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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

Relativistic gas accretion onto supermassive black hole binaries from inspiral through merger

Accreting supermassive black hole binaries are powerful multimessenger sources emitting both gravitational and electromagnetic (EM) radiation. Understanding the accretion dynamics of these systems and predicting their distinctive EM signals is crucial to informing and guiding upcoming efforts aimed at detecting gravitational waves produced by these binaries. To this end, accurate numerical modeling is required to describe both the spacetime and the magnetized gas around the black holes. In this paper, we present two key advances in this field of research. First, we have developed a novel 3D general relativistic magnetohydrodynamics (GRMHD) framework that combines multiple numerical codes to simulate the inspiral and merger of supermassive black hole binaries starting from realistic initial data and running all the way through merger. Throughout the evolution, we adopt a simple but functional prescription to account for gas cooling through photon emission. Next, we have applied our new computational method to follow the time evolution of a circular, equal-mass, nonspinning black hole binary for ∼200 orbits, starting from a separation of 20⁢𝑟 𝑔 and reaching the postmerger evolutionary stage of the system. We have shown how mass continues to flow toward the binary even after the binary “decouples” from its surrounding disk, but the accretion rate onto the black holes diminishes. We have identified how the minidisks orbiting each black hole are slowly drained and eventually dissolve as the binary compresses. We confirm previous findings that the system’s luminosity decreases by a factor of a few during inspiral; however, we observe an abrupt increase by ∼50% in this quantity at the time of merger, likely accompanied by an equally abrupt change in spectrum. Lastly, we have demonstrated that during the inspiral, fluid ram pressure regulates the fraction of the magnetic flux transported to the binary that attaches to the black holes’ horizons.

Accretion disk & black-hole plasma↗
DOE OSTI2025

Impacts of PV Module Connector Failures on Cost and Performance of Utility Scale Photovoltaic Systems

The reliability, cost and performance of electrical connectors are a concern in all types of electrical systems, and demands on connectors used on photovoltaic (PV) systems include that connectors maintain electrical conductivity and physical strength, endure ultraviolet sunlight and high ambient temperature, and resist moisture and chemical intrusion over a very long (>25 year) performance period. Connector failures increase operation and maintenance (O&M) costs and reduce plant production, but connector failure can also cause safety and liability problems, which are of greater concern. This work results from a three-year collaboration between Sandia National Laboratories (SNL), the Electric Power Research Institute (EPRI), and the National Renewable Energy Laboratory (NREL) and funded by the U.S. Department of Energy (DOE) Solar Energy Technology Office (SETO) under Agreements #39035 and #38531 "Connector Reliability Across the US Solar Sector." a multi-pronged investigation of PV connector health across the US (see https://energy.sandia.gov/pvconnectors/). This report presents derivation of a Techno-Economic Analysis (TEA) that models failure modes and frequencies (how often failure occurs), estimates O&M costs and lost production associated with connector failures, and then calculates the effect that PV module connectors can have on Levelized Cost of Energy (LCOE). The model is informed with initial data from quantitative assessment of failure rates, root causes and mechanisms, in-situ diagnostics and data collection, lab-based forensics, and interviews with PV connector manufacturers and plant operators. SNL conducted site inspections at multiple utility-scale sites in different climates and subjected field samples of new, used, and degraded connectors to visual and electrical characterization. EPRI conducted metallurgical analysis of the pin and sleeve conductors to study failure-induced morphological and compositional changes. There is in general a shortage of statistically valid data, but data from PVROM database maintained by SNL was sufficient to ascertain failure rates and lost production as well as provide qualitative insight in its curated maintenance records. This report details the structure of the mathematical model but the sources of data to inform the model will continue to evolve. Analysis of a 100 MW PV plant is provided as an example of the use of the model, with results indicating that connectors are responsible for Annualized O&M Costs of $\$$71,933/year; Annualized Unit O&M Costs of $\$$0.72/kW/year; that a Reserve Account of $\$$187,220 should be available to fund repairs related to connectors; that connectors add $\$$1,494,004 to the Net Present Value of the O&M Costs (project life); and that O&M related to connectors adds about $\$$0.00088/kWh to the Levelized Cost of Energy. The impact of this model is to provide a tool to make the US solar sector more robust by quantifying and monetizing the reliability risks to utility-scale PV systems posed by poorly installed, mismatched and/or poorly designed and manufactured connectors. The TEA provides a model incorporating failure statistics, O&M cost data, and lost production into a single figure of merit, informing decisions and enabling practitioners to optimize cost and performance trade-offs. Stakeholders include connector manufacturers, system designers and equipment specifiers, standards bodies, installers and O&M providers, investors and insurance underwriters. This report supports continued growth of PV predicated on assurances that properly installed and maintained PV system connectors are safe and reliable. The project team is proposing future work including accelerated testing of connectors and expanding the approach taken here to other PV system components, such as TEA for rapid shut-down devices.

