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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Ab Initio Design of High-Entropy Thermal/ Environmental Barrier Coatings

Next generation thermal/environmental barrier coatings (TEBC) require carefully balancing various properties including phase stability, thermal conductivity, coefficient of thermal expansion (CTE), mechanical properties, and resistance against hot corrosion and water vapor recession. This work mainly focuses on rapid design of cost-effective high entropy rare-earth disilicates and aluminum garnets to protect SiC-based ceramic matrix composites and nickel-based superalloys in the hot section of gas turbine engines using density functional theory methods. Our calculations identify several low-cost high entropy TEBC exhibiting ultralow thermal conductivity at 1500 K and desirable CTE while maintaining good mechanical properties, including Er1/2Y3/4Yb3/4Si2O7, Gd1/4Er1/4Y3/4Yb3/4Si2O7, Eu1/4Er1/4Y3/4Yb3/4Si2O7, and (Y1/4Gd1/4Er1/4Yb1/4)3Al5O12. This work also aims to gain fundamental understanding of oxygen diffusion in model disilicates. Minimizing oxidizer (such as water vapor and oxygen) permeability through the EBC layer can significantly decrease the growth rate of thermally grown oxide and extend the service life of the coating system. Oxygen diffusion mechanisms including formation energy of defects under varying oxygen conditions and defect migration energy barriers will be presented.

coefficient of thermal expansion↗
DOE OSTI2026

A comparison of the dielectric function for p-point water models

We compute the longitudinal dielectric function ɛl(k) for p-point water models, p = 3 (SPC/E, TIP3P), p = 4 (TIP4P-EW, OPC), and p = 5 (TIP5P-E) from the charge–charge fluctuation function SZZ(k), obtained from two methods: average of the charge in k-space (method:sum) and from the pair distribution function in Fourier space (method:integral). The latter requires a continuation to small k-values, which we thoroughly discuss. We conclude with a detailed comparison of the longitudinal dielectric function for the different p-point models and its main characteristics: small k and large k limits, position of the poles, and its (complex) zeros.

Shevy, Noah [Ames National Laboratory, Department ↗
DOE OSTI2025

Monocrystalline CdSeTe/MgCdTe Double‐Heterostructure Solar Cells

This article reports monocrystalline CdSeTe/MgCdTe double‐heterostructure (DH) solar cells with varying Se compositions in the absorber layers that are grown on InSb substrates by using molecular beam epitaxy. The Se composition in the samples studied is determined to be 4%–11% through high‐resolution X‐ray diffraction (XRD) and photoluminescence measurements. Increased Se incorporation induces higher defect density attributed to increased lattice mismatch, and mixed‐phase formation due to the small difference in formation energies between the wurtzite and zinc blende phases of CdSe, which causes stacking faults and grain boundaries. Devices are fabricated by directly depositing an n‐type indium tin oxide (ITO) layer on the CdSeTe/MgCdTe DHs followed by Ag metal contacts. Reduced bandgap is observed in solar cells with increased Se composition. The devices with an absorber containing 4% Se exhibit an average open‐circuit voltage ( V OC ) of 0.925 V, a short‐circuit current density ( J SC ) of 23.4 mA/cm 2 , a fill factor ( FF ) of 0.650 and an efficiency of 14.1% without anti‐reflection coating. Absorbers with higher Se compositions result in poor device performance, mainly attributed to high defect density in the materials.

Ju, Zheng [Center for Photonics Innovation Arizona↗
DOE OSTI2024

Models implemented in the methodological approach to design the initial STEP first wall contour

