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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 OSTI2026

Localized Interactions in Neutrino Simulations

The Deep Underground Neutrino Experiment (DUNE) requires precise modeling of neutrino--nucleus interactions to achieve its neutrino-oscillation measurement goals. GENIE, the Monte Carlo event generator used by DUNE, exhibits a known discrepancy with MicroBooNE measurements of transverse kinematic imbalance (TKI): the data display a larger high-TKI tail while maintaining a peak similar to that predicted by the baseline GENIE model. Previous variations of final-state interaction (FSI) strength affected both the peak and the tail and therefore did not resolve the discrepancy. This work investigates whether inconsistencies between local and global treatments of intranuclear physics contribute to the observed mismodeling. The GENIE FSI routines were modified to use the particle position when generating nucleons, thereby introducing a local-density treatment in the hA and hN intranuclear models for their 2018 and 2025 implementations. Meson-exchange-current (MEC) localization was also tested for the hN 2018 model. Comparisons of the intranuclear scattering-center momentum and its radial dependence confirm that the localization was implemented as intended. Localization produces modest changes in the proton momentum spectrum, primarily at low momentum, but only a small change in the TKI distribution between approximately \SI{0.25}{\giga\electronvolt} and \SI{0.40}{\giga\electronvolt}. These changes are insufficient to account for the discrepancy with MicroBooNE data. Although a localized treatment improves the internal consistency of the GENIE model, the origin of the TKI discrepancy remains unresolved.

Bulla, Braden [Unlisted, US, IL]↗
DOE OSTI2026

Studies of Localization Effects on Transverse Kinematic Imbalance in the GENIE Generator

The forthcoming Deep Underground Neutrino Experiment (DUNE) requires precise modeling of neutrino--nucleus interactions to achieve its neutrino-oscillation measurement goals. GENIE, the Monte Carlo event generator used by DUNE and other Fermilab-based experiments as a central value model for neutrino-nucleus cross sections, exhibits known discrepancies in comparison to MicroBooNE cross section data, especially when considering neutrino scattering events' transverse kinematic imbalance (TKI). For quasielastic-like measurements, the data show a larger transverse missing momentum tail while maintaining a peak similar to that predicted by the baseline GENIE model. Previous attempts at a solution include variations of final-state interaction (FSI) strengths, incorrectly decreasing both the peak and tail, thus leaving the discrepancy unresolved. This work investigates whether inconsistencies between local and global treatments of intranuclear physics contribute to the observed mismodeling. In this work, GENIE FSI routines have been modified to directly utilize intranuclear particle positions, thereby introducing a newly localized momentum treatment in correlation with the nominal density in both the hA and hN intranuclear cascade models for their 2018 and 2025 variants. Meson-exchange-current (MEC) localization was also tested for the hN 2018 model. Comparisons of the intranuclear nucleons' ("scattering center") momentum and its radial dependence validate the localization implementation. However, while such consistent localization produces modest changes in the proton momentum spectrum at low-to-medium momenta, only small changes are observed in the TKI distributions. These changes are insufficient to account for the discrepancy with MicroBooNE data. Although a localized treatment improves the internal consistency of the GENIE model, the origin of the TKI discrepancy remains unresolved by such a solution.