14 SOLAR ENERGY↗
DOE OSTI2026

SBND Detector Status and First Results

The Short-Baseline Near Detector (SBND) is one of three liquid argon time projection chamber (LArTPC) neutrino detectors positioned along the axis of the Booster Neutrino Beam (BNB) at Fermilab, and serves as the near detector in the Short-Baseline Neutrino (SBN) Program. The detector just completed its second year of running, collecting over 6.5e20 POT, equivalent to an unprecedented sample of over 5 million neutrino interactions. Initial data has demonstrated superb performance of the detector’s subsystems, enabling precise tracking and calorimetric reconstruction of events. With this large statistical data set, SBND is performing multi-dimensional cross section measurements of inclusive and exclusive topologies and precise searches for beyond the Standard Model (BSM) processes. As the near detector in the SBN Program, it will enable the full potential of the joint sterile neutrino measurements program by precisely characterizing the unoscillated neutrino beam, constraining BNB flux and neutrino-argon cross-section systematic uncertainties. In this talk, the current status of the experiment, first results from SBND’s neutrino interaction program, as well as status and prospects for BSM and oscillation searches are discussed.

Paton, Josephine [Fermilab] (ORCID:000000032651489↗
DOE OSTI2026

Quantum fate of the Choptuik naked singularity

Classical critical collapse provides a dynamical route from smooth initial data to a naked singularity, representing a sharper violation of predictability than ordinary black hole singularities. We argue that this distinction is erased by quantum backreaction. Building on the semiclassical interior analysis, where quantum self-energy of the collapsing matter generates a universal growing mode and a finite mass gap, we study the exterior naked singularity region that determines global visibility in the Einstein-scalar system. We analyze controlled exterior models in both $2+1$ and $3+1$ dimensions. In the former, smooth matching and physical boundary conditions analytically select a vacuum polarization state, whose backreaction cloaks the classically naked region by a quantum trapped branch. In the latter, numerical horizon tracing shows that near a quantum-shifted threshold the exterior develops finite-mass marginally trapped surfaces rather than a zero-mass naked endpoint. These results suggest a global quantum picture in which the Choptuik naked singularity shares the fate of an ordinary black hole singularity: quantum effects push the putative Cauchy horizon behind a quantum-generated horizon, thereby reducing the loss of predictability to the standard black hole evaporation problem.

FOS: Physical sciences↗
DOE OSTI2024

EVALS: Enhanced VALidation of Advanced Battery Supply Chains

This is an overview of EVALS (Enhanced VALidation of advanced battery Supply chains), funded by the U.S. Department of Energy Vehicle Technologies Office (VTO) and some initial data from a transformational laboratory directed research and development (LDRD) program project funded at NREL. EVALS aims to accelerate the process to bring domestic and allied primary sources of battery materials online, from production to deployment.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Public Data Set: Erratum: “Initial Characterization of Electron Temperature and Density Profiles in PEGASUS Spherical Tokamak Discharges Driven Solely by Local Helicity Injection” [Phys. Plasmas 28, 102504 (2021)]

