale Earth System Model land model at the Missouri Ozark AmeriFlux forest site. Results indicated that DBUQ produced accurate parameter posterior distributions similar to those from Markov Chain Monte Carlo sampling but with 30 times less computing time. This significant improvement in efficiency suggests that DBUQ can enable rapid, site‐level model calibration at a global scale, enhancing our predictive understanding of climate impacts on terrestrial ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Interconnection of Aerosol‐Cloud Interactions and Cloud Feedback Through Warm Rain Process

Recent research has revealed a correlation within the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations: models exhibiting more pronounced warming due to positive cloud feedback simultaneously show enhanced cooling from aerosol‐cloud interactions (ACI). However, the interplay between ACI and cloud feedback is not well understood in these models. Our study addresses this gap by modifying the autoconversion rate in two Earth system models (ESMs), elucidating how ACI could connect with cloud feedback through the warm rain process. We find that higher autoconversion rates, which are associated with stronger ACI, suppress the increase in cloud liquid water path (LWP) and cloud optical depth with warming by enhancing the precipitation efficiency over the extratropical regions, leading to a larger positive cloud feedback. This study offers new insights into the compensatory mechanism between ACI and cloud feedback through the warm rain process and highlights the importance of constraining the autoconversion parameterization in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

MULTI-LEADER: MULTI-source LEarning-Accelerated Design of high-Efficiency multi-stage compRessor (Final Technical Report)

The objective of MULTI-LEADER is to cut design costs by 80% while generating more energy-efficient designs of multi-stage compressors by developing and implementing novel machine learning (ML) techniques, which enable faster and fewer design iterations, improved solver performance, and concurrent multi-disciplinary design. Current industrial practices for the design of multi-stage compressors involve simulation-based design optimization with successive levels of model fidelity, iteratively evaluated between distinct disciplines, one stage at a time to tackle the high dimensional design variations. This project addresses these key design challenges: (1) concurrent optimization of multiple stages under many non-linear constraints; (2) multitude of evaluation of high-fidelity and expensive solvers and their gradients during optimization convergence in high-dimensional design; (3) multi-disciplinary design to maximize aerodynamic performance while guaranteeing structural integrity and additive manufacturability; (4) utilization of multiple fidelity of solvers with disparate parameterization and modeling assumptions. MULTI-LEADER achieved more than 5x speed up in detailed design of more energy-efficient compressors via these machine learning (ML) innovations: (i) rapid design surrogates by multi-source learning from diverse fidelities across multiple disciplines, (ii) physics-constrained data-augmented modeling for improved empiricism, (iii) generative manifold embedding for high dimensional concurrent design without gradient information; (iv) budget-constrained fidelity-adaptive sampling towards fewer design iterations.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long-term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C-cycle processes. Bayesian calibration was conducted using quality-controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass-shrub mixture, and grass-tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R 2 > 0.6, RMSE <390 g C m -2 ) relative to net ecosystem exchange of CO 2 (NEE) (R 2 > 0.4, RMSE <180 g C m -2 ). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R 2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long-term network-based monitoring of vegetation biomass, C fluxes, and SOC stocks.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

Recent deformation rates on Venus

Constraints on the recent geological evolution of Venus may be provided by quantitative estimates of the rates of the principal resurfacing processes, volcanism and tectonism. This paper focuses on the latter, using impact craters as strain indicators. The total postimpact tectonic strain lies in the range 0.5-6.5%, which defines a recent mean strain rate of 10(exp -18)-10(exp -17)/s when divided by the mean surface age. Interpretation of the cratering record as one of pure production requires a decline in resurfacing rates at about 500 Ma (catastrophic resurfacing model). If distributed tectonic resurfacing contributed strongly before that time, as suggested by the widespread occurrence of tessera as inliers, the mean global strain rate must have been at least approximately 10(exp -15)/s, which is also typical of terrestrial active margins. Numerical calculations of the response of the lithosphere to inferred mantle convective forces were performed to test the hypothesis that a decrease in surface strain rate by at least two orders of magnitude could be caused by a steady decline in heat flow over the last billion years. Parameterized convection models predict that the mean global thermal gradient decreases by only about 5 K/km over this time; even with the exponential dependence of viscosity upon temperature, the surface strain rate drops by little more than one order of magnitude. Strongly unsteady cooling and very low thermal gradients today are necessary to satisfy the catastrophic model. An alternative, uniformitarian resurfacing hypothesis holds that Venus is resurfaced in quasi-random 'patches' several hundred kilometers in size that occur in response to changing mantle convection patterns.

