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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Variability in terrestrial characteristics and erosion rates on the Alaskan Beaufort Sea coast

Abstract Arctic coastal environments are eroding and rapidly changing. A lack of pan-Arctic observations limits our ability to understand controls on coastal erosion rates across the entire Arctic region. Here, we capitalize on an abundance of geospatial and remotely sensed data, in addition to model output, from the North Slope of Alaska to identify relationships between historical erosion rates and landscape characteristics to guide future modeling and observational efforts across the Arctic. Using existing datasets from the Alaska Beaufort Sea coast and a hierarchical clustering algorithm, we developed a set of 16 coastal typologies that captures the defining characteristics of environments susceptible to coastal erosion. Relationships between landscape characteristics and historical erosion rates show that no single variable alone is a good predictor of erosion rates. Variability in erosion rate decreases with increasing coastal elevation, but erosion rate magnitudes are highest for intermediate elevations. Areas along the Alaskan Beaufort Sea coast (ABSC) protected by barrier islands showed a three times lower erosion rate on average, suggesting that barrier islands are critical to maintaining mainland shore position. Finally, typologies with the highest erosion rates are not broadly representative of the ABSC and are generally associated with low elevation, north- to northeast-facing shorelines, a peaty pebbly silty lithology, and glaciomarine deposits with high ice content. All else being equal, warmer permafrost is also associated with higher erosion rates, suggesting that warming permafrost temperatures may contribute to higher future erosion rates on permafrost coasts. The suite of typologies can be used to guide future modeling and observational efforts by quantifying the distribution of coastlines with specific landscape characteristics and erosion rates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Ice processes in Antarctica: identification via multi-wavelength active and passive measurements and model evaluation (Final Report)

This project is based on new multi-frequency radar observations collected during the AWARE field campaign in 2016 at McMurdo Station by the ARM Mobile facility. Three scientific goals have been achieved. We have developed ice and mixed-phase cloud microphysics retrievals from multi-wavelength radar observations and ancillary observations, generally applicable at ARM sites. These technique generally better characterize the size of the ice crystals, their equivalent water content and the degree of riming better than using single wavelength techniques. We have characterized ice microphysical properties and ice processes via multi-frequency signatures. We have identified a unique mode in the McMurdo dataset appearing at quite cold temperature that we have attributed to riming processes. Triple frequency radar measurements unfortunately were available only for a short period but our work lays the foundation for building climatological and testing their replicability in models. Simultaneously case studies from the AWARE campaign have been exploited to evaluate and improve the NASA GISS ModelE3 climate model, including selection of case studies for combined goals of (i) running side-by-side using large-eddy simulation (LES) and ModelE3 in single-column model (SCM) mode and (ii) fingerprinting cloud processes in multi-wavelength radar observations. The different components of this work have contributed to 15 published papers with two currently under review.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Modeling Intercalation Chemistry with Multiredox Reactions by Sparse Lattice Models in Disordered Rocksalt Cathodes

Modern battery materials can contain many elements with substantial site disorder, and their configurational state has been shown to be critical for their performance. The intercalation voltage profile is a critical parameter to evaluate the performance of energy storage. The application of commonly used cluster expansion techniques to model the intercalation thermodynamics of such systems ab initio is challenged by the combinatorial increase in configurational degrees of freedom as the number of species grows. Such challenges necessitate the efficient generation of lattice models without overfitting and proper sampling of the configurational space under the requirement of charge balance in ionic systems. In this work, we introduce a combined approach that addresses these challenges by (1) constructing a robust cluster expansion Hamiltonian using the sparse regression technique, including -norm regularization and structural hierarchy; and (2) implementing semigrand-canonical Monte Carlo to sample charge-balanced ionic configurations using the table-exchange method and an ensemble average approach. These techniques are applied to a disordered rocksalt oxyfluoride (LMNOF) that is part of a family of promising earth-abundant cathode materials. The simulated voltage profile is found to be in good agreement with experimental data and particularly provides a clear demonstration of the and oxygen contributions to the redox potential as a function of content.

