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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Dominant balance-based adaptive mesh refinement for incompressible fluid flows

This work introduces a novel adaptive mesh refinement (AMR) method that utilizes dominant balance analysis (DBA) for efficient and accurate grid adaptation in computational fluid dynamics (CFD) simulations. The proposed method leverages a Gaussian mixture model (GMM) to classify grid cells into active and passive regions based on the dominant physical interactions within the equation space. By modeling truncation error probabilistically from discretized terms, the method identifies regions of high interaction where numerical accuracy is most sensitive to resolution. Unlike traditional AMR strategies, this approach does not rely on heuristic-based sensors or user-defined thresholds, providing a fully automated and problem-independent framework for AMR. Applied to the incompressible Navier-Stokes equations for steady and unsteady flow past a cylinder, the DBA-based AMR method achieves comparable accuracy to high-resolution grids while reducing computational costs by up to 70 %. The validation highlights the method’s effectiveness in capturing complex flow features while minimizing grid cells, directing computational resources toward regions with the most critical dynamics. This modular and scalable strategy is adaptable to a wide range of applications, presenting a promising tool for efficient high-fidelity simulations in CFD and other multiphysics domains.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

High resolution numerical simulations of methane pool fires using adaptive mesh refinement

The ability to accurately predict the structure and dynamics of pool fires using computational simulations is of great interest in a wide variety of applications, including accidental and wildland fires. However, the presence of physical processes spanning a broad range of spatial and temporal scales poses a significant challenge for simulations of such fires, particularly at conditions near the transition between laminar and turbulent flow. Here, in this study, we examine the transition to turbulence in methane pool fires using high-resolution simulations with multi-step finite rate chemistry, where adaptive mesh refinement (AMR) is used to directly resolve small-scale flow phenomena. We perform three simulations of methane pool fires, each with increasing diameter, corresponding to increasing inlet Reynolds and Richardson numbers. As the diameter increases, the flow transitions from organized vortex roll-up via the puffing instability to much more chaotic mixing associated with finger formation along the shear layer and core collapse near the inlet. These effects combine to create additional mixing close to the inlet, thereby enhancing fuel consumption and causing more rapid acceleration of the fluid above the pool. We also make comparisons between the transition to turbulence and core collapse in the present pool fires and in inert helium plumes, which are often used as surrogates for the study of buoyant reacting flows.

42 ENGINEERING↗
DOE OSTI2025

E3SM‐Arctic: Regionally Refined Coupled Model for Advanced Understanding of Arctic Systems Interactions

Earth system models are essential tools for climate projections, but coarse resolutions limit regional accuracy, especially in the Arctic. Regionally refined meshes (RRMs) enhance resolution in key areas while maintaining computational efficiency. This paper provides an overview of the United States (U.S.) Department of Energy's (DOE's) Energy Exascale Earth System Model version 2.1 with an Arctic RRM, hereafter referred to as E3SMv2.1-Arctic, for the atmosphere (25 km), land (25 km), and ocean/ice (10 km) components. We evaluate the atmospheric component and its interactions with land, ocean, and cryosphere by comparing the RRM (E3SM2.1-Arctic) historical simulations (1950–2014) with the uniform low-resolution (LR) counterpart, reanalysis products, and observational data sets. The RRM generally reduces biases in the LR model, improving simulations of Arctic large-scale mean fields, such as precipitation, atmospheric circulation, clouds, atmospheric river frequency, and sea ice thickness. However, it introduces a seasonally dependent surface air temperature bias, reducing the LR cold bias in summer but enhancing the LR warm bias in winter, which contributes to the underestimated winter sea ice area and volume. Radiative feedback analysis shows similar climate feedback strengths in both model configurations, with the RRM exhibiting a more positive surface albedo feedback and contributing to a stronger surface warming than LR. These findings underscore the importance of high-resolution modeling for advancing our understanding of Arctic climate changes and their broader global impacts, although some persistent biases appear to be independent of model resolution at 10–100 km scales.