The official Spherical Tokamak for Energy Production mission aims to demonstrate the ability to generate net electricity from fusion with the STEP Prototype Power plant. One of the key technological and engineering challenges in fusion power plants is managing the loads on the first wall within acceptable limits. Therefore, the conceptual design development of the STEP Prototype Power plant needs to be based on load estimates derived using legitimate plasma physics assumptions through dynamic and flexible tools. The current design foresees the STEP main chamber first wall to withstand steady-state heat loads of up to ~1 MW/m 2 , excluding critical regions expected to receive higher heat loads such as the baffle regions approaching the divertors. These critical areas will require ad hoc assessments and will be designed with the presence of limiters. This article focuses on the models and methodology adopted for designing the 2-D poloidal contour of the STEP first wall, based on the anticipated charged particle and radiation heat loads during normal operation. Firstly, the models adopted for calculating the charged particle and radiation heat loads are introduced. The first model is validated through benchmarking against the particle tracing code SMARDDA, while the second model is verified by comparing it with data from the MAST-U experiment. Secondly, the model used to design the 2-D first wall contour according to the heat loads is explained. We acknowledge that this preliminary design stage assumes certain simplifications, notably an axisymmetric geometry, for computational efficiency and clarity in presentation. It is understood that subsequent design phases will address the complexities of real-world engineering, including non-axisymmetric effects, transient plasma scenarios, and the impact of disruptions on the first wall design. Finally, an automatic procedure based on these models is presented for defining the 2-D poloidal contour of the STEP first wall to minimize heat loads, taking into account the need to radiate most of the alpha-particle and auxiliary heating power. Here, by providing an overview of the models, methodology, and an automatic procedure, this paper contributes to the design process of the STEP first wall, addressing the engineering challenges associated with fusion power plant development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Graph network heterogeneity predicts interplant wake losses

Wind plants generate large-scale wakes, which can affect the performance of neighboring installations. Such wakes are challenging to estimate due to the inherent complexity in modeling wake interactions between large quantities of turbines at various distances. Weighted directed graph networks can inform complex models by linking turbine pairs into chains of upstream and downstream neighbors for a given wind direction. A novel interpretation of the graph network adjacency matrix is proposed where each element of the matrix represents the cumulative impact of upstream turbines on an individual. In this study, wake losses were estimated with an engineering wake model across a range of inflow conditions for nine parametric variations of a system containing two neighboring wind plants. The parametric nature of the study isolates turbine spacing within the plant, separation distance between plants, and wind direction as the main drivers of wake losses. Spatial heterogeneity is computed from the weighted average adjacency matrix of each plant arrangement. The proposed method is orders of magnitude faster than wake modeling and does not require detailed turbine information or atmospheric conditions. Furthermore, the weighted average adjacency matrix provides insight on the spatial organization of wake losses at various scales. Plant heterogeneity is correlated with wake losses within and among plants. Framing wind plant wake interaction in terms of graph network spatial heterogeneity provides an efficient approach for predicting wake losses within and among neighboring wind plants with applications to other complex systems where wake interactions are key factor.

17 WIND ENERGY↗
DOE OSTI2025

Uncertainty-Guided Prediction Horizon of Phase-Resolved Ocean Wave Forecasting Under Data Sparsity: Experimental and Numerical Evaluation

Accurate short-term wave forecasting is critical for the safe and efficient operation of marine structures that rely on real-time, phase-resolved ocean wave information for control and monitoring purposes (e.g., digital twins). These systems often depend on environmental sensors (e.g., waverider buoys, wave-sensing LIDAR). Challenges arise when upstream sensor data are missing, sparse, or phase-shifted due to drift. This study investigates the performance of two machine learning models, time-series dense encoder (TiDE) and long short-term memory (LSTM), for forecasting phase-resolved ocean surface elevations under varying degrees of data degradation. We introduce the τ-trimming algorithm, which adapts the prediction horizon based on uncertainty thresholds derived from historical forecasts. Numerical wave tank (NWT) and wave basin experiments are used to benchmark model performance under short- and long-term data masking, spatially coarse sensor grids, and upstream phase shifts. Results show under a 50% probability of upstream data loss, the τ-trimmed TiDE model achieves a 46% reduction in error at the most upstream target, compared to 22% for LSTM. Furthermore, phase misalignment in upstream data introduces a near-linear increase in forecast error. Under moderate model settings, a ±3 s misalignment increases the mean absolute error by approximately 0.5 m, while the same error is accumulated at ±4 s using the more conservative approach. These findings inform the design of resilient, uncertainty-aware wave forecasting systems suited for realistic offshore sensing environments.