This public data set contains openly-documented, machine readable digital research data corresponding to figures published in G.M. Bodner et al., ‘Erratum: “Initial Characterization of Electron Temperature and Density Profiles in PEGASUS Spherical Tokamak Discharges Driven Solely by Local Helicity Injection” [Phys. Plasmas 28, 102504 (2021)],’ Physics of Plasmas 31, 129904 (2024).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Impact of recent ENDF nuclear data update, high initial enrichment and high burnup fuel on critical experiments applicability determination via the integral index c k for burnup credit validation

In 2012, NUREG/CR-7109 reported on the validation of burnup credit calculations involving major and minor actinides and major fission products which was investigated for pressurized and boiling water reactor (PWR and BWR) fuel enrichments up to 5 wt% 235 U and assembly-average burnups up to 60 GWd/MTU. Recently, there has been interest in increasing the maximum enrichment used in PWR fuel as high as 8 wt% 235 U and correspondingly increasing the maximum assembly-average burnups to approximately 75 GWd/MTU. These proposed increases in enrichment and burnup necessitate reinvestigation of the validation basis for k eff calculations for this expanded application space. Additionally, the 2012 study was performed by using the Evaluated Nuclear Data File (ENDF)/B-VII.0 nuclear data with the SCALE 6 covariance library, and the effects of using the newly released ENDF/B-VII.1 and ENDF/B-VIII.0 nuclear data and covariance libraries should be evaluated. In this work, published in NUREG/CR-7309 in 2025, the validation assessment was performed consistently with NUREG/CR-7109: modeling irradiated fuel assemblies in the Generic Burnup Credit (GBC)-32 cask defined in NUREG/CR-6747. The TSUNAMI-3D sequence was used to generate sensitivity data for the application model, and the data were compared with sensitivity data from select benchmark models. The integral parameter c k is the metric of similarity used in this study and is consistent with NUREG/CR-7109, where a c k value in excess of 0.8 indicates sufficient similarity for use in validation. A new set of benchmark experiments with sensitivity data has been assembled for this effort. The number of experiments with available sensitivity data is now 2,104, compared to 474 in NUREG/CR-7109. This increase was facilitated by the efforts of the Nuclear Energy Agency to generate sensitivity data for a majority of the experiments in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook to supplement the data available in the Oak Ridge National Laboratory (ORNL) Verified, Archived Library of Inputs and Data (VALID). The complete set of benchmarks considered here includes experiments for low-enriched uranium (LEU), intermediate enriched uranium (IEU), and a mixture of uranium and plutonium (MIX) from the ICSBEP Handbook and VALID, as well as ORNL models of the Haut Taux de Combustion (HTC) experiments and other potentially relevant models not included in VALID. The updated similarity study shows that none of the extended burnup and higher enrichment combinations considered show a significant decrease in the number of potentially applicable experiments, meaning sufficient critical experiments exist for the validation of BUC criticality safety calculations, with initial enrichments up to 8 wt% 235 U and burnups up to 80 GWd/MTU. Additionally, both the ENDF/B-VII.1 and ENDF/B-VIII.0 nuclear data libraries can be used for validation since the number of critical experiments applicable for validation increases for most cases with the most recent nuclear data compared to the previous one. As in previous BUC validation studies, the French HTC experiments are the most similar in a majority of the application cases studied, especially from representative discharge burnups ranging from 40 to 80 GWd/MTU. In conclusion, these results match the conclusions presented in NUREG/CR-7109 regarding validation of the primary actinides in BUC analyses.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Newton-Raphson AC Power Flow Convergence Based on Deep Learning Initialization and Homotopy Continuation