Energy Exascale Earth System Model (E3SM)↗
DOE OSTI2025

Recovery and Refining of Rare Earth Elements from Lignite Mine Wastes

The University of North Dakota (UND), in collaboration with a comprehensive team of technical, business and host-site partners, built on prior technology development to complete a front-end engineering and design (FEED) and business planning study to recover and refine rare earth elements (REE) and critical minerals (CM) from North Dakota (ND) lignite mine wastes. The end of project goal was to have an investment quality project and a committed team ready to commercialize the proposed technologies in a future construction and operations phase.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

Traveling Molten Zone Refining Process Development for Innovative Fuel Cycle Solutions

This ARPA-E ONWARDS project successfully developed and demonstrated an innovative immiscibility zone refining process for separating actinides from active fission products in metallic nuclear fuel applications. The technology addresses a critical challenge in advanced nuclear fuel cycles by providing a high-throughput, low-waste alternative to conventional electrorefining.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Refining Microstructures in Additively Manufactured Al/Cu Gradients Through TiB 2 Inclusions

Here, the additive manufacture of compositionally graded Al/Cu parts by laser engineered net shaping (LENS) is demonstrated. The use of a blue light build laser enabled deposition on a Cu substrate. The thermal gradient and rapid solidification inherent to selective laser melting enabled mass transport of Cu up to 4 mm from a Cu substrate through a pure Al deposition, providing a means of producing gradients with finer step sizes than the printed layer thicknesses. Divorcing gradient continuity from layer or particle size makes LENS a potentially enabling technology for the manufacture of graded density impactors for ramp compression experiments. Printing graded structures with pure Al, however, was prevented by the growth of Al 2 Cu 3 dendrites and acicular grains amid a matrix of Al 2 Cu. A combination of adding TiB 2 grain refining powder and actively varying print layer composition suppressed the dendritic growth mode and produced an equiaxed microstructure in a compositionally graded part. Material phase was characterized for crystal structure and nanoindentation hardness to enable a discussion of phase evolution in the rapidly solidifying melt pool of a LENS print.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Refining seasonal performance metrics for room air-conditioning in emerging markets: Integrating building simulations with real-world equipment performance data

Buildings significantly impact worldwide energy consumption, emphasizing the need to reduce the cooling energy demand, especially in warm climates. Minimum energy performance standards (MEPS) and seasonal performance metrics such as the Cooling Seasonal Performance Factor (CSPF) are crucial for improving room air conditioning (RAC) efficiency. However, challenges remain, particularly in emerging markets like Brazil, where seasonal performance metrics have recently been introduced. This study assesses the factors influencing country-level seasonal efficiency metrics and proposes a framework to refine these calculations by considering local climates and expected RAC usage in real-world households via building simulations. Key considerations include outdoor air temperature binning for different climates, RAC usage patterns (i.e., daytime and nighttime usages), envelope thermal performance of households, and urban heat island (UHI) effects. The results reveal that CSPF values can vary significantly based on climate conditions, with observed CSPF ranging from 4.10 to 11.59 Wh/Wh across 577 Brazilian climates. The inclusion of UHI effects led to a reduction in CSPF values by up to 29% during nighttime operations in hot urban areas. Additionally, building envelope efficiency showed contrasting impacts on RAC performance, with CSPFs reaching up to 15.35 Wh/Wh under specific optimized conditions. These findings highlight the need for transparent policymaking in RAC performance databases, facilitating the application of approaches like those proposed in this study and supporting diverse stakeholders in decision-making.

Bavaresco, Mateus↗
DOE OSTI2025

Microstructure refinement of hot deformed Dy-free NdFeB magnets through a novel cold sintering approach by pressure-assisted spark plasma sintering

Anisotropic fine-grained Dy-free NdFeB high-performance magnets were produced using a novel cold sintering approach by pressure-assisted spark plasma sintering (SPS). The NdFeB nanocrystalline ribbon powders were subjected to cold sintering below 450 °C using pressure-assisted SPS, followed by hot deformation at 710 °C and 810 °C via SPS. Results indicate that cold sintering below 450 °C effectively restricts grain growth in the NdFeB magnets, yielding sub-100 nm scale grains across both fine-grained regions and most coarse-grained regions. The refined grain structure achieved through cold sintering allows for a lower hot deformation temperature as compared with the magnet by conventional sintering, as finer grains allow easier deformation and grain sliding/rotation. This process produces a highly fine-grained and anisotropic microstructure to achieve high magnetic performance (i.e., high intrinsic coercivity, remanence, and maximum energy product) in the magnets. The cold sintering (at 350 °C) and lower hot deformation temperatures required (e.g., 710 °C) help control grain coarsening and limit grain size that is commensurate with the size of a single magnetic domain of the Nd 2 Fe 14 B magnetic matrix phase, resulting in high intrinsic coercivity (H ci =11.4 kOe). Furthermore, the reduced hot deformation temperature mitigates the formation of coarse grain bands, leading to improved magnetic properties, specifically an increased remanence (B r = 14.3 kGs) and a high maximum energy product ((BH) max = 48 MGOe). In conclusion, cold sintered hot deformed NdFeB magnet also shows good thermal stability with reversible temperature coefficient α(B r )= -0.097 %/K and α(H ci )= -0.616 %/K at elevated temperatures up to 400 K.