42 ENGINEERING↗
DOE OSTI2025

Parameter uncertainties for imperfect surrogate models in the low-noise regime

Abstract Bayesian regression determines model parameters by minimizing the expected loss, an upper bound to the true generalization error. However, this loss ignores model form error, or misspecification, meaning parameter uncertainties are significantly underestimated and vanish in the large data limit. As misspecification is the main source of uncertainty for surrogate models of low-noise calculations, such as those arising in atomistic simulation, predictive uncertainties are systematically underestimated. We analyze the true generalization error of misspecified, near-deterministic surrogate models, a regime of broad relevance in science and engineering. We show that posterior parameter distributions must cover every training point to avoid a divergence in the generalization error and design a compatible ansatz which incurs minimal overhead for linear models. The approach is demonstrated on model problems before application to thousand-dimensional datasets in atomistic machine learning. Our efficient misspecification-aware scheme gives accurate prediction and bounding of test errors in terms of parameter uncertainties, allowing this important source of uncertainty to be incorporated in multi-scale computational workflows.

Swinburne, Thomas D. (ORCID:0000000232554257)↗
DOE OSTI2025

Integrase-on-Demand

SAND2025-07449O Integrase-on-Demand is a software tool that allows users to identify regions in genomic sequences where genetic material can be integrated with high probability. It uses a database of integrases and their DNA attachment sites to search against any genomic sequence, producing a list of open sites, the integrase sequence, and the source of the genomic island. The program requires MASH software to be available on the system. It consists of a main script and a precomputed input file, with a taxonomy mode that searches closely related genomes and a search mode that looks for identical attachment site matches in the integrase/attachment input file. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Williams, Kelly [Sandia National Lab. (SNL-CA), Li↗
DOE OSTI2025

Twinac: initiation of a community-driven accelerator digital twin framework

We present the initiation of a community-driven framework for the integration of accelerator digital twins into control systems: Twinac. Few facilities have fully integrated accelerator digital twins like at Cornell’s CHESS. Many facilities have active research to employ surrogate models to aid in operational decisions like at Argonne’s ALS, MSU’s FRIB, SLAC’s LCLS-II, and Fermilab’s FAST/IOTA, PIP-II, and main complex. To lower the barrier to entry for all accelerator facilities to build and benefit from a digital twin of their own accelerators, we propose the following software framework. Twinac will provide the capability to compose one’s own digital twin using reusable components engineered at other facilities. With this model in place, Twinac will also support tools for (1) predictive maintenance systems; (2) discovery of correlated but uncontrolled environmental factors, like seasonal temperature variations causing performance changes on power supplies, magnets, etc.; and (3) prototyping and updating sophisticated optimization and controls algorithms. The Twinac framework will enable sharing and simplified deployment of modeled components and control algorithms at all facilities. With an inter-facility team to build and support the Twinac framework, it will be easy to publish and try out the latest advancements at one’s own facility.

Miceli, Tia [Fermilab]↗
DOE OSTI2024

Design and Performance of an InAs Quantum Dot Scintillator with Integrated Photodetector

A new scintillation material composed of InAs quantum dots (QDs) hosted within a GaAs matrix was developed, and its performance with different types of radiation is evaluated. A methodology for designing an integrated photodetector (PD) with a low defect density and that is optically matched to the QD’s emission spectrum is introduced, utilizing an engineered epitaxial InAlGaAs metamorphic buffer layer. The photoluminescence (PL) collection efficiency of the integrated PD is examined using two-dimensional scanning laser excitation. The detector response to 5.5 MeV α-particles and 122 keV photons is presented. Yields of 13 electrons/keV for α-particles and 30–60 electrons/keV for photons were observed. The energy resolution of 12% observed with α-particles was mainly limited by noise- and geometry-related optical losses. The radiation hardness of an InAs QDs hosted within GaAs and a wider band gap AlGaAs ternary alloy was studied under a 1 MeV proton implantation up to a 10 14 cm –2 dose. The integrated PL responses were compared to evaluate PL quenching due to non-radiative defects. The QDs embedded in the AlGaAs demonstrated improved radiation hardness compared to QDs in the GaAs matrix and in the InGaAs quantum wells.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Numerical Investigation of Ammonia/n-Heptane Dual-Fuel Spray Flames Using Large Eddy Simulations