Bulla, Braden [Ctr. Coll., Danville; Fermilab]↗
DOE OSTI2025

LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty

Gridded population datasets represent high-resolution distributions of human occupancy, enabling informed decision-making across a broad range of fields. These data products are valuable for assessing environmental risk, urban development, disaster preparedness and resource allocation—areas where accurate population estimates directly enhance policy effectiveness and optimize resource distribution. Despite the importance of gridded population datasets, traditional population modeling approaches often overlook inherent uncertainties in the estimation process. This limitation can create a false sense of certainty in population estimates, potentially leading to flawed decisions by those who rely on the data. To address this methodological gap, we introduce a probabilistic machine learning modeling framework, LandScan Mosaic, that explicitly incorporates uncertainty into the population modeling process. Our approach systematically quantifies uncertainty in three key modeling parameters of the LandScan HD gridded population dataset: building use types, floor counts, and occupancy rates. By employing Monte Carlo simulations, we propagate these uncertainties through the modeling process, yielding probability distributions of population counts in place of deterministic point estimates. We demonstrate the practical application of this framework in Iloilo City, Philippines, using structured decision-making techniques and our probabilistic estimates to identify and prioritize areas most affected by projected flooding, supporting targeted interventions that address both economic and social risks. In doing so, we propose a population-specific approach for incorporating confidence into structured decision making processes. Through a comparative analysis with conventional deterministic approaches and point estimate approaches, including LandScan HD and WorldPop, we evaluate how the incorporation of machine learning and uncertainty influences decision rankings. This research advances population distribution modeling by offering a robust, quantitative approach that explicitly accounts for uncertainty in the underlying data, along with guidance for how users can apply uncertainty in their decision-making.

Environmental sciences↗
DOE OSTI2026

Reliability Assessment of Cooling Fans for PV Inverters: Testing, Modeling, and Case Studies

The reliability of photovoltaic (PV) inverters is critical for long-term solar system performance, with cooling fan failures frequently leading to costly downtime. While much research exists on general cooling fan reliability, little attention has been given to fans operating within PV inverters and their unique environmental challenges. Here, this article proposes a comprehensive methodology to address this gap. First, a failure mode and effects analysis is performed on fans to identify the key failure mechanisms in PV applications, their corresponding stressors, and the models necessary for lifetime prediction. Second, an accelerated life test is designed and conducted to collect valuable experimental data for PV inverter fans in a reasonable amount of time. Third, a mathematical conversion of dynamic mission profiles into effective constant stress levels is derived. Fourth, case studies are given, showcasing lifetime estimates that account for geographic variations in mission profile data. The results demonstrate that this integrated approach leads to an accurate reliability assessment for PV inverter cooling fans.

accelerated life testing (ALT)↗
DOE OSTI2025

Flexible User-Defined Domain Decomposition in Kilometer-Scale E3SM Land Model Simulation

The Energy Exascale Earth System Model (E3SM) Land Model (ELM) has been extended to kilometer-scale (km-ELM) resolutions, enabling high-fidelity simulations of terrestrial processes at 1 km x 1 km grid spacing. In ELM, domain decomposition partitions the computational domain across processors, ensuring efficient parallel execution. Currently, round-robin decomposition is applied, providing a straightforward way to distribute computational workload. As ELM continues evolving at the kilometer-scale (km-scale), particularly with integrating lateral flow modeling, decomposition strategies must also account for the increased workload and data movement. This paper introduces a flexible user-defined domain decomposition framework, allowing users to customize domain partitioning based on application requirements. The impact of different decomposition strategies is evaluated across various applications concerning computation, communication, and I/O. Results demonstrate that while 1D partitioning yields superior I/O performance, k-nearest neighbors (KNN) clustering effectively reduces inter-process communication overhead. This study lays the groundwork for scalable partitioning in large-scale land surface simulations, enhancing next-generation Earth system modeling.

Wang, Dali [ORNL] (ORCID:0000000168065108)↗
DOE OSTI2025

Diverse magnetic phase diagram and anomalous Hall effect in antiferromagnetic LuMn 6⁢ Sn 6