25 ENERGY STORAGE↗
DOE OSTI2023

Origin of Rapid Delithiation In Secondary Particles Of LiNi 0.8 Co 0.15 Al 0.05 O 2 and LiNi y Mn z Co 1− y − z O 2 Cathodes

Abstract Most research on the electrochemical dynamics in materials for high‐energy Li‐ion batteries has focused on the global behavior of the electrode. This approach is susceptible to misleading analyses resulting from idiosyncratic kinetic conditions, such as surface impurities inducing an apparent two‐phase transformation within LiNi 0.8 Co 0.15 Al 0.05 O 2 . Here, nano‐focused X‐ray probes are used to measure delithiation operando at the scale of secondary particle agglomerates in layered cathode materials during charge. After an initial latent phase, individual secondary particles undergo rapid, stochastic, and largely uniform delithiation, which is in contrast with the gradual increase in cell potential. This behavior reproduces across several layered oxides. Operando X‐ray microdiffraction (‐XRD) leverages the relationship between Li content and lattice parameter to further reveal that rate acceleration occurs between Li‐site fraction ( x Li ) ≈0.9 and ≈0.5 for LiNi 0.8 Co 0.15 Al 0.05 O 2 . Physics‐based modeling shows that, to reproduce the experimental results, the exchange current density ( i 0 ) must depend on x Li , and that i 0 should increase rapidly over three orders of magnitude at the transition point. The specifics and implications of this jump in i 0 are crucial to understanding the charge‐storage reaction of Li‐ion battery cathodes.

25 ENERGY STORAGE↗
DOE OSTI2024

Ruthenium supported on silicate and aluminosilicate mesoporous materials applied to selective sugar hydrogenation: Xylose to xylitol

A series of ruthenium-based catalysts supported on a set of silicate and aluminosilicate mesoporous molecular sieves was synthesized and tested in xylose hydrogenation. The materials were characterized in terms of morphology, textural properties, acidity, as well as ruthenium loading, dispersion, and oxidation state. In general, the aluminosilicates-based catalysts displayed a higher activity compared to their respective silicate supports, which can be ascribed to a higher Ru content and dispersion, enhanced by a higher acidity. The most active synthesized catalyst (Ru/Al-MCM-4) displayed an improved performance compared to a commercial Ru/C catalyst due to a better xylitol selectivity. Two modelling approaches were implemented to describe the kinetic rate. The first model was based on the hypothesis that xylose molecules and hydrogen are adsorbed in different active sites on the catalyst surface, while the second model supposes the formation of an intermediate on the catalyst surface that reacts to form xylitol. Both models gave a very good description of the experimental data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A review on the state of thermal hydraulics research on air ingress scenarios in High-Temperature Gas-cooled Reactors following a D-LOFC

With the expectation of near-immediate carbon neutrality, widespread implementation of proven High-Temperature Gas-cooled Reactors (HTGRs) embodies a viable solution pathway given their inherent, passive safety features and high thermal efficiency. This study provides an overview of the current state of research involving the thermal hydraulics associated with air ingress from a depressurized loss of forced cooling (D-LOFC) in HTGRs. Accurately characterizing and predicting the physical phenomena underlying air ingress is of paramount concern, as the integrity of the fuel and core graphite support structures are threatened by the presence of oxygen. Broadly speaking, the air ingress scenario can be delineated into three main stages: (1) Depressurization, (2) Density-Driven Flow, and (3) Natural Convection. In tandem with the underlying fundamental theory, this review collates and synthesizes the existing body of contemporary research concerning the air ingress scenario following a D-LOFC. As evinced by this review, our current understanding and predictive abilities have benefited from extensive research, predominantly concentrated on the rate of air ingestion into the core. Here, additional research is necessary to holistically capture the phenomenology of an air ingress scenario following a D-LOFC by considering an additional variable: the oxygen content of the ingressing air. The latter variable requires investigation into the complex interactions of the fully integrated system. Additionally, while numerical tools are evolving domestically through the Nuclear Energy Advanced Modeling and Simulation program, a sufficiently validated code remains absent.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Search for GeV-scale dark matter annihilation in the Sun with IceCube DeepCore

The Sun provides an excellent target for studying spin-dependent dark matter-proton scattering due to its high matter density and abundant hydrogen content. Dark matter particles from the Galactic halo can elastically interact with Solar nuclei, resulting in their capture and thermalization in the Sun. The captured dark matter can annihilate into Standard Model particles including an observable flux of neutrinos. Here, we present the results of a search for low-energy (<500 GeV) neutrinos correlated with the direction of the Sun using 7 years of IceCube data. This work utilizes, for the first time, new optimized cuts to extend IceCube's sensitivity to dark matter mass down to 5 GeV. We find no significant detection of neutrinos from the Sun. Our observations exclude capture by spin-dependent dark matter-proton scattering with cross section down to a few times 10 -41 cm 2 , assuming there is equilibrium with annihilation into neutrinos/antineutrinos for dark matter masses between 5 GeV and 100 GeV. These are the strongest constraints at GeV energies for dark matter annihilation directly to neutrinos.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Decayheatml

