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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

OpenCRUMS USA: An Open Machine Learning Framework for Characterizing Variability in Aerosol Reanalysis Data

Advances in artificial intelligence (AI) have called for exploring how these techniques can be used for exploring patterns in large climate datasets. To that regard, the U.S. Department of Energy AI for Earth System Predictability (AI4ESP) supported a pilot initiative called the Open Classification of Regimes in the Southeast USA (OpenCRUMS USA) project to explore how AI can be used to characterize modes of spatial variability in large climate datasets. For this study, we focus on comparing two methods for characterizing the modes of spatial variability of surface aerosol concentration over the Houston region: empirical orthogonal functions (EOFs) and layerwise relevance propagation (LRP) applied to a convolutional neural network (CNN) classifier. We show that EOF analysis typically attributes spatial variability modes that span all of southeast Texas, prohibiting the attribution of spatial variability to localized regions. However, using LRP on the CNN classifier resolves the explanatory parameters at a finer spatial resolution than EOFs. This allows for the attribution of the spatial variability of surface aerosols to local regions of organic carbon which was not possible using EOFs. In addition, the LRP analysis also suggests that synoptic-scale transport of dust is most prevalent during anticyclonic and pretrough synoptic conditions as categorized by self-organizing maps.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Lake-Effect Snowstorm Events and Associated Snowfall Totals Integrated from NOAA Storm Reports, ERA5, and HRRR for the Laurentian Great Lakes (1997–2024)

Lake-effect snowstorms are localized, impactful winter weather phenomena that can generate substantial snowfall totals and pose significant challenges for forecasting, transportation, and regional infrastructure. To support the analysis and modeling of these events, this dataset compiles observational reports of lake-effect snowstorms alongside corresponding snowfall estimates derived from gridded atmospheric datasets. The observational component of the data originates from the National Weather Service (NWS) winter storm report, subset to lake-effect snow event type, covering 1997–2024. For each lake-effect snow event, this data provides the impacted county, event start and end datetimes at an hourly resolution, as well as relevant storm narratives. The complementary reanalysis-derived data is sourced from European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and High-Resolution Rapid Refresh (HRRR) gridded data. For both gridded datasets, the maximum total snowfall (in units mm) was extracted, constrained by the county and datetimes specified by the observational report. ERA5 data covers the entire observational period (1997–2024), whereas HRRR data is only available from November 2016 – December 2024. Three CSV files are provided here: (1) the observational lake-effect snow event report, (2) ERA5 maximum snowfall detections for each event, and (3) HRRR maximum snowfall detections for each event. Relevant data from the observational files, such as impacted state and county, event datetimes, and event IDs, were included for convenience. Users can inspect and visualize the data using tools such as Microsoft Excel and Python pandas/matplotlib packages. This dataset may support a variety of applications, including climatological analyses of lake-effect snowfall, evaluation of snowfall representation in atmospheric datasets and numerical weather prediction models, and the development of machine learning approaches for detecting or predicting lake-effect snowfall events.

EARTH SCIENCE > ATMOSPHERE > PRECIPITATION > SOLID↗
DOE OSTI2020

A Comprehensive System of Energy Intensity Indicators for the U.S.: Methods, Data and Key Trends

This report describes a comprehensive system of energy intensity indicators for the United States that has been developed for the Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) over the past decade. This system of indicators is hierarchical in nature, beginning with detailed indexes of energy intensity for various sectors of the economy, which are ultimately aggregated to an overall energy intensity index for the economy as a whole. The aggregation of energy intensity indexes to higher levels in the hierarchy is performed with a version of the Log Mean Divisia index (LMDI) method. Based upon the data and methods in the system of indicators, the economy-wide energy intensity index shows a decline of about 16% in 2014 relative to a 1985 base year. Discussion of energy intensity indicators for each of the broad end-use sectors of the economy—residential, commercial, industrial, and transportation—is presented in the report. An analysis of recent changes in the efficiency of electricity generation in the U.S. is also included. A detailed appendix describes the data sources and methodology behind the energy intensity indicators for each sector.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

A Comprehensive System of Energy Intensity Indicators for the U.S.: Methods, Data and Key Trends