Grimm, Robert E.↗
DOE OSTI2025

Mesoscale modeling of restructuring in high burnup UO 2 fuel

Here, this work aims to simulate the restructuring behavior observed in different regions of high burnup fuel, providing a first-of-its-kind restructuring model for the dark zone and rim region of high-burnup UO 2 fuel. We employed a grand-potential-based phase-field model to concurrently evaluate subgrain formation and the growth of fission gas bubbles within the fuel. An energy-based subgrain formation criterion was introduced to simulate the restructuring process. The effects of different initial conditions and different modeling parameters were systematically studied to capture how each of these parameters influences the characteristics of the restructured fuel. Subgrain formation was observed to begin around existing fission gas bubbles and proceed toward triple junctions, grain boundaries, and grain interiors. Restructuring was demonstrated to be influenced by a combination of initial dislocation densities, burnup rate, subgrain formation rate, and temperature. Under a given subgrain formation rate, the rate of restructuring increases with rising fuel temperature. A restructuring bias was observed within the microstructure, due to the variation in defect accumulation when comparing different grains. Microstructures corresponding to the dark zone and rim region can be obtained by parameterizing the model with the appropriate defect production rate, as determined based on the burnup rate and temperature. Furthermore, bubble size and distribution do not significantly affect the rate of restructuring. The predicted microstructures are consistent with experimental observations of the restructured regions. Finally, we present a correlation demonstrating the evolution of the restructuring volume fraction as a function of local burnup.

UO2↗
DOE OSTI2024

Understanding spatial and temporal drivers of variation in tree hydraulic processes and their consequences for climate feedbacks (Final Technical Report)

This is the final technical report from the first phase of a project that changed institutions. The grant was titled “Understanding spatial and temporal drivers of variation in tree hydraulic processes and their consequences for climate feedbacks.” The overall objectives of this project were to (1) provide model‐compatible datasets of key plant hydraulic traits and status for model evaluation, parameterization and validation and (2) use these data to pinpoint ecosystem responses to a changing hydroclimate by addressing both long‐term climatic drying and episodic extreme droughts. We planned to address the objectives with three research activities to quantify plant responses to chronic water stress and episodic drought: (1) generate high frequency observations of soil and plant hydraulic data across different landscape positions at multiple sites, (2) quantify plant hydraulic trait plasticity in response to experimental soil moisture reduction in situ in two central hardwood forests, and (3) simulate the carbon consequences of incorporating plant hydrodynamics and plant acclimation to water stress in the DOE‐sponsored plant hydrodynamics model FATES‐HYDRO. As of the transfer of this project to another institution, we had made substantial progress on activities 1 and 2, and started activity 3.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Animal movement estimation and network-based epidemic modeling: Illustration for the swine industry in Iowa (US)

Animal movement plays a critical role in disease transmission between farms. However, in the United States, the lack of available animal shipment data, sometimes coupled with a lack of detailed information about farm demographics and characteristics, presents great challenges for epidemic modeling and prediction. In this study, we proposed a new method based on the maximum entropy to generate “synthetic” animal movement networks, considering available statistics about the premises operation type, operation size, and the distance between premises. We illustrated our method for the swine movement networks in Iowa and performed network analyses to gain insights into the swine industry. We then applied the generated networks to a network-based epidemic model to identify potential system vulnerabilities in terms of disease transmission. The model was parameterized for African Swine Fever (ASF) as the US swine industry is quite concerned about this disease. Results show that premises with a central role in the network are more vulnerable to disease outbreaks and play an important role in disease spread. Simulations with outbreaks starting from random farms reveal no significant large outbreaks, indicating the system’s relative robustness against arbitrary disease introductions. However, outbreaks originating from high out-degree farms can lead to large epidemic sizes. This underscores the importance for stakeholders and policymakers to continue improving animal movement records and traceability programs in the US and the value of making that data available to epidemiologists and modelers to better understand risk and inform strategies aimed to cost-effectively prevent and control disease transmission. Our approach could be easily adapted to estimate movement networks in other animal production systems and to inform disease spread models for various infectious diseases.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1990