Diesel engines are extensively used in heavy-duty transportation, power generation, and marine vehicles due to their superior thermal efficiency and extended high-load operability compared to spark ignition (SI) engines. However, combustion in diesel engines is generally characterized by locally rich fuel–air mixtures and high combustion temperatures, causing significant amounts of soot and NO x emissions from these engines. Utilizing carbon-free alternative fuels and enhancing fuel efficiency represent promising strategies to mitigate greenhouse gas (GHG) and other emissions in the heavy-duty transportation sector. In this context, ammonia (NH 3 ), as a hydrogen carrier, has received significant attention as a viable substitute for hydrocarbon fuels due to its carbon-free composition, relatively high energy density, and well-established infrastructure. Many previous studies have considered combustion and emission characteristics of ammonia-hydrocarbon fuel blends in engines and simplified flames. But, detailed investigations on the effects of ammonia on the performance of hydrocarbon fuels under engine conditions are lacking. In the present study, we perform large eddy simulations (LES) of the ignition and flame processes in a constant-volume combustion reactor, where n-heptane is injected in an ammonia/air ambient mixture in a diesel-like environment. A detailed and validated reaction mechanism containing 302 species and 1981 reactions is employed. The Engine Combustion Network Spray H experimental data is used to validate the spray model under both non-reacting and reacting conditions. Dual-fuel combustion is simulated using the well-stirred reactor (WSR) approach. Results are presented for two spray cases: (1) single fuel (SF) with n-heptane injected into a mixture of air and combustion products and (2) dual-fuel (DF) with the injection of n-heptane in a mixture of air, ammonia, and combustion products. It is observed that the presence of ammonia has a significant effect on the ignition and flame development processes. With ammonia addition, both the first- and second-stage ignition delay times increase, but the effect of ammonia on the second-stage ignition is significantly more prominent. In addition, the ignition kernel size and growth rate decrease noticeably. For SF spray, the main ignition is characterized by multiple ignition kernels near the spray tip, whereas for DF spray, a single relatively small ignition kernel forms and grows slowly in the downstream direction. The flame development and the final quasi-steady flame structure are also modified due to ammonia. Here, the outcome of this research would enable a better understanding of ammonia–diesel dual-fuel spray flame behavior and guide the development of associated engine combustion strategies.

ammonia↗
DOE OSTI2026

Bypassing the yellow phase for extremely stable formamidinium lead iodide perovskite solar cells

INTRODUCTION Formamidinium lead iodide (FAPI) emerged as an ideal material for single-junction perovskite solar cells owing to its near optimal bandgap of 1.45 to 1.5 eV and outstanding thermal stability. However, the photoactive cubic α-phase (3C-FAPI) of FAPI is structurally unstable and undergoes a reconstructive phase transition to the nonperovskite yellow hexagonal δ-phase (2H-FAPI) at ambient temperature. The phase reconstruction from 3C-FAPI to 2H-FAPI could be prevented by alloying methylammonium (MA) or Cs or both at the A-site and Br at the halide site, but this limits long-term durability owing to phase segregation or materials instability. Addressing these challenges requires a rational design strategy to stabilize 3C-FAPI by restricting lattice reconstruction without compromising thermal stability. RATIONALE Two main strategies have emerged to improve the phase stability and film quality of FAPI. Here, the first is a lattice-templating approach, which enables the slow formation of perovskite but it eventually degrades through the formation of yellow phases. The second approach, which has been widely explored, involves additive engineering, using alkyl ammonium halide or mostly chloride-based additives, which provide better control over the crystallization route. However, the FAPI films fabricated using these additives are often alloyed and compromise long-term stability. Moreover, the exact role of Cl has been unclear and speculative, specifically when Cl-based additives are used. Even after using a high additive concentration, the incorporation of Cl in perovskite lattice is rare. RESULTS Guided by synergistic modeling and experimental studies, we developed a coadditive strategy using 15 mol % FACl and 0.5 mol % BA 2 PbI 4 perovskites in combination (where BA is butylammonium) to enable a highly oriented (100) Cl-doped FAPI film with exceptional durability. Synchrotron-based in situ wide-angle x-ray scattering revealed a favorable transition for the coadditive-treated FAPI (FAPI-CA) to the corner-sharing 3C black phase through a progressive transformation through the 2H, 4H, 6H, and 8H phases. Moreover, solid-state 35 Cl nuclear magnetic resonance (NMR) revealed Cl incorporation in the perovskite lattice and, as predicted by modeling, indicated that Cl plays a key role in altering the energetics of both the formation and degradation pathways. The Cl-doped perovskite can completely bypass the expected and energetically favorable degradation pathway via the yellow phase or the 2H-PbI 2 phase. Instead, it undergoes degradation only upon exposure to harsh conditions such as 15-sun illumination and 90°C through the energetically uphill 3R-PbI 2 phase path. A p-i-n device fabricated with FAPI-CA film demonstrated a power conversion efficiency (PCE) of 25.1% with an average of 24.1% (40 devices). The notable film stability translated to other devices and retained 98% of its initial PCE under open-circuit conditions at 85° ± 5°C for 1200 hours. CONCLUSION Our study highlights the decisive role of chloride in regulating both the formation and degradation pathways. This regulation is critical for creating a perovskite film with commercially relevant durability.