This code is designed to predict and analyze the decay heat generated in molten salt reactors (MSRs) using a hybrid approach that combines machine learning and segmented polynomial fitting. The accurate prediction of decay heat is essential for reactor safety and the optimization of spent fuel storage. The code operates through several key components: 1) Data Architecture: It incorporates a modular data architecture that handles various MSR-specific operational parameters such as power density, humidity content, and air ingress. These parameters are sampled using Sobol sequences to ensure comprehensive coverage of operational uncertainties. 2) Machine Learning Framework: The code employs a diverse set of machine learning models, including polynomial regression, decision trees, random forests, gradient boosting, support vector regression, k-nearest neighbors, multi-layer perceptrons, and symbolic regression. These models are trained to predict decay heat over a wide temporal range, from immediate shutdown up to 10,000 years. 3) Region-Optimized Training: The temporal domain is divided into multiple regions, each modeled separately to capture distinct decay heat characteristics across different time scales. This approach significantly improves the accuracy and interpretability of predictions. 4) Segmented Polynomial Interpretation (SPI): The SPI method translates machine learning predictions into piecewise polynomial equations. These equations are physically interpretable and can be directly integrated into existing engineering workflows and safety analyses. 5) Front-End Interfaces: The code includes both a Jupyter notebook interface for research development and a Streamlit web application for operational deployment. These interfaces allow users to interactively explore decay heat predictions, adjust operational parameters, and visualize results in real-time. 6) Applications: The framework supports various applications, including safety system validation and spent fuel container optimization. It enables real-time evaluation of worst-case decay heat scenarios, informing the design of passive safety systems and optimizing container designs for long-term storage. Overall, this code provides a robust, accurate, and user-friendly tool for predicting decay heat in MSRs, enhancing reactor safety, and optimizing spent fuel management.

Retamales, Mauricio Eduardo Tano [Idaho National L↗
DOE OSTI2025

Carbon Dioxide Removal Measurement, Reporting, and Verification Simulation toolkit (CDR MRVSim) v0.1

CDR MRVSim is a statistical software toolkit for techno-economic analysis of measurement, reporting, and verification of multiple carbon dioxide removal technologies. The current version applies Monte Carlo simulation to existing datasets estimate the cost and uncertainty of measuring the soil organic carbon (SOC) content of agricultural land, accounting for multiple sources of uncertainty, using data from field measurements. We are planning to incorporate enhanced rock weathering and other technologies into the software. The current model leverages existing data to characterize underlying variability in soil organic carbon and related parameters, including bulk density, in an agricultural field attempting to increase its SOC. We then simulate baseline and post-intervention "measurement campaigns" in which some number of SOC and bulk density measurements are conducted using a selected technology. This enables estimates of the cost and accuracy of measuring changes in SOC in the simulated field. The primary advantages over similar software are: 1) ability to quantify tradeoffs between cost and uncertainty in MRV across a range of possible MRV approaches 2) focus on guiding development of novel sensors by determining desirable sets of characteristics

Sherwin, Evan [Lawrence Berkeley National Laborato↗
DOE OSTI2021

A simplified integrated framework for predicting the economic impacts of feedstock variations in a catalytic fast pyrolysis conversion process

Feedstock attributes of lignocellulosic biomass, such as particle size, compositional makeup, and moisture content, can vary substantially even within pre-processed materials and have a significant effect on conversion in fast pyrolysis-based processes. However, the economic impacts of these attributes are not well understood. To address this, biomass deconstruction phenomena captured with a versatile particle-scale simulation were linked to techno-economic impacts via reduced-order models. Parametric analysis of the particle-scale model, which was validated using literature data, was used in combination with multiple linear regression models to develop correlations between feedstock attributes and yields of pyrolysis oil, gas, and char. Yields were then correlated with the minimum fuel selling price (MFSP) using a techno-economic model, bridging the gap between physics-based biomass conversion simulations and predictions of MFSP for a catalytic fast-pyrolysis process. Empirical correlations derived from the literature regarding the impact of mineral matter (ash) on oil yield were also considered. The model correlations deployed in the integrated framework capture the impacts of variation in feedstock attributes on the MFSP. Variations in ash were shown to have the biggest impact, varying MFSP by -13%/+22% due to catalytic effects and lower relative amounts of convertible lignocellulosic material. It was also found that, if ash can be controlled to low levels, the increased extractives in forest residues can help compensate for some yield losses associated with increased ash. As a result, other inputs considered (particle size, moisture content, and reactor temperature) had relatively negligible effects on process economics within the ranges analyzed considering particle-scale effects alone.