The interactions between conduction electrons and magnetism can significantly enhance the Hall signal, a phenomenon known as the anomalous Hall effect (AHE). While the AHE is generally not expected in antiferromagnets, a large AHE is observed in certain antiferromagnets with noncollinear spin textures and nonvanishing Berry curvature. In this work, we present a rich temperature and magnetic phase diagram with eight distinct magnetic phases for the antiferromagnetic kagome compound LuMn 6 Sn 6 . The Hall effect analysis in LuMn 6 Sn 6 reveals both intriguing physical phenomena and methodological challenges. In the coplanar canted antiferromagnetic phase, we observe an AHE, which likely originates from the intrinsic effects. At low temperatures, upon entering the ferromagnetic phase, the AHE sharply increases and exceeds the conventional limits expected from intrinsic mechanisms. We also demonstrate the limitations of standard experimental methods in extracting the topological contribution to the Hall effect data. In particular, we show that accounting for magnetoresistance anisotropy helps reduce spurious contribution that can mimic topological Hall signals, although it does not fully resolve it. Furthermore, these shortcomings in current approaches in partitioning the Hall response necessitate new tools to interpret transport results in complex magnetic materials such as LuMn 6 Sn 6 .

Anomalous Hall effect↗
DOE OSTI2025

Optimal Control of Differentially Private EV Charging: A Scalable Learning Approach Under Uncertainty

Internet of Things (IoT)-enabled electric vehicles (IoEVs) enable intelligent charging coordination that accounts for grid congestion. However, increased data exchange raises privacy concerns, as charging patterns can reveal sensitive driver behavior to grid operators. Here, we propose a differentially private (DP) EV charging framework that enables coordinated control while protecting driver data with theoretical privacy guarantees. Nevertheless, integrating DP inevitably introduces uncertainty into the control strategy for EVs, which can lead to infeasible solutions. To tackle this challenge, we develop a feasible and scalable control algorithm based on constrained reinforcement learning (CRL) and convex hulls. While our framework is designed to handle the uncertainty introduced by DP, it is general and also applicable to other sources of uncertainty in EV charging, such as the stochastic nature of driver behavior and renewable variability. This ensures feasible and privacy-preserving coordination of EV charging at scale. Our method constructs convex hulls within the action space to guarantee feasibility under stochastic constraints and incorporates constraint reduction techniques to improve scalability. Case studies based on IEEE benchmark systems demonstrate that the proposed approach effectively balances feasibility under uncertainty, scalability, and privacy in large-scale EV charging control.

Engineering - Power transmission and distribution↗
DOE OSTI2026

Metrology for femtosecond pulsed x-ray heating in diamond anvil cell experiments at the European XFEL: Revisiting the iron phase diagram up to 150 GPa

The development of pulsed intense x-ray sources, such as free electron laser, offers new avenues for high pressure experiments. Here, we study the feasibility and metrology of x-ray heating in diamond anvil cells at the European x-ray free electron laser. This method enables one to volumetrically heat the sample while inhibiting chemical migration and probing the crystallographic structure of the sample throughout the heating with a high repetition rate. We focus our study on iron, whose phase diagram is well established up to 100 GPa, to explore the possibilities and limitations of this technique. We volumetrically heat iron samples at starting pressures ranging from 10 to 138 GPa, using the x-ray beam pulsed at 4.5 MHz in a serial pump-and-probe experimental design. Experimental challenges arise from temperature gradients within the sample, changes in temperature at the 100 ns timescale, the difficulty of direct temperature estimates, the effect of thermal pressure, and the presence of metastable crystallites due to rapid cycles of heating and cooling. Hence, we develop a multi-crystal-like data processing method that allows us to account for sample heterogeneity in probed conditions. We then calibrate our measurements using known physical properties of iron under pressure. Thermal pressure in our experiments increases from 4% of the isochoric prediction at 10 GPa to 23% at 138 GPa, and we show that our data are in agreement with most previous observations of iron in this pressure range. The method can now be implemented at higher pressures and temperatures and on materials with unknown phase diagrams.

