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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Modeling Particle Acceleration and Release from Solar Eruptions

Determining the relative contribution of solar flares versus coronal mass ejections in large solar energetic particle (SEP) events is a long-standing problem. Flare-accelerated particles may travel through complex magnetic fields in the eruption region and escape into interplanetary space, thereby contributing to large SEP events. The process by which flare accelerated particles are released into the heliosphere is poorly understood and yet is critical to advancing our understanding of SEPs. In this work, we address the release problem by solving the focused transport equation in the context of a 2.5D ARMS magnetohydrodynamic simulation of a breakout coronal mass ejection (CME)/flare event. We find that particles accelerated by flare reconnection can be released into interplanetary space through interchange reconnection between closed and open field lines. These particles can contribute directly to SEP events and may become an important seed population for further acceleration by CME-driven shocks. Additionally, we find that the energetic particle fluxes in the inner heliosphere remain elevated for an extended period, allowing them to contribute to SEP acceleration by subsequent CMEs. This study represents the first direct particle modeling of how flare-accelerated particles can contribute to major SEP events.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Compound Drought and Heatwaves at a Global Scale: The Role of Natural Climate Variability-Associated Synoptic Patterns and Land-Surface Energy Budget Anomalies

Compound drought and heatwave (CDHW) events have garnered much attention in recent studies. However, thus far, the identification of such events is oversimplified, and their association with natural climate variability is not fully explored. Here, we derive anomalies in the weekly self-calibrated Palmer Drought Severity Index (sc_PDSI) and daily maximum temperatures to identify CDHW events from 1982 to 2016 over 26 climate regions across the globe. Using a Poisson Generalized Linear Model (GLM), we analyze yearly occurrences of seasonal CDHW events and their association with the warm and cold phases of El Nino Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO). ENSO exhibits robust association with CDHW events over the Southern Hemisphere during the austral summer and fall, while PDO influences their occurrences over the Western North America in the Northern Hemisphere during the boreal summer, which is supported by the composites of anomalies in the atmospheric circulations and surface energy budget. However, NAO association with CDHW events is relatively weak. The CDHW occurrence over other regions is driven by a combination of these large-scale natural forcing. Our analyses also highlight that the cooccurrence of weekly to submonthly scale anomalies in the observed temperature and precipitation may not be always aligned between the observations and the reanalysis. Therefore, caution must be exercised while explaining such observed anomalies on the basis of reanalysis-based circulations and surface energy budget. Lastly, our analyses provide a new insight towards concurrent extremes and should help foster research efforts in this area.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Forecasting Megaelectron-Volt Electrons Inside Earth's Outer Radiation Belt: PreMevE 2.0 Based on Supervised Machine Learning Algorithms

We present the recent progress in upgrading a predictive model for megaelectron-volt (MeV) electrons inside the Earth's outer Van Allen belt. This updated model, called PreMevE 2.0, provides improved forecasts, particularly at outer L-shells, by adding upstream solar wind speeds to the model's input parameter list that originally includes precipitating electrons observed at low Earth orbits and MeV electron fluxes in situ measured by a geosynchronous satellite. Furthermore, based on several kinds of linear and artificial neural networks algorithms, a list of models was constructed, trained, validated, and tested with 42-month MeV electron observations from Van Allen Probes. Out-of-sample test results from these models show that, with optimized model hyperparameters and input parameter combinations, the top performer from each category of models has the similar capability of making reliable 1-day (2-day) forecasts of 1-MeV electron flux distributions with performance efficiency values ~0.87 (~0.82) averaged over the L-shell range of 2.8–6.6, significantly outperforming the previous version of PreMevE particularly at L-shells > ~4.5. Interestingly, the linear regression model is often the most successful when compared to other models, which suggests the relationship between dynamics of trapped 1-MeV electrons and precipitating electrons is dominated by linear components. Results also show that PreMevE 2.0 can reasonably well predict the onsets of MeV electron events in 2-day forecasts. PreMevE 2.0 is designed to be driven by observations from longstanding space infrastructure to make high-fidelity forecasts for MeV electrons, thus can be an invaluable space weather forecasting tool for the future.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

The Effects of Climate Change on Interregional Electricity Market Dynamics on the U.S. West Coast

The United States (U.S.) West Coast power system is strongly influenced by variability and extremes in air temperatures (which drive electricity demand) and streamflows (which constrain hydropower production). As hydroclimate changes across the West Coast, a combination of forces may work in tandem to make its bulk power system more vulnerable to physical reliability issues and market price shocks. In particular, a warmer climate is expected to increase summer cooling (electricity) demands and shift the average timing of peak streamflow (hydropower production) away from summer to the spring and winter, depriving power systems of hydropower when it is needed the most. Here, we investigate how climate change could alter interregional electricity market dynamics on the West Coast, including the potential for hydroclimatic changes in one region (e.g. Pacific Northwest (PNW)) to “spill over” and cause price and reliability risks in another (e.g. California). We find that the most salient hydroclimatic risks for the PNW power system are changes in streamflow, while risks for the California system are driven primarily by changes in summer air temperatures, especially extreme heat events that increase peak system demand. Altered timing and amounts of hydropower production in the PNW do alter summer power deliveries into California but show relatively modest potential to impact prices and reliability there. Instead, it is future climate conditions (extreme heat) in California that may exert a stronger significant influence on prices and reliability in the PNW, especially if California continues to rely on its northern neighbor for imported power to meet higher summer demands.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Contrasting biological production trends over land and ocean