BIOMASS FUELS↗
DOE OSTI2022

Controlling pollutant emissions in a high-pressure combustor with fuel-diluent blending

This paper investigates the formation of nitric oxide (NO) and carbon monoxide (CO) emissions as a function of nitrogen (N 2 ) or carbon dioxide (CO 2 ) diluent content, added to a premixed reacting jet in crossflow. Here, reaction characteristics of a rich methane-air jet injected into a lean vitiated crossflow were analyzed at an elevated pressure of 5 atm. The jet was pre-heated and enriched with 0%, 15%, 30%, and 50% mass fraction to quantify the effect on pollutant emissions. Simulated results of the full chemistry Star-CCM+CFD model were verified with data taken in the experimental high-pressure combustion facility, which provides pressure, temperature and velocity profiles, as well as line-of-sight chemiluminescence and exit emission measurements. The analysis revealed the significant influence of the diluent to delay axial combustion; this mixing delay increased the axial flame lift-off and controlled any thermal ignition limitations. Increasing the diluent content increased the timescale for the flame to stabilize, which allowed for greater entrainment of crossflow oxidizer into the axial jet stream, and led to decreased pollutant emissions. Hence, crossflow entrainment is a critical driving force at high diluent content. Local nitric oxide (NO) emission formation in the axial stage was predicted numerically, showing the correlation between diluent addition, axial heat release, and the formation of nitric oxide pollution. The high diluent levels leaned out the jet mixture and delayed axial combustion, while reducing prompt and thermal NOx by mitigating flame hotspots and minimizing the timescale that the products remain at high temperatures.

03 NATURAL GAS↗
DOE OSTI2024

Increasing the Reproducibility and Replicability of Supervised AI/ML in the Earth Systems Science by Leveraging Social Science Methods

Artificial intelligence (AI) and machine learning (ML) pose a challenge for achieving science that is both reproducible and replicable. The challenge is compounded in supervised models that depend on manually labeled training data, as they introduce additional decision-making and processes that require thorough documentation and reporting. We address these limitations by providing an approach to hand labeling training data for supervised ML that integrates quantitative content analysis (QCA)—a method from social science research. The QCA approach provides a rigorous and well-documented hand labeling procedure to improve the replicability and reproducibility of supervised ML applications in Earth systems science (ESS), as well as the ability to evaluate them. Specifically, the approach requires (a) the articulation and documentation of the exact decision-making process used for assigning hand labels in a “codebook” and (b) an empirical evaluation of the reliability” of the hand labelers. In this paper, we outline the contributions of QCA to the field, along with an overview of the general approach. We then provide a case study to further demonstrate how this framework has and can be applied when developing supervised ML models for applications in ESS. With this approach, we provide an actionable path forward for addressing ethical considerations and goals outlined by recent AGU work on ML ethics in ESS.

58 GEOSCIENCES↗
DOE OSTI2024

Enhanced Resistance Pines for Improved Renewable Biofuel and Chemical Production (Technical Report)