Cold Sintering↗
DOE OSTI2025

Multi-frequency progressive refinement for learned inverse scattering

Interpreting scattered acoustic and electromagnetic wave patterns is a computational task that enables remote imaging in a number of important applications, including medical imaging, geophysical exploration, sonar and radar detection, and nondestructive testing of materials. However, accurately and stably recovering an inhomogeneous medium from far-field scattered wave measurements is a computationally difficult problem, due to the nonlinear and non-local nature of the forward scattering process. We design a neural network, called Multi-Frequency Inverse Scattering Network (MFISNet), and a training method to approximate the inverse map from far-field scattered wave measurements at multiple frequencies. We consider three variants of MFISNet, with the strongest performing variant inspired by the recursive linearization method — a commonly used technique for stably inverting scattered wavefield data — that progressively refines the estimate with higher frequency content. MFISNet outperforms past methods in regimes with high-contrast, heterogeneous large objects, and inhomogeneous unknown backgrounds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Measuring Success: A Refined Methodology for Estimating Long-term Continuous Improvement

Successful resource management systems require current and detailed feedback on operational and corporate-level performance. As corporate accountability concerns intensify, the precision and reliability of these performance metrics have become crucial. Traditional savings estimation methods can be difficult to understand, particularly linear regression, and can provide varying results. This paper reviews common efficiency metrics and highlights underlying mathematical inconsistencies when estimating total and percent savings with current methods. A refined approach to calculating long-term utility savings is proposed that simplifies current methodologies utilizing ratios to define an adjusted baseline, allowing for consistent and fair aggregation of results across multiple scales from resources to corporate performance. A simplified example demonstrates how the proposed methodology improves upon existing methods, especially in intermediate years. This paper’s major contributions are the simplified approach for converting modeled utility usage into estimated savings and the consistent roll-up methodology enabling more comparable, aggregable, and actionable results across scales.

Price, Chris [ORNL] (ORCID:0000000202007906)↗
DOE OSTI2025

Determination of Site Occupancy in the M–Pd–Zn (M = Cu, Ag, and Au) γ-Brass Phase by CALculation of PHAse Diagrams Modeling and Rietveld Refinement

The Pd–Zn γ-brass phase provides exciting opportunities for synthesizing site-isolated catalysts with precisely controlled Pd active site ensembles. Introducing a third metallic element into the γ-brass lattice further perturbs the catalytic active site ensembles. Here, in this work, we introduce coinage metallic elements M (M = Cu, Ag, and Au) into the Pd–Zn γ-brass phase and investigate the site occupation factors of each element in the γ-brass lattice. The CALculation of PHAse Diagrams (CALPHAD) modeling approach supported by energetics predicted by the density functional theory and X-ray and neutron diffraction with Rietveld refinement were used to identify the SOF on each Wyckoff site for various M amounts alloyed into the Pd–Zn γ-brass phase. The present analysis unveils the strong preference for Pd occupying the outer tetrahedral (OT) site in the γ-brass lattice, while the coinage metallic elements tend to substitute for Zn on the octahedral (OH) site. The determination of site occupancy in the bulk M–Pd–Zn γ-brass phase provides opportunities to investigate and tailor potential catalytically active site ensembles in the γ-brass phase materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The Martini 3 Lipidome: Expanded and Refined Parameters Improve Lipid Phase Behavior

Lipid membranes are central to cellular life. Complementing experiments, computational modeling has been essential in unraveling complex lipid-biomolecule interactions, crucial in both academia and industry. The Martini model, a coarse-grained force field for efficient molecular dynamics simulations, is widely used to study membrane phenomena but has faced limitations, particularly in capturing realistic lipid phase behavior. Here, we present refined Martini 3 lipid models with a mapping scheme that distinguishes lipid tails that differ by just two carbon atoms, enhancing the structural resolution and thermodynamic accuracy of model membrane systems including ternary mixtures. The expanded Martini lipid library includes thousands of models, enabling simulations of complex and biologically relevant systems. These advancements establish Martini as a robust platform for lipid-based simulations across diverse fields.