This report describes a comprehensive system of energy intensity indicators for the United States that has been developed for the Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) over the past decade. This system of indicators is hierarchical in nature, beginning with detailed indexes of energy intensity for various sectors of the economy, which are ultimately aggregated to an overall energy intensity index for the economy as a whole. The aggregation of energy intensity indexes to higher levels in the hierarchy is performed with a version of the Log Mean Divisia index (LMDI) method. Based upon the data and methods in the system of indicators, the economy-wide energy intensity index shows a decline of about 16% in 2014 relative to a 1985 base year. Discussion of energy intensity indicators for each of the broad end-use sectors of the economy—residential, commercial, industrial, and transportation—is presented in the report. An analysis of recent changes in the efficiency of electricity generation in the U.S. is also included. A detailed appendix describes the data sources and methodology behind the energy intensity indicators for each sector.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Liquid Hydrogen Heavy-Duty Vehicle Safety Review and Refueling Facility Design

Liquid hydrogen (LH 2 ) used as a fuel onboard a heavy-duty vehicle can result in increased storage capacity and faster refueling relative to compressed gas. However, there are concerns about hydrogen losses from boil-off, potential safety issues, gaps in codes and standards for cryogenic hydrogen fuel, and technical challenges with LH 2 systems for widespread transportation applications. A failure modes and effects analysis (FMEA), a safety codes and standards review, and a design review of the onboard liquid hydrogen system for a heavy-duty vehicle identified some of these potential safety issues and gaps in the codes and standards. The FMEA identified some medium and low risk failure points of the conceptual design, and the design review identified how carefully pressure relief needs to be considered for LH 2 systems. In addition, a conceptual design for a LH 2 refueling station was developed. Rough capital costs for the refueling station design were $\$1 million$ and the layout occupied approximately 13,000 ft 2 . These results can be used to inform future designs and analyses for LH 2 heavy-duty vehicles.

08 HYDROGEN↗
DOE OSTI2024

Travel Patterns and Characteristics of Low-Income Population in New York State: 2017 Update

This study examines the key characteristic of low-income people, focusing on New York State populations and households and their comparison with the rest of the United States. The characteristics includes their demographics, trip activities, accessibility, travel attitudes, and equity. The major data source used is 2017 National Households Travel Survey (NHTS). Supplemental data sources are also used such as American Community Survey and Census Transportation Planning Products for a more comprehensive analysis.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

MetaHeuristic Feature Selection for Energy Group Optimization and Analysis

Energy discretization is a crucial component of deterministic neutron transport simulations. Metaheuristic (MH) optimizers are effective algorithms to determine group structures that maximize both solution accuracy and computational efficiency. This project establishes a framework for optimizing group structures for PARTISN simulations using the Python library MEALPY. Group structure optimization is formulated as a binary feature selection problem, and results are investigated with permutation and material importance techniques to determine physically relevant energy bounds. We conclude that MH optimizers find group structures that drastically improve flux calculations while preserving k-effective accuracy. Further, we find that individual energy bounds are not necessarily physically relevant, but rather specific energy ranges are.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

SAM Code Enhancements for Fission Product Tracking of Noble Gases and Metals in MSRs

This report documents fiscal year 2026 enhancements to the System Analysis Module (SAM) for modeling fission product transport in liquid-fueled molten salt reactors (MSRs). The work advances three principal areas: noble gas transport, noble metal deposition, and user interface improvements. The noble gas transport capability integrates drift-flux gas transport, Henry’s law two-film interphase mass transfer with pressure-based nucleation suppression, Knudsen-regime pore diffusion into porous graphite with a conjugate salt-graphite interface constraint, built-in material properties, five Sherwood-number mass transfer correlations including three derived from high-fidelity NekRS simulations, and xenon-135 reactivity feedback through SAM’s point-kinetics model. This work also presents a comprehensive verification test suite, including new analytically verified cases for pressure-dependent onset of interphase gas transfer in a stagnant vertical pipe, a postulated FLiBe-graphite Xe extraction permeator, a gravity riser with a fission-product source, and a descending pipe with gas redissolution driven by hydrostatic pressure. A machine learning framework for bubble rise velocity prediction in molten salt systems is developed and benchmarked on molten-salt and diverse aqueous bubble datasets. The best-performing fine-tuned transfer-learning networks achieve an 82% reduction in RMSE relative to the Clift correlation, and is implemented directly in SAM. The noble metal transport capability is developed, including a liquid-wall deposition model and a gas-surface flotation mechanism that transfers insoluble particles entrained by sparging gas to wetted structures. Verification tests and demonstration cases cover the surface deposition, flotation efflux, and flotation shedding. Finally, a new [SpeciesTransport] input structure replaces positional global vectors with selfcontained, order-independent, named species blocks, simplifies the specification of multiphase species and decay chains, and remains fully compatible with existing SAM input files. Together, these developments improve the physical fidelity, verification basis, and usability of SAM for system-level analyses of fissionproduct behavior in MSRs.