We completed phenotyping constitutive and inducible oleoresin flow across two seasons, constitutive resin canal number and density and wood terpene content in our ADEPT2 and CCLONES populations. We completed genetic association between 19 oleoresin phenotypes and a total of 523,192 SNP markers from ADEPT2 and 13,883 SNP markers in CCLONES using four mixed linear models. A total of 293 significant SNPs (FDR = 0.20) were identified. We used the MENTOR tool to mine mechanistic connections from a multiplex network constructed from poplar multi-omic data to construct a conceptual model for a subset of these significant SNPs. Our model contains 6 transcriptional regulators in addition to 3 monoterpene synthases. To generate more lines of evidence for these significant SNPs, we completed a time course RNAseq experiment after inducing vascular zone cells to differentiate into new resin canals with a methyl jasmonate treatment, a single nuclei RNAseq that identified differentiating resin canal epithelial cells and are completing analysis for a QTL study in a hybrid pine population. The time course identified 4634 significantly down and 1890 significantly up regulated transcripts after treatment with methyl jasmonate, an inducer of new resin canal formation in the vascular cambial meristem. To analyze this large set of differentially regulated genes, we created a predictive expression network and analyzed it with random walk restart using 6 seed genes coding for transcription factors regulating xylem differentiation in poplar. Of the top ranked 200 transcripts, 119 transcripts were significant differentially expressed supporting these transcripts as potential candidates regulating resin canal formation. Analysis of single nuclei sequencing of shoot tips that contain differentiating resin canals, identified 10 clusters. One cluster was highly enriched in transcripts coding for 9 of the enzymes in the MEP pathway 3 prenyl synthetases, and 3 monoterpene synthases strongly suggesting that this cluster represents resin canal epithelial cells. We are mining the additional transcripts to create a trajectory analysis. In summary, we have identified > 10 novel genes that are strongly supported candidates for further analysis in breeding lines and for genetic engineering over- and under- expressing lines to increase wood terpene content to improve resistance to insect and fungal pathogens while simultaneously increasing terpene supplies for renewable chemicals and biofuels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Impact of the numerical solution approach of a plant hydrodynamic model (v0.1) on vegetation dynamics

Abstract. Numerous plant hydrodynamic models have started to be implemented in vegetation dynamics models, reflecting the central role of plant hydraulic traits in driving water, energy, and carbon cycles, as well as plant adaptation to climate change. Different numerical approximations of the governing equations of the hydrodynamic models have been documented, but the numerical accuracy of these models and its subsequent effects on the simulated vegetation function and dynamics have rarely been evaluated. Using different numerical solution methods (including implicit and explicit approaches) and vertical discrete grid resolutions, we evaluated the numerical performance of a plant hydrodynamic module in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES-HYDRO version 0.1) based on single-point and global simulations. Our simulation results showed that when near-surface vertical grid spacing is coarsened (grid size >10 cm), the model significantly overestimates aboveground biomass (AGB) in most of the temperate forest locations and underestimates AGB in the boreal forest locations, as compared to a simulation with finer vertical grid spacing. Grid coarsening has a small effect on AGB in the tropical zones of Asia and South America. In particular, coarse surface grid resolution should not be used when there are large and prolonged water content differences among soil layers at depths due to long dry-season duration and/or well-drained soil or when soil evaporation is a dominant fraction of evapotranspiration. Similarly, coarse surface grid resolution should not be used when there is lithologic discontinuity along the soil depth. This information is useful for uncertainty quantification, sensitivity analysis, or the training of surrogate models to design the simulations when computational cost limits the use of ensemble simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Optimization of Magnesium-Doped Lithium Metal Anode for High Performance Lithium Metal Batteries through Modeling and Experiment

Lithium (Li)-magnesium (Mg) alloy with limited Mg amount, which can also be called Mg-doped Li (Li-Mg), has been considered as a potential alternative anode for high energy density rechargeable Li metal batteries. However, the optimum doping-content of Mg in Li-Mg anode and the mechanism of the improved performance are not well understood. In this study, density functional theory (DFT) calculations are used to investigate the effect of Mg amount in Li-Mg anode. The Li-Mg with about 5 wt. % Mg (abbreviated as Li-Mg5) has the lowest absorption energy of Li, thus all the surface area can be “controlled” by Mg atoms, leading to the smooth and continuous deposition of Li on the surface around the Mg center. A localized high concentration electrolyte enables Li-Mg5 to exhibit the best cycling stability in Li metal batteries with high-loading cathode and lean electrolyte under 4.4 V high-voltage, which is approaching the demand of practical application. This electrolyte also helps generate an inorganic-rich solid electrolyte interphase, which leads to smooth, compact and less corrosion layer on the Li-Mg5 surface. Both theoretical simulations and experimental results prove that Li-Mg5 has optimum Mg content and gives best battery cycling performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Observations Indicate That Clouds Amplify Mechanisms of Southern Ocean Heat Uptake