Lipids↗
DOE OSTI2026

Convective Biases in the US DOE Global Storm‐Resolving Model: Insights From Regionally Refined Simulations During the CACTI Campaign

Accurately simulating convective processes in complex terrain remains a critical challenge for global storm-resolving models (GSRMs). This study systematically evaluates moist convective biases in the Regionally Refined Mesh configuration of the U.S. Department of Energy Simple Cloud-Resolving E3SM Atmosphere Model (RRM-SCREAM) using comprehensive observations and large-eddy simulations from the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) campaign in the mountainous area of central Argentina. Comparisons of simulations with high-resolution observations and reanalysis data indicate that RRM-SCREAM effectively captures large-scale meteorological patterns, including regional atmospheric gradients and diurnal variability. However, RRM-SCREAM disproportionately produces smaller precipitation clusters referred to as “popcorn convection,” and exaggerated rainfall intensities compared to observations and reference models. Detailed examination of a representative orographic shallow-to-deep convective transition case shows that RRM-SCREAM delays initial shallow convection growth due to lower-tropospheric dryness and sustained convective inhibition, but once triggered, deep convection becomes overly vigorous with excessively strong vertical velocities and elevated cloud ice content, linked to a thermodynamic structure characterized by suppressed low-level moistening and excessive upper-level moisture retention. Our results highlight specific deficiencies in the model representation of convective vertical velocity, cloud microphysical processes, and convective precipitation organization within RRM-SCREAM. Addressing these biases is essential for improving the predictions of convective clouds and precipitation in the global high-resolution atmospheric models.

Su, Tianning [Lawrence Livermore National Laborato↗
DOE OSTI2026

Validation Studies of RELAP5-3D for High-Temperature Gas-Cooled Reactor Analysis Using a Refined RELAP5-3D Model of the High Temperature Test Facility

RELAP5-3D has been used extensively for high-temperature gas-cooled reactor (HTGR) analysis, but such analysis lies outside the validation basis for the code. Oregon State University (OSU) and the United States Department of Energy’s (DOE) Advanced Reactor Technologies (ART) – Gas-cooled reactor campaign worked together to design and operate the High Temperature Test Facility (HTTF) at OSU to provide validation data for prismatic HTGR thermal hydraulics analysis. Work using a previous RELAP5-3D model of HTTF demonstrated the ability to reproduce trends in HTTF data but an inability to reproduce the values measured in multiple experiments within the uncertainty. It was hypothesized that a new model with a finer radial nodalization would perform better for HTTF analysis. In this paper, we present the development of that more refined model and analyze HTTF experiments PG-27 and PG-29, a pressurized (PCC) and depressurized conduction cooldown (DCC) transient. Comparing PG-27 results to experimental data shows a better prediction of steady state and transient temperatures; however, a deeper dive reveals that better transient temperature predictions are a result of better initial conditions. The transient temperature rise in the core is comparable between the two models, but the legacy model predicts temperatures in the reflectors better. The new model demonstrates the ability to predict unique temperatures in each sector of the core during PG-29, capturing the azimuthal asymmetry; however, the temperature predictions still differ from measured temperatures. One hypothesis is that differences between the assumed and actual power distribution leads to the underprediction of the individual block temperatures. These results suggest that gaps and uncertainties in the measured boundary conditions for the HTTF tests do not allow to fully validate RELAP5-3D for prismatic HTGR analyses. The authors suspect that some of the differences between predicted and measured temperatures may be a result of uncertainties associated with HTTF itself rather than RELAP5-3D. We demonstrated improved validation for steady-state analysis, but significantly improved transient performance has yet to be recognized.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Axion Mass Prediction from Adaptive Mesh Refinement Cosmological Lattice Simulations