Parameterization of eddy sensible heat transports in a zonally averaged dynamic model of the atmosphere

A Charney-Branscome based parameterization has been tested as a way of representing the eddy sensible heat transports missing in a zonally averaged dynamic model (ZADM) of the atmosphere. The ZADM used is a zonally averaged version of a general circulation model (GCM). The parameterized transports in the ZADM are gaged against the corresponding fluxes explicitly simulated in the GCM, using the same zonally averaged boundary conditions in both models. The Charney-Branscome approach neglects stationary eddies and transient barotropic disturbances and relies on a set of simplifying assumptions, including the linear appoximation, to describe growing transient baroclinic eddies. Nevertheless, fairly satisfactory results are obtained when the parameterization is performed interactively with the model. Compared with noninteractive tests, a very efficient restoring feedback effect between the modeled zonal-mean climate and the parameterized meridional eddy transport is identified.

Genthon, Christophe↗
NASA NTRS2015

RACORO Continental Boundary Layer Cloud Investigations: 3. Separation of Parameterization Biases in Single-Column Model CAM5 Simulations of Shallow Cumulus

Climatically important low-level clouds are commonly misrepresented in climate models. The FAst-physics System TEstbed and Research (FASTER) Project has constructed case studies from the Atmospheric Radiation Measurement Climate Research Facility's Southern Great Plain site during the RACORO aircraft campaign to facilitate research on model representation of boundary-layer clouds. This paper focuses on using the single-column Community Atmosphere Model version 5 (SCAM5) simulations of a multi-day continental shallow cumulus case to identify specific parameterization causes of low-cloud biases. Consistent model biases among the simulations driven by a set of alternative forcings suggest that uncertainty in the forcing plays only a relatively minor role. In-depth analysis reveals that the model's shallow cumulus convection scheme tends to significantly under-produce clouds during the times when shallow cumuli exist in the observations, while the deep convective and stratiform cloud schemes significantly over-produce low-level clouds throughout the day. The links between model biases and the underlying assumptions of the shallow cumulus scheme are further diagnosed with the aid of large-eddy simulations and aircraft measurements, and by suppressing the triggering of the deep convection scheme. It is found that the weak boundary layer turbulence simulated is directly responsible for the weak cumulus activity and the simulated boundary layer stratiform clouds. Increased vertical and temporal resolutions are shown to lead to stronger boundary layer turbulence and reduction of low-cloud biases.

parameterization↗
NASA NTRS1991

A model of onshore-offshore change in faunal diversity

Onshore-offshore patterns of faunal change occurred at many taxonomic scales during the Paleozoic Era, ranging from replacement of the Cambrian evolutionary fauna by the Paleozoic fauna to the environmental expansion of many orders and classes. A simple mathematical model is constructed to investigate such change. The environmental gradient across the marine shelf-slope is treated as a linear array of discrete habitats, each of which holds a set number of species, as observed in the fossil record. During any interval of time, some portion of the species in each habitat becomes extinct by background processes, with rates of extinction varying among both clades and habitats, as also observed in the record. After extinction, species are replaced from within the habitat and from immediately adjacent habitats, with proportions dependent on surviving species. This model leads to the prediction that extinction-resistant clades will always diversify at the expense of extinction-prone clades. But if extinction intensity is highest in nearshore habitats, extinction-resistant clades will expand preferentially in the onshore direction, build up diversity there, and then diversify outward toward the offshore. Thus, onshore-offshore patterns of diversification may be the expectation for faunal change quite independently of whether or not clades originate onshore. When the model is parameterized for Paleozoic trilobites and brachiopods, numerical solutions exhibit both a pattern of faunal change and a time span for diversification similar to that seen in the fossil record. They also generate structure similar to that seen in global diversification, including logistic patterns of growth, declining origination but constant extinction within clades through time, and declining overall extinction across clades through time.