Materials science↗
DOE OSTI2024

Evaluating the 238 U PFNS Including Chi-Nu Experimental Data

This report documents an evaluation of 238 U prompt fission neutron spectra (PFNS) which is a deliverable for a FY2024 Q4 NCSP (Nuclear Criticality Safety Program) milestone. This evaluation is new; its prior input is based on extended Los Alamos and exciton models implemented in the code CoH. Experimental covariances were estimated for five experimental data sets. One of these data sets that was measured by the Chi-Nu team of LANL and LLNL. It covers the 238 U PFNS for continuous incident-neutron energies of 1–20 MeV and outgoing-neutron energies from 10 keV– 10 MeV with high precision. Contrary to Chi-Nu data, previous data sets were measured in a limited energy range. The resulting evaluated data correspond well to the experimental PFNS taken into account for the evaluation. The evaluated PFNS also produce average mean energies in agreement with associated Chi-Nu data. If one uses the new evaluated data to predict the neutron multiplication factor, k eff , of the Flattop, Flattop-Pu and BigTen ICSBEP critical assemblies (which all have thick reflectors with high percentages of 238 U), the differences of simulated values compared to those using ENDF/B-VIII.1β3 is modest (less than 25 pcm). In addition to that, the new PFNS predict on average 238 U LLNL pulsed-sphere neutron-leakage spectra slightly better than ENDF/BVIIII.0 and ENDF/B-VIII.1β3 PFNS. The differences are, however, well within the experimental uncertainties.

238U↗
DOE OSTI2025

Development and Evaluation of a General Drag Model for Gas-Solid Flows via Deep Learning

This project presents the development and evaluation of a general drag model for gas–solid multiphase flows using deep learning techniques. A comprehensive database of more than 4,000 experimental and numerical data points for spherical and non spherical particles was compiled, incorporating geometric features such as sphericity, aspect ratio, and orientation. Several predictive approaches—including traditional em pirical correlations, machine learning, and deep neural networks—were benchmarked, with the proposed Drag Coefficient Correlation-aided Deep Neural Network (DCC DNN) demonstrating superior accuracy. To account for particle–particle interactions, additional drag data were generated using CFD-based simulations of packed and flu idized beds, leading to the development of a retrained model capable of incorporat ing volume fraction effects. Integration of the trained model with the MFiX CFD solver was achieved using FTorch, enabling drag predictions during discrete element method (DEM) simulations. Validation against experimental data for single particles and fluidized beds confirmed the model’s improved predictive ability, particularly for non-spherical geometries. While the model performed strongly under fluidized con ditions, limitations remained in unfluidized regimes, suggesting a need for expanded datasets. Overall, this study demonstrates the feasibility of combining deep learning with physics-informed CFD to improve drag modeling for gas–solid flows, with promis ing implications for scaling multiphase simulations in industrial applications.

42 ENGINEERING↗
DOE OSTI2024

Updated ASME design correlations and qualification plan for powder bed fusion 316H stainless steel

This report provides an update on the Advanced Materials and Manufacturing Technologies (AMMT) program effort to qualify Laser-Powder Bed Fusion (L-PBF) 316H stainless steel for use with the ASME Boiler & Pressure Vessel Code Section III, Division 5 rules. The report summarizes progress in testing and characterizing L-PBF material at elevated temperatures by providing preliminary design data for L-PBF 316H and by comparing the elevated temperature performance of the L-PBF material to wrought and conventional fusion welded 316H. The report then updates the initial AMMT qualification plan for L-PBF 316H, originally developed in 2023, to update the accelerated qualification strategy adopted in that plan to account for the new high temperature test data. The report also explores a few methods for further accelerating the qualification process using machine learning techniques to supplement the more conventional, empirical analysis methods typically used by ASME to correlate and extrapolate time-dependent material test data.

36 MATERIALS SCIENCE↗
DOE OSTI2026

2002 Champaign-Urbana Urbanized Area Transportation Study

This travel survey, conducted by Champaign County Regional Planning Commission, collected daily weekday travel data from 395 urbanized-area households—accounting for every individual 12 years old or older—in the Champaign-Urbana-Savoy region. The information gathered from the survey was used to forecast future transportation demand and update the Champaign Urbana Urbanized Area Transportation Study Long Range Transportation Plan 2035. Of the 395 households recruited for the study, 362 completed their travel logs. The household travel survey was completed during the summer and fall of 2002, and it was the first large-scale attempt at collecting single-weekday travel data in Champaign, Urbana, and Savoy.