Mui, Travis (ORCID:0000000303736470)↗
DOE OSTI2023

Engineering-Scale Integrated Energy System Data Projection Demonstration via the Dynamic Energy Transport and Integration Laboratory

The objective of this study is to demonstrate and validate the Dynamic Energy Transport and Integration Laboratory (DETAIL) preliminary scaling analysis using Modelica language system-code Dymola. The DETAIL preliminary scaling analysis includes a multisystem integral scaling package between thermal-storage and hydrogen-electrolysis systems. To construct the system of scaled equations, dynamical system scaling (DSS) was applied to all governing laws and closure relations associated with the selected integral system. The existing Dymola thermal-energy distribution system (TEDS) facility and high-temperature steam electrolysis (HTSE) facility models in the Idaho National Laboratory HYBRID repository were used to simulate a test case and a corresponding scaled case for integrated system HYBRID demonstration and validation. The DSS projected data based on the test-case simulations and determined scaling ratios were generated and compared with scaled case simulations. The preliminary scaling analysis performance was evaluated, and scaling distortions were investigated based on data magnitude, sequence, and similarity. The results indicated a necessity to change the normalization method for thermal storage generating optimal operating conditions of 261 kW power and mass flow rate of 6.42 kg/s and the possibility of reselecting governing laws for hydrogen electrolysis to improve scaling predictive properties. To enhance system-scaling similarity for TEDS and HTSE, the requirement for scaling validation via physical-facility demonstration was identified.

08 HYDROGEN↗
DOE OSTI2023

TRACER-Tethersonde VOC data

One objective of TRACER-Tethersonde campaign was to deploy a volatile organic compound (VOC) sampler on the Tethered Balloon System (TBS) to create vertical profiles and characterize VOC composition. Volatile organic compounds (VOCs) are highly reactive precursor species that undergo atmospheric processing to form secondary products, including aerosol. VOC emissions are influenced by many factors that include but are not limited to temperature, time of day, local anthropogenic activities, and local vegetation, which result in large spatiotemporal variability. The vertical distribution of volatile species is crucial for understanding gas-phase processing for particle production but challenging to accomplish using currently available sampling devices. The standalone sampler built during the campaign enabled the execution of robust experimental designs, including sample collection using resin tubes at multiple altitudes on subsequent flights. Following field sampling, resin tubes were transported back to Baylor University for chemical analysis using a Markes International thermal desorption unit coupled with a gas chromatograph-tandem mass spectrometer (Thermo Scientific). The target analyte list includes biogenic and anthropogenic VOCs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Particle transport constraints via Bayesian spectral fitting of multiple atomic lines

Optimized operation of fusion devices demands detailed understanding of plasma transport, a problem that must be addressed with advances in both measurement and data analysis techniques. In this work, we adopt Bayesian inference methods to determine experimental particle transport, leveraging opportunities from high-resolution He-like ion spectra in a tokamak plasma. The Bayesian spectral fitting code is used to analyze resonance (w), forbidden (z), intercombination (x, y), and satellite (k, j) lines of He-like Ca following laser blow-off injections on Alcator C-Mod. This offers powerful transport constraints since these lines depend differently on electron temperature and density, but also differ in their relation to Li-like, He-like, and H-like ion densities, often the dominant Ca charge states over most of the C-Mod plasma radius. Using synthetic diagnostics based on the AURORA package, we demonstrate improved effectiveness of impurity transport inferences when spectroscopic data from a progressively larger number of lines are included.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Energy conversion and transport in molecular-scale junctions