Garai, Rabindranath [Rice Univ., Houston, TX (Unit↗
DOE OSTI2026

Grid Architecture Mapping to Understand Transformation (GAMUT): Methods and Framework Architecture

Grid architecture (GA) is a concept that was developed to address the need for a comprehensive view of power grid challenges. GA can be viewed as a relatively consistent and fixed high-level approach; however, for any instantiation of grid structures, a combinatorial explosion results from each lower-layer expansion. This constitutes the main challenge with GA—it is a grid architect’s view of the system, which might not be very informative at the implementation level. Grid Architecture Mapping to Understand Transformation (GAMUT project) seeks to bridge that gap by integrating subject matter expertise across GA structures, providing users who lack expertise in GA approaches with valuable insights and informational materials. GAMUT seeks to answer feasibility questions for the approach. System-level expectations are that a GA baseline needs to be established in order for GA to be the common framework to which any lower layer approach is tied. This report explores a potential information ingestion and documentation framework to support GAMUT. The main concepts that enable the solution domain of GAMUT are discussed, and examples are provided. The solution domain leverages already-existing technology and concepts related to GA, knowledge management, and other relevant areas. To assess GAMUT building blocks and the overall approach, a feasibility assessment is proposed, rooted in systems engineering and GA architecture evaluation concepts.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

The Short-Baseline Near Detector at Fermilab: Input to the European Strategy for Particle Physics 2026 Update

SBND is a 112 ton liquid argon time projection chamber (LArTPC) neutrino detector located 110 meters from the Booster Neutrino Beam (BNB) target at Fermilab. Its main goals include searches for eV-scale sterile neutrinos as part of the Short-Baseline Neutrino (SBN) program, other searches for physics beyond the Standard Model, and precision studies of neutrino-argon interactions. In addition, SBND is providing a platform for LArTPC neutrino detector technology development and is an excellent training ground for the international group of scientists and engineers working towards the upcoming flagship Deep Underground Neutrino Experiment (DUNE). SBND began operation in July 2024, and started collecting stable neutrino beam data in December 2024 with an unprecedented rate of ~7,000 neutrino events per day. During its currently approved operation plans (2024-2027), SBND is expected to accumulate nearly 10 million neutrino interactions. The near detector dataset will be instrumental in testing the sterile neutrino hypothesis with unprecedented sensitivity in SBN and in probing signals of beyond the Standard Model physics. It will also be used to significantly advance our understanding of the physics of neutrino-argon interactions ahead of DUNE. After the planned accelerator restart at Fermilab (2029+), opportunities are being explored to operate SBND in antineutrino mode in order to address the scarcity of antineutrino-argon scattering data, or in a dedicated beam-dump mode to significantly enhance sensitivity to searches for new physics. SBND is an international effort, with approximately 40% of institutions from Europe, contributing to detector construction, commissioning, software development, and data analysis. Continued European involvement and leadership are essential during SBND's operations and analysis phase for both the success of SBND, SBN and its role leading up to DUNE.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

OC7 phase I: Toward practical sea-state-dependent modeling of hydrodynamic viscous drag and damping

Here, this article presents a collaborative research campaign under the OC7 project on refining the engineering modeling approach for hydrodynamic viscous drag and damping on floating wind platforms, focusing on the adjustment of hydrodynamic drag and damping coefficients for different sea states. The participant simulation results show significant improvements over the previous OC6 project in predicting the low-frequency resonance motion under nonoperational conditions. The improvements are mainly due to enhanced modeling, including the adoption of wave stretching, and directly tuning the coefficients to measured platform motion in waves instead of free decay. For accurate predictions of mean- and slow-drift motion, the better performing models use a decreasing column splash zone drag coefficient and increasing surge damping/drag with increasing wave height. The model tuning for heave and pitch resonance shows less consistency. Generally, both quadratic drag and additional heave or pitch damping are needed for accurate predictions. Alternatively, a quadratic drag formulation with velocity filtering for the rectangular pontoons leads to improved predictions without additional damping. This model is also potentially more predictive, requiring minimal adjustment to its parameters for different conditions.