Abstract The Southern Ocean has absorbed most of the excess heat associated with anthropogenic greenhouse gas emissions. Since Southern Ocean observations are sparse in certain regions and seasons, much of our knowledge of ocean heat uptake is based on climate model simulations. However, climate models still inadequately represent some properties of Southern Ocean clouds, and they have not identified the mechanisms by which clouds may affect Southern Ocean heat uptake (SOHU). Here, we use the ERA5 and JRA‐55 reanalyses to assess the influence of clouds and other atmospheric processes on SOHU from 1979 to 2020. We find that years with the highest SOHU between 45° and 65°S are dominated by ocean heat uptake anomalies during winter and spring, but not during summer or fall. Winter and spring cloud cover are up to 7% higher when SOHU is up to 5.5 W/m 2 higher than the climatological seasonal mean, with the largest increases in the South Pacific Ocean. Clouds also contain more liquid water. These changes in cloud properties increase downwelling longwave radiation, amplifying ocean heat uptake. Cloud changes are also concomitant with a more stable lower atmosphere, which suppresses turbulent heat fluxes out of the surface. Overall, we find that SOHU is likely not mediated by enhanced surface shortwave absorption over the observational time period. A better understanding of how atmospheric processes impact ocean heat uptake may help improve our understanding of ocean heat uptake mechanisms in the current generation of climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Developing rapid electrochemical relithiation protocols for scalable relithiation of lithium-ion battery cathode materials

The recent and ongoing boom in electric vehicle sales has caused the circularity of the supply chain for electric vehicle battery materials to come under a great deal of scrutiny. Innovative recycling processes, or direct recycling, that offer the possibility of reducing the cost of recycling are one possible solution to regaining resources from end-of-life (EoL) electric vehicle batteries. Electrochemically shuttling lithium back into the cathode, or electrochemical relithiation, is a possible technique for restoring lithium content to NMC materials (EoL) in a direct recycling process. This study provides essential understanding towards developing an electrochemical relithiation protocol that will restore lithium loss in intercalation cathode materials that reach EoL by loss of lithium inventory (LLI) as opposed to other degradation mechanisms like loss of active material (LAM), cation mixing or phase transition. Electrochemically aged NMC cathode materials have been prepared and characterized to establish the extent of EoL material structural degradation and lithium loss. A model-informed experimental process is used to identify the optimal electrochemical relithiation protocol to minimize the time taken to relithiate EoL materials and maximize the amount of lithium restored. Protocols were evaluated based on their ability to enable rapid lithium intercalation, maintain structural uniformity in the EoL material and fully restore lithium content. An optimal protocol was identified at elevated temperatures utilizing a novel scanning voltage step.

25 ENERGY STORAGE↗
DOE OSTI2025

Interpretation of Ion Irradiation and Neutron Irradiation Damage in Additively Manufactured 316 Stainless Steel using Multiscale Modeling

The accelerated adoption of nuclear energy necessitates advanced manufacturing technologies, such as additive manufacturing, to meet heightened supply chain requirements and support innovative reactor technologies. Due to the unique microstructural characteristics of additively manufactured materials under distinct solidification conditions, comprehensive evaluation of their performance in reactor environments is essential. The Advanced Materials and Manufacturing Technologies program under the Department of Energy's Office of Nuclear Energy focuses on understanding the irradiation performance and damage evolution of laser powder bed fusion 316 stainless steel, with an emphasis on integrating ion and neutron irradiation data to accelerate the development and qualification of materials for advanced nuclear reactor applications. While ion irradiation is a cost- and time-effective method, modeling and simulation are required to interpret the data for the broader range of irradiation conditions encountered in advanced reactors. In fiscal year 2025, integrated multiscale modeling and simulations were conducted to assess irradiation damage in additively manufactured 316 stainless steel. Key outcomes include predictions of chromium enrichment at grain boundaries, nickel enrichment at dislocation cell walls and void surfaces, and heterogeneous void evolution under ion and neutron irradiation conditions. Cluster dynamics simulations revealed the coarsening of voids at high irradiation temperatures and the suppression of void growth by high network dislocation density, while also demonstrating significant growth and coarsening of voids and self-interstitial atom loops at low dose rates. Machine learning-accelerated atomistic simulations highlighted the impact of the local environment and chromium concentration on vacancy diffusivity, providing key insights on the influence of composition on void swelling and radiation-induced segregation. Additionally, molecular dynamics simulations demonstrated the presence of defect production bias and a significant effect of carbon content on defect cluster behavior. These combined efforts aim to predict the performance of additively manufactured materials under various reactor conditions, supporting their qualification for nuclear reactor applications by interpreting ion irradiation data. This report underscores the potential of integrated multiscale modeling to analyze ion irradiation data in the effort to accelerate the qualification of additively manufactured materials for nuclear reactor components.

316 stainless steel↗