Molecular-scale junctions (MSJs) have been considered the ideal testbed for probing physical and chemical processes at the molecular scale. Due to nanometric confinement, charge and energy transport in MSJs are governed by quantum mechanically dictated energy profiles, which can be tuned chemically or physically with atomic precision, offering rich possibilities beyond conventional semiconductor devices. While charge transport in MSJs has been extensively studied over the past two decades, understanding energy conversion and transport in MSJs has only become experimentally attainable in recent years. As demonstrated recently, by tuning the quantum interplay between the electrodes, the molecular core, and the contact interfaces, energy processes can be manipulated to achieve desired functionalities, opening new avenues for molecular electronics, energy harvesting, and sensing applications. This Review provides a comprehensive overview and critical analysis of various forms of energy conversion and transport processes in MSJs and their associated applications. We elaborate on energy-related processes mediated by the interaction between the core molecular structure in MSJs and different external stimuli, such as light, heat, electric field, magnetic field, force, and other environmental cues. Key topics covered include photovoltaics, electroluminescence, thermoelectricity, heat conduction, catalysis, spin-mediated phenomena, and vibrational effects. Furthermore, the review concludes with a discussion of existing challenges and future opportunities, aiming to facilitate in-depth future investigation of promising experimental platforms, molecular design principles, control strategies, and new application scenarios.

Charge transport↗
DOE OSTI2022

Transport physics dependence of Bohm speed in presheath–sheath transition

We report the ion exit flow speed at the sheath entrance is constrained by the Bohm criterion, which is used as a boundary condition for simulations that do not resolve the sheath region. Traditional Bohm criterion analysis invokes the equation of state and, thus, ignores transport physics in the sheath transition problem. An expression for the Bohm speed away from the asymptotic limit is derived from a set of anisotropic plasma transport equations. The thermal force, collisional temperature isotropization, and heat flux enter into the evaluation of the Bohm speed. By comparison with kinetic simulation results, this expression is shown to be accurate in the presheath–sheath transition region rather than a single point at the sheath entrance over a broad range of collisionality.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Decay Dose Shielding Analysis with Hybrid Unstructured Mesh/Constructive Solid Geometry Monte Carlo Calculation and ADVANTG Acceleration

Here, the target segments of the Oak Ridge National Laboratory Second Target Station (STS) neutron production facility become highly activated due to spallation reactions or nuclei transmutation by primary protons and emitted neutrons. Once the target segments are removed from their location within the core vessel, decay dose rates must be accurately quantified to determine the shielding configurations of remote-handling tools and transport casks and to aid in planning maintenance activities. For this analysis, we utilized a hybrid unstructured mesh (UM)/constructive solid geometry approach for calculating spallation products and neutron fluxes, activation calculations using the AARE package that includes the CINDER2008 activation code to calculate the decay photon source at different cooling times, and the ADVANTG code to accelerate the final decay photon transport calculation. Both Type 316 stainless steel (SS-316) and lead were investigated as candidates for shielding materials. The decay photon transport calculation through the thick SS-316 or lead shields exhibited between 25 and 30 orders-of-magnitude attenuations in the radial direction, depending on the shield. Such a difficult shielding calculation required advanced variance reduction. ADVANTG has some missing features, which limits its usability in spallation neutron source applications. It does not support volumetric sources created for MCNP6.2 UM capability. An approximate source was created for this problem. Not only was this approximate source needed for running the ADVANTG calculation to generate the weight windows, but also it was essential to develop source biasing (SB) parameters that were crucial for dramatically accelerating the decay photon transport in this problem. With this approximate source, the analysis was completed in a very reasonable computational time, and the design of the STS remote-handling equipment was finalized. This paper compares the efficiency of Monte Carlo simulations with different weight window and SB parameters calculated using different approximate ADVANTG calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Data for Monitoring Fracture Saturation with Internal Transportable Seismic Sources and Twin Neural Networks