1Hz data↗
DOE OSTI2025

Ultrasonic characterization of material heterogeneities in stainless steel components produced by laser powder bed fusion

We introduce pulse-echo ultrasound as a method for characterizing the impact of powder bed fusion parameters on the properties of additively manufactured stainless-steel components, their material anisotropy, and location-dependent heterogeneity. Our results indicate that accurate characterization requires careful selection of ultrasonic propagation paths, which must consider the direction of additive layering, variations in processing parameters, and the component's geometry. We employed two distinct methods to estimate material properties from ultrasonic data: One assumes isotropy, while the other accounts for anisotropic interactions during the propagation of elastic waves. When applied to samples fabricated with laser energy densities ranging from 24 to 42 J/mm³ , these methods revealed transverse isotropy and weak anisotropy (quantified by small Thomsen parameters, ε = 0.0651 and γ = 0.0092) and less than a ∼6 % change in acoustic impedance. The assumption of isotropy, in this case, leads to small errors (less than 4 % or 1 % for Young's modulus in the build or transverse directions) when estimating orthotropic material properties using ultrasonic data measured along just two orthogonal directions, one of which must align with the build direction. By comparing ultrasonic measurements — which aggregate the spatial variability in material properties along the length of elastic wave propagation into a single value — with localized measurements obtained from surface nanoindentation, we uncovered and spatially profiled significant differences between the surface and interior properties. Specifically, the surface Young's modulus decreased from approximately 210 GPa to 180 GPa within a depth of about 3 mm. We attribute this surface-localized heterogeneity in PBF-fabricated components to distinct thermal histories experienced by the surface and interior regions. Collectively, the results of this study establish a framework for the ultrasonic characterization of material heterogeneity and anisotropy in material properties and demonstrate its application in additively manufactured metal components.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Cosmological parameter estimation with a joint-likelihood analysis of the cosmic microwave background and big bang nucleosynthesis

Here, we present a joint-likelihood analysis of big bang nucleosynthesis (BBN) and cosmic microwave background (CMB) data, consistently combining likelihoods and taking into account uncertainties in nuclear reaction rates for the first time. Bayesian inference is performed on the baryon abundance and the effective number of neutrino species, 𝑁 eff , using a CMB Boltzmann solver in combination with LINX , a new flexible and efficient BBN code. We marginalize over Planck nuisance parameters and nuclear rates to find 𝑁 eff =3.0⁢8$^{+0.15}_{−0.14}$, 2.9⁢4$^{+0.16}_{−0.15}$, or 2.96$^{+0.13}_{−0.14}$, for three separate reaction networks. This framework enables robust testing of the lambda cold dark matter paradigm and its variants with CMB and BBN data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

The Need for a New LLNL Pulsed Sphere Neutron Leakage Spectra Series

Here, it is shown that spectra measured as part of the Lawrence Livermore National Laboratory Pulsed Sphere (LPS) program offer decisive information to locate formatting or physics issues in nuclear data of key interest for fusion reactor simulations. However, experiments from this measurement series are not benchmarks. For instance, their uncertainties are incomplete. There are also many open questions—e.g., on the setup, the detector response, and whether LPS are accurately modeled—that cannot be answered anymore given the limited documentation and that many of the experimenters are no longer actively working. This limited knowledge has implications when one tries to adjust nuclear data to LPS spectra. Usually, one adjusts to benchmarks representing an application with the hope to get more precise nuclear data for the application of interest where differential data might be scarce and/or to reduce nuclear data uncertainties in the application simulations. However, it is demonstrated that adjustment with LPS spectra without accounting for missing uncertainties and modeling potential biases in the experimental data leads to adjusted data that are highly unphysical. That means adjusted data differ significantly from evaluated data based on information from differential experiments; also, application quantities predicted with the adjusted data deviate distinctly from experimental ones. While we can approximate our limited knowledge on these experiments with Gaussian processes in the adjustment process, this modeling of bias is arbitrary rather than based on a physics explanation, calling into doubt the validity of resulting adjusted data. Thus, we discuss here the need for a new measurement series, learning from the strengths and weaknesses of the LPS program, to yield decisive and well-benchmarked integral experiments to support fusion reactor research.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Convergence in simulating global soil organic carbon by structurally different models after data assimilation