17 WIND ENERGY↗
DOE OSTI2024

Environmentally Assisted Fatigue in Light Water Reactor Environment

This report summarizes the Environmentally Assisted Fatigue (EAF) research conducted at ANL under the US DOE Light Water Reactor Sustainability (LWRS) program. Starting from a rich background in theoretical and experimental EAF, ANL previously developed an approach to evaluate fatigue performance of reactor materials in light water reactor environments with the correction factor F en . The approach was based on a large body of experimental work performed at ANL and elsewhere, and was consistent with American Society of Mechanical Engineers (ASME)’s methodology governing the design and construction of reactor components. In recent years, the program was focused on component fatigue prediction and made several major and fundamental contributions in this area. These accomplishments help meet the needs identified by the industry concerning component level fatigue predictions in complex, transient conditions. The main contribution of the ANL program involved the development of a system-level model for estimating residual strain and life of nuclear reactor coolant system components under connected-system-thermal-mechanical boundary conditions. The goal was to predict the stress hotspots, strain residuals, strain amplitudes and the resulting fatigue lives. Thermal-mechanical stress analysis was performed considering thermal stratification and a design-basis reactor loading cycle. Based on the finite element (FE) model results, the strain residuals, strain amplitudes and resulting fatigue lives of reactor coolant system (RCS) components were predicted. The results show that some of the RCS components can have significantly different strain amplitudes, residual strain, and fatigue lives, despite having similar geometry and material. In addition, the simulated component-level strain profile can guide the selection of appropriate test inputs for conducting laboratory-scale EAF tests. Building upon the system-level model, ANL developed a digital twin (DT) framework to predict the structural states and associated fatigue life of components in real-time. This framework is a comprehensive system designed to predict the structural states and fatigue lives of reactor components. It includes multiple models and integrates artificial intelligence (AI), machine learning (ML), and FE based modeling tools to evaluate the structural states and fatigue lives.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Accurate machine-learning predictions of coercivity in high-performance permanent magnets

Increased demand for high-performance permanent magnets in the electric vehicle and wind-turbine industries has prompted the search for cost-effective alternatives. Discovering magnetic materials with the desired intrinsic and extrinsic permanent magnet properties presents a significant challenge to researchers because of issues with the global supply of rare-earth elements, material stability, and a low maximum magnetic energy product BH max . While first-principles density functional theory (DFT) predicts materials’ magnetic moments, magnetocrystalline anisotropy constants, and exchange interactions, it cannot compute extrinsic properties such as coercivity (H c ). Although it is possible to calculate H c theoretically with micromagnetic simulations, the predicted value is larger than the experiment by almost an order of magnitude due to the Brown paradox. To circumvent these issues, we employ machine-learning (ML) methods on an extensive database obtained from experiments, DFT calculations, and micromagnetic modeling. The use of a large experimental dataset enables realistic H c predictions for materials such as Ce-doped Nd 2 ⁢Fe 14 ⁢B, comparing favorably against micromagnetically simulated coercivities. Remarkably, our ML model accurately identifies uniaxial magneto-crystalline anisotropy as the primary contributor to H c . With DFT calculations, we predict the Nd-site-dependent magnetic anisotropy behavior in Nd 2 ⁢Fe 14 ⁢B, confirming that Nd 4⁢g sites mainly contribute to uniaxial magnetocrystalline anisotropy, and also calculate the Curie temperature (T c ). Finally, both calculated results are in good agreement with the experiments. The coupled experimental dataset and ML modeling with DFT input predict H c with far greater accuracy and speed than was previously possible using micromagnetic modeling. Further, we reverse engineer the grain-boundary and intergrain exchange coupling with micromagnetic simulations by employing the ML predictions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

LOTO Training Flow Simulator Design

The LOTO Training Flow Simulator Design poster discusses the intended purpose of the simulator along with the steps taken in the design process. It highlights the two main systems designed in the primary flow loop and the bubbler system, along with a brief description of the flow calculations made to ensure the functionality of the system.

42 - ENGINEERING↗