Seismic coda-wave analysis is a well-developed method for detecting subtle physical changes in complex media by measuring arrival times in the late-arriving energy from multiply-scattered or reflected waves. However, a challenge arises when multiply-scattered waves are not sufficiently separated in time from the direct arrivals to provide a clear coda wave train. Additional complications for monitoring changes in fracture systems arise when the signals originate from unsynchronized internal sources, such as natural induced seismicity, from acoustic emission, or from transportable intra-fracture sources (chattering dust), that generate uncontrolled signals that vary in arrival time, amplitude and frequency content. Here, we use a twin neural network (also known as a Siamese neural network) for dimensionality reduction to analyze signals from chattering dust to classify the fluid saturation state of a synthetic fracture system. The twin neural network with shared weights generates a low-dimensional representation of the data input by minimizing contrastive loss, serving as the input to a multiclass classifier that accurately classifies whether multiple fractures in a fracture system are fully saturated or partially saturated, or whether a change in saturation has occurred in different fractures in the system. These results show that information buried in unresolved codas from uncontrolled sources can be extracted using machine learning to monitor the evolution of fracture systems caused by physical and chemical processes even when the scattered and direct wave fields overlap.

58 GEOSCIENCES↗
DOE OSTI2024

Artificial Transmembrane Channel Constructed from Shape-Persistent Covalent Organic Molecular Cages Capable of Ion and Small Molecule Transport

Shape-persistent arylene ethynylene molecular cages have been investigated as transmembrane channels for ions and small molecules. The molecular cages were obtained starting from tetrayne monomers through alkyne metathesis cyclooligomerization. We found these porphyrin-based rigid molecular cages can insert into the lipid bilayer and efficiently transport ions and small molecules (e.g., calcein). Our study reveals longer hydrophobic alkyl chains on the cage molecule promote the channeling efficiency, while shorter and/or more polar side chains impair such activity. Kinetic analysis shows linear correlation between the rate of proton transport and the concentration of the cage, suggesting the active species is likely a monomeric cage. We found that C70-encapsulated cages are nearly inactive for transmembrane ion transportation, indicating that ions are likely transported through the internal cavity of the cage. Discrete shape-persistent organic cages represent highly stable synthetic ion channels or pores, which could have interesting applications in biomimetic signaling and drug delivery.

alkyne metathesis↗
DOE OSTI2022

Evaluation of subsurface transport processes of delayed gas signatures applicable to underground nuclear explosions

Radioactive gas signatures from underground nuclear explosions (UNEs) result from gas-migration processes occurring in the subsurface. The processes considered in this study either drive or retard upward migration of gases from the detonation cavity. The relative importance of these processes is evaluated by simulating subsurface transport in a dual-permeability medium for the multi-tracer Noble Gas Migration Experiment (NGME) originally intended to study some aspects of transport from a UNE. For this experiment, relevant driving processes include weak two-phase convection driven by the geothermal gradient, over pressuring of the detonation cavity, and barometric pumping while gas sorption, dissolution, radioactive decay, and usually diffusion represent retarding processes. From deterministic simulations we found that over-pressuring of the post-detonation chimney coupled with barometric pumping produced a synergistic effect amplifying the tracer-gas reaching the surface. Bounding simulations indicated that the sorption and dissolution of gases, tending to retard transport, were much smaller than anticipated by earlier laboratory studies. The NGME observations themselves show that differences in gas diffusivity have a larger effect on influencing upward transport than do the combined effects of tracer-gas sorption and dissolution, which is consistent with a Sobol’ sensitivity analysis. Both deterministic simulations and those considering parametric uncertainties of transport-related properties predict that the excess in concentration of SF 6 compared to 127 Xe as might be captured in small volumetric samples should be much smaller than the order-of-magnitude contrast found in the large-volume gas samples taken at the site. While extraction of large-volume subsurface gas samples is shown to be capable of distorting in situ gas compositions, the highly variable injection rate of SF 6 into the detonation cavity relative to that of 127 Xe at the start of the field experiment is the most likely explanation for the large difference in observed concentrations.

58 GEOSCIENCES↗