Power flow forms the basis of many power system studies. With the increased penetration of renewable energy, grid planners tend to perform multiple power flow simulations under various operating conditions and not just selected snapshots at peak or light load conditions. Getting a converged AC power flow (ACPF) case remains a significant challenge for grid planners especially in large power grid networks. This paper proposes a two-stage approach to improve Newton-Raphson ACPF convergence and was applied to a 6102 bus Electric Reliability Council of Texas (ERCOT) system. The first stage utilizes a deep learning-based initializer with data re-training. Here a deep neural network (DNN) initializer is developed to provide better initial voltage magnitude and angle guesses to aid in power flow convergence. This is because Newton-Raphson ACPF is quite sensitive to the initial conditions and bad initialization could lead to divergence. The DNN initializer includes a data re-training framework that improves the initializer's performance when faced with limited training data. The DNN initializer successfully solved 3,285 cases out of 3,899 non-converging dispatch and performed better than random forest and DC power flow initialization methods. ACPF cases not solved in this first stage are then passed through a hot-starting algorithm based on homotopy continuation with switched shunt control. The hot-starting algorithm successfully converged 416 cases out of the remaining 614 non-converging ACPF dispatch. In conclusion, the combined two-stage approach achieved a 94.9% success rate, by converging a total of 3,701 cases out of the initial 3,899 unsolved cases.

Deep learning↗
DOE OSTI2025

Influence of initial conditions on data-driven model identification and information entropy for ideal mhd problems

Data-driven methods of model identification are able to discern governing dynamics of a system from data. Such methods are well suited to help us learn about systems with unpredictable evolution or systems with ambiguous governing dynamics given our current understanding. Many plasma problems of interest fall into these categories as there are a wide range of models that exist, however each model is only useful in a certain regime and often limited by computational complexity. To ensure data-driven methods align with theory, they must be consistent and predictable when acting on data whose governing dynamics are known. Weak Sparse Identification of Nonlinear Dynamics (WSINDy) is a recently developed data-driven method that has shown promise in learning governing dynamics from data with high noise levels [1]. This work examines how WSINDy acts on ideal MHD test problems as the initial conditions are varied and specifies limiting requirements for successful equation identification. Furthermore, it is hard to recover the governing dynamics from data that emphasize a single dominant behavior. In these low information cases, Shannon information entropy is able to pick up on the redundancies in the data that affect recoverability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

ATLAS HL-LHC Demonstrators with Data Carousel: Dataon-Demand and Tape Smart Writing

The High Luminosity upgrade to the LHC (HL-LHC) is expected to deliver scientific data at the multi-exabyte scale. To tackle this unprecedented data storage challenge, the ATLAS experiment initiated the Data Carousel project in 2018. Data Carousel is a tape-driven workflow in which bulk production campaigns with input data resident on tape are executed by staging and promptly processing a sliding window to disk buffer such that only a small fraction of inputs are pinned on disk at any one time. Put in ATLAS production before Run3, Data Carousel continues to be our focus for seeking new opportunities in disk space savings, and enhancing tape usage throughout the ATLAS Distributed Computing (ADC) environment. These efforts are highlighted by two recent ATLAS HL-LHC demonstrator projects: data-on-demand and tape smart writing. In this paper, we will discuss the recent studies and outcomes from these projects. The research was conducted together with site experts at CERN and Tier-1 centers.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

ARM Trajectories Data Set Value-Added Product Report

The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s ARM Trajectories Data Set (ARMTRAJ) Value-Added Product (VAP) provides trajectory data sets initialized at ARM deployment coordinates and configured using ARM data sets. The four trajectory data sets support aerosol, cloud, and planetary boundary-layer research. Trajectory calculations use the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model informed by the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation atmospheric reanalysis (ERA5) data set at its highest spatial resolution (~31 km). HYSPLIT also runs at multiple initial starting locations surrounding ARM deployments (in latitude/longitude and/or vertical coordinates), facilitating an ensemble for each sample in the data sets. The ensemble mean and variability reported in ARMTRAJ improve the fidelity and provide uncertainty estimates of trajectory coordinates, thermodynamic properties, and other output fields.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