NASA Discipline Number 52-40↗
DOE OSTI2025

Mesoscale Modeling for Restructuring and Fragmentation in High Burnup UO 2

This report summarizes the mesoscale modeling conducted in fiscal year 2025 under the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, focusing on the microstructural evolution and restructuring in high burnup UO 2 nuclear fuel and its impact on fuel fragmentation. We developed a pioneering phase-field model to simulate restructuring behavior across different regions of high burnup fuel, including the dark zone and rim region. A grand-potential-based phase-field model is employed to concurrently evaluate subgrain formation and the growth of fission gas bubbles within the fuel. An energy-based subgrain formation criterion was introduced to simulate the restructuring process. The effects of temperature and burnup rate were studied to capture how each of these parameters influences the characteristics of the restructured fuel. Subgrain formation was observed to initiate around existing fission gas bubbles and proceed toward triple junctions, grain boundaries, and grain interiors. Under a given subgrain formation rate, the rate of restructuring increases with rising fuel temperature. The restructuring occurs faster with higher burnup rate. A restructuring bias was observed within the microstructure, due to the variation in defect accumulation when comparing different grains. Microstructures corresponding to the dark zone and rim region can be obtained by parameterizing the model with the appropriate defect production rate, as determined based on the burnup rate and temperature. The predicted microstructures are consistent with experimental observations of the restructured regions. Based on the mesoscale simulations, a mechanistic model for restructuring and grain size evolution was implemented in BISON. Thus, this work provides a first-of-its-kind restructuring model for different regions of high-burnup fuel to BISON. It was shown that the model predicts appropriate grain size evolution along fuel radius as those observed in experiments. This model enables BISON to predict the effect of restructuring on the fission gas release. Finally, the phase-field fracture simulation with dark zone specific microstructures were presented to provide the fragmentation criteria for the dark zone. This work introduces a first-of-its-kind restructuring model for high burnup fuel, significantly enhancing BISON's predictive capabilities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Phase-field modeling of radiation-induced composition redistribution: An application to additively manufactured austenitic Fe–Cr–Ni

Multicomponent alloys undergoing irradiation damage develop radiation-induced composition redistribution at point defect sinks such as grain boundaries (GBs) and dislocations. Such redistribution results in undesired changes to their mechanical behavior and corrosion resistance. Additively manufactured alloys proposed for future nuclear applications are expected to demonstrate a distinct response to irradiation owing to their unique microstructure with as-solidified dislocation density and chemical microsegregation. To capture the composition redistribution in such systems, we develop a mesoscale model with coupled evolution of atomic and point defect components in the presence of dislocation density, dislocation heterogeneity, and thermodynamic interactions at the GB. The model is parameterized for an FCC Fe–Cr–Ni alloy as a representative system for austenitic stainless steels, and simulations are performed in 1D and 2D as a function of irradiation temperature, dose, dislocation density, and grain size. Radiation-induced segregation (RIS) characterized by Cr depletion and Ni enrichment is predicted at both the GB and the dislocation cell wall, with RIS being lower in magnitude but wider at the cell wall. Strongly biased absorption of self-interstitials by dislocations is found to suppress Ni enrichment but slightly enhance Cr depletion under certain conditions. Thermodynamic segregation at the GB is predicted to be narrower and opposite in sign to RIS for both Cr and Ni. Importantly, non-monotonic segregation is found to occur when both thermodynamic and RIS mechanisms are considered, providing a novel physical interpretation of experimental observations. The model is expected to serve as a key tool in accelerated qualification of irradiated materials.

additively manufactured microstructure↗