Abstract Current biogeochemical models produce carbon–climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first‐order or Michaelis–Menten kinetics at the global scale. Nevertheless, a wider range of data with high‐quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics‐function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Cosmological constraints from a joint DESI DR1 Full-Shape and DR2 BAO

We present a cosmological analysis combining full-shape (FS) clustering measurements from the Dark Energy Spectroscopic Instrument (DESI) DR1 with baryon acoustic oscillation (BAO) measurements from DESI DR2. To achieve a robust combination that accounts for the correlation between the two data releases, we employ the ShapeFit compression method and estimate the joint covariance using EZmocks. This compressed approach inherently mitigates the prior volume effects that have previously dominated Bayesian constraints from DESI data with minimal external priors. Consequently, we obtain — for the first time within a Bayesian framework — reliable DESI-only constraints on extensions to ΛCDM using only a Big Bang Nucleosynthesis prior on the baryon density and a wide prior on the spectral index. In flat ΛCDM, we find Ω m = 0.3035 ± 0.0085, h = 0.6876 ± 0.0059, and σ 8 = 0.822 ± 0.034. For the w 0 w a CDM dynamical dark energy model, we measure w 0 = -0.49 ± 0.25 and w a = -1.52 ± 0.77, improving constraints by ∼ 30% relative to the analogous DR1 measurement and reducing the discrepancy with ΛCDM to 1.4σ when compared to BAO only analyses. We also report competitive limits on the sum of neutrino masses and spatial curvature. This work demonstrates that the ShapeFit compression provides a prior-robust and computationally efficient pathway to constrain beyond-ΛCDM physics with large-scale structure.

baryon acoustic oscillations↗
DOE OSTI2025

Accounting for electron-beam-induced warping of molecular nanocrystals in MicroED structure determination

High-energy electrons induce sample damage and motion at the nanoscale to fundamentally limit the determination of molecular structures by electron diffraction. Using a fast event-based electron counting (EBEC) detector, we characterize beam-induced, dynamic, molecular crystal lattice reorientations (BIRs). These changes are sufficiently large to bring reciprocal lattice points entirely in or out of intersection with the sphere of reflection, occur as early events in the decay of diffracted signal due to radiolytic damage, and coincide with beam-induced migrations of crystal bend contours within the same fluence regime and at the same illuminated location on a crystal. These effects are observed in crystals of biotin, a series of amino acid metal chelates, and a six-residue peptide, suggesting that incident electrons inevitably warp molecular lattices. The precise orientation changes experienced by a given microcrystal are unpredictable but are measurable by indexing individual diffraction patterns during beam-induced decay. Reorientations can often tilt a crystal lattice several degrees away from its initial position before irradiation, and for an especially beam-sensitive Zn(II)-methionine chelate, are associated with dramatic crystal quakes prior to 1 e − Å −2 electron beam fluence accumulates. Since BIR coincides with the early stages of beam-induced damage, it echoes the beam-induced motion observed in single-particle cryoEM. As with motion correction for cryoEM imaging experiments, accounting for BIR-induced errors during data processing could improve the accuracy of MicroED data.

Vlahakis, Niko (ORCID:0000000250920265)↗