The quantum chromodynamics (QCD) axion arises as the pseudo-Goldstone mode of a spontaneously broken Abelian Peccei-Quinn (PQ) symmetry. If the scale of PQ symmetry breaking occurs below the inflationary reheat temperature and the domain wall number is unity, then there is a unique axion mass that gives the observed dark matter (DM) abundance. Computing this mass has been the subject of intensive numerical simulations for decades since the mass prediction informs laboratory experiments. Axion strings develop below the PQ symmetry-breaking temperature, and as the string network evolves, it emits axions that go on to become the DM. A key ingredient in the axion mass prediction is the spectral index of axion radiation emitted by the axion strings. We compute this index in this Letter using the most precise and accurate large-scale simulations to date of the axion-string network leveraging adaptive mesh refinement to achieve the precision that would, otherwise, require a static lattice with 262,144 3 lattice sites. We find a scale-invariant axion radiation spectrum to within 1% precision and find no evidence that the spectral index of radiation evolves with time. Accounting for axion production from strings prior to the QCD phase transition leads us to predict that the axion mass should be approximately 𝑚 𝑎 ∈ (45, 65) μ ⁢eV. However, we provide preliminary evidence that axions are produced in greater quantities from the string-domain-wall network collapse during the QCD phase transition, potentially increasing the mass prediction to as much as 300 μ ⁢eV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Refining the two-band model for highly compensated semimetals using thermoelectric coefficients

In studying compensated semimetals, the two-band model has proven extremely useful in capturing electrical conductivity under magnetic field, as a function of density and mobility of electronlike and holelike carriers. However, it rarely offers practical insight into magnetothermoelectric properties. Here, in this work, we report the field dependence of thermoelectric (TE) coefficients in a highly compensated semimetal NbSb 2 , where we find the Seebeck (𝑆 𝑥⁢𝑥 ) and Nernst (𝑆 𝑥⁢𝑦 ) coefficients increase quadratically and linearly with applied magnetic field, respectively. Such field dependence was predicted in previous work that studied a system of two parabolic bands, within semiclassical Boltzmann transport theory when the following two conditions are simultaneously met: 𝜔 𝑐 ⁢𝜏 ≫ 1 and tan ⁡𝜃 𝐻 ≪ 1, where 𝜔 𝑐 and 𝜏 refer to the cyclotron frequency and relaxation time, respectively, and 𝜃 𝐻 is the Hall angle. Under these conditions, we find the field dependence of the TE coefficients directly provides a relation between the electronlike (𝑛 𝑒 ) and holelike (𝑛 ℎ ) carrier densities, which in turn can be used to refine two-band model fitting. With this, we find the compensation factor ($\frac{|Δ⁢𝑛|}{𝑛_{𝑒}}$, where Δ⁢𝑛 = 𝑛 𝑒 −𝑛 ℎ ) of NbSb 2 is two orders of magnitude smaller than what was found in unrestricted fitting, resulting in a larger saturation-field scale for magnetoresistance. Within the same framework of the semiclassical theory, we also deduce that the thermoelectric Hall angle tan⁡ 𝜃 𝛾 = $\frac{𝑆_{𝑥⁢𝑦}}{𝑆_{𝑥⁢𝑥}}$ can be expressed as ($\frac{Δ⁢𝑛}{𝑛_{𝑒}}$ ×𝜔 𝑐 ⁢𝜏) −1 , which serves as a parameter to predict the degree of compensation. Our findings offer crucial insights into identifying empirical conditions for field-induced enhancement of TE performance and into engineering-efficient thermoelectric devices based on semimetallic materials.

electrical conductivity↗
DOE OSTI2024

Integrating machine learning interatomic potentials with hybrid reverse Monte Carlo structure refinements in RMCProfile

Structure refinement with reverse Monte Carlo (RMC) is a powerful tool for interpreting experimental diffraction data. To ensure that the under-constrained RMC algorithm yields reasonable results, the hybrid RMC approach applies interatomic potentials to obtain solutions that are both physically sensible and in agreement with experiment. To expand the range of materials that can be studied with hybrid RMC, we have implemented a new interatomic potential constraint in RMCProfile that grants flexibility to apply potentials supported by the Large-scale Atomic/Molecular Massively Parallel Simulator ( LAMMPS ) molecular dynamics code. This includes machine learning interatomic potentials, which provide a pathway to applying hybrid RMC to materials without currently available interatomic potentials. To this end, we present a methodology to use RMC to train machine learning interatomic potentials for hybrid RMC applications.

Cuillier, Paul↗