ARM Trajectories Data Set Value-Added Product Report

The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s ARM Trajectories Data Set (ARMTRAJ) Value-Added Product (VAP) provides trajectory data sets initialized at ARM deployment coordinates and configured using ARM data sets. The six trajectory data sets support aerosol, cloud, planetary boundary layer, and related research (aerosol-cloud interactions, etc.), as well as studies using ARM Aerial Facility (AAF) and tethered balloon system (TBS) measurements. Trajectory calculations use the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model informed by the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation atmospheric reanalysis (ERA5) data set at its highest spatial resolution (~31 km). HYSPLIT also runs at multiple initial starting locations surrounding ARM deployments (in latitude/longitude and/or vertical coordinates), facilitating an ensemble for each sample in the data sets. The ensemble mean and variability reported in ARMTRAJ improve the fidelity and provide uncertainty estimates of trajectory coordinates, thermodynamic properties, and other output fields.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

SITCOMTN-149: An Interim Report on the LSSTComCam On-Sky Campaign

From 24 October to 11 December 2024, the NSF-DOE Vera C. Rubin Observatory conducted an on-sky campaign using the engineering LSST Commissioning Camera (LSSTComCam) to test the end-to-end functionality of hardware and software, as well as operational procedures. This interim report provides a preliminary technical overview of our understanding of the integrated system performance based tests and analyses conducted during the LSSTComCam on-sky campaign. The objectives are to synthesize what we have learned about the system in a timely way to inform on-going commissioning efforts, and to inform the Rubin science community on the progress of the LSSTComCam on-sky campaign. The report is organized into sections to describe major activities during the campaign, as well as multiple aspects of the demonstrated system and science performance. All of the results presented here are to be understood as work in progress using engineering data and the initial versions of the data processing pipelines; the report is a living document that will be updated as analyses are refined.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Examples of Mission-driven Data Science from Jefferson Lab and ACES

This presentation details mission-driven data science initiatives at Jefferson Lab and the Joint Institute for Advanced Computing on Environmental Studies (ACES). JLab, a U.S. Department of Energy Office of Science national laboratory, operates the Continuous Electron Beam Accelerator Facility (CEBAF), and is the lead institute for the new High Performance Data Facility (HPDF) Hub. The Joint Institute for ACES brings together interdisciplinary teams in health informatics, climate modeling, computer science, and physics to address environmental challenges, including flood modeling. The Hampton Roads region, particularly Norfolk and Virginia Beach, faces increasing flood risks, motivating the need for rapid, reliable, and risk-aware decision support. ACES’s flooding work has a focus on uncertainty quantification (UQ) and machine learning (ML) for coastal flood management. The work is motivated by the increasing vulnerability of communities such as Norfolk and Virginia Beach, Virginia, to frequent coastal flooding events, and the need for rapid, reliable decision support. The research develops computationally efficient ML surrogate models to forecast water levels and flooding risk. A central theme is the quantification and calibration of predictive uncertainty, especially for out-of-distribution (OOD) scenarios, using techniques such as Monte Carlo Dropout, Deep Ensembles, Gaussian Processes, and Deep Quantile Regression (DQR). The study demonstrates that distance-aware UQ is critical for reliable scientific AI, particularly in high-dimensional, safety-critical, and real-time applications.

McSpadden, Diana [Thomas Jefferson National Accele↗
DOE OSTI2025

Lattice structure and dynamics of sparse molecular crystals: OsO4 and RuO4

This dataset contains input and output files from DFT simulations used to reproduce the electronic and phonon calculations of bulk and molecular OsO₄ and RuO₄. The files include data from initial electronic structure and phonon calculations performed using different functionals: PBE, PBEsol, and vdW-DF-optB86b. The computed phonon frequencies are compared with Raman crystal and gas-phase frequencies from existing literature, while the calculated phonon density of states (PhDOS) is compared with experimental PhDOS data.

36 MATERIALS SCIENCE↗