Terrestrial and marine ecosystems constitute the primary components of the Earth’s biosphere, yet their photosynthetic productions are typically studied separately, which limits understanding of planetary carbon uptake and biosphere health. Here, in this study, using multiple satellite-derived products, we identify contrasting net primary production (NPP) trends between land and ocean, probably reflecting their differential sensitivity to climate warming, especially in tropical regions. Planetary NPP shows an overall increase of 0.11 ± 0.13 PgC yr −1 (P = 0.05) from 2003 to 2021, driven by a significant terrestrial enhancement of 0.20 ± 0.07 PgC yr −1 (P < 0.001) and partially offset by an oceanic decline of −0.12 ± 0.12 PgC yr −1 (P = 0.07). While land contributes to the strong upwards NPP trend, the interannual variability in global NPP is predominantly driven by the ocean, especially during strong El Niño–Southern Oscillation events. Our findings highlight the resilience and potential vulnerability of biosphere primary productivity in a warming climate, calling for integrated land–ocean monitoring and assessment to support climate mitigation initiatives.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A fluid-structure interaction solver for investigating torsional galloping in solar-tracking photovoltaic panel arrays

Solar-tracking photovoltaic arrays are susceptible to aeroelastic fluttering during high-wind events. This dynamic fluttering behavior can grow in amplitude until the panels enter an unstable mode known as torsional galloping which can lead to panel failure or total array destruction. To better understand the physics of the torsional galloping phenomenon and to inform the discussion around panel design and recommended panel stow positions during high wind events, a fluid-structure interaction solver composed of a simulated atmospheric boundary layer with simplified panel structural responses was designed. The simulation choices and features of this solver were informed by the geometry and physical properties of an experimental panel array known to exhibit torsional galloping behavior during hind-wind events. Furthermore, these simulations revealed that the torsional galloping instability is driven by a combination of cyclic vortex shedding from the sun-facing side of the panel and the elastic properties of the torque tube linking the panel assemblies. Testing different stow angles across a range of wind speeds indicates that panels are generally more stable when stowed at negative angles where the leading edge is closer to the ground, hypothesized to be due to ground-blocking effects. These results are supplemented by a discussion of stability trends noted during testing and possible implications when considering multi-row array interactions.

14 SOLAR ENERGY↗
DOE OSTI2025

Machine learning neutrino-nucleus cross sections

Neutrino-nucleus scattering cross sections are critical theoretical inputs for long-baseline neutrino oscillation experiments. However, robust modeling of these cross sections remains challenging. For a simple but physically motivated toy model of the DUNE experiment, we demonstrate that an accurate neural-network model of the cross section—leveraging only Standard-Model symmetries—can be learned from near-detector data. We perform a neutrino oscillation analysis with simulated far-detector events, finding that oscillation analysis results enabled by our data-driven cross-section model approach the theoretical limit achievable with perfect prior knowledge of the cross section. We further quantify the effects of flux shape and detector resolution uncertainties as well as systematics from cross-section mismodeling. This proof-of-principle study highlights the potential of future neutrino near-detector datasets and data-driven cross-section models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Resimulation-based self-supervised learning for pretraining physics foundation models

Self-supervised learning (SSL) is at the core of training modern large machine learning models, providing a scheme for learning powerful representations that can be used in a variety of downstream tasks. However, SSL strategies must be adapted to the type of training data and downstream tasks required. We propose resimulation-based self-supervised representation learning (RS3L), a novel simulation-based SSL strategy that employs a method of resimulation to drive data augmentation for contrastive learning in the physical sciences, particularly, in fields that rely on stochastic simulators. By intervening in the middle of the simulation process and rerunning simulation components downstream of the intervention, we generate multiple realizations of an event, thus producing a set of augmentations covering all physics-driven variations available in the simulator. Using experiments from high-energy physics, we explore how this strategy may enable the development of a foundation model; we show how RS3L pretraining enables powerful performance in downstream tasks such as discrimination of a variety of objects and uncertainty mitigation. In addition to our results, we make the RS3L dataset publicly available for further studies on how to improve SSL strategies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Direct visualization of electromagnetic wave dynamics by laser-free ultrafast electron microscopy

Integrating femtosecond lasers with electron microscopies has enabled direct imaging of transient structures and morphologies of materials in real time and space. Here, we report the development of a laser-free ultrafast electron microscopy (UEM) offering the same capability but without requiring femtosecond lasers and intricate instrumental modifications. We create picosecond electron pulses for probing dynamic events by chopping a continuous beam with a radio frequency (RF)–driven pulser with the pulse repetition rate tunable from 100 MHz to 12 GHz. As a first application, we studied gigahertz electromagnetic wave propagation dynamics in an interdigitated comb structure. We reveal, on nanometer space and picosecond time scales, the transient oscillating electromagnetic field around the tines of the combs with time-resolved polarization, amplitude, and local field enhancement. This study demonstrates the feasibility of laser-free UEM in real-space visualization of dynamics for many research fields, especially the electrodynamics in devices associated with information processing technology.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Enhancing Resilience of Urban Systems Against Climate-Induced Floods Using Advanced Data-Driven and Computing Techniques: A Driver-Pressure-State-Impact-Response (DPSIR) Framework

Focal Area: This white paper proposes advanced data-driven and computing techniques to improve the predictability of extreme climate events (precipitation) and focuses on comprehending their impact dynamics to enhance resilience of urban systems. Science Challenge: In urban systems, climate change has presented significant challenges related to accurate spatiotemporal prediction of extreme climate events (heavy precipitation) and their impacts, i.e. flooding. The occurrence of extreme climate events and their compounding repercussions on the built environment make urban systems increasingly more vulnerable. The interactions between extreme climate events, urban systems, and the corresponding impacts are irregular, non-linear, and dynamic, making it difficult to standardize resilience. As it stands, the management of complex urban systems is affected by uncertainties related to the nature of short- and long-term consequences caused by extreme events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Benchmarking State-of-the-Art Theory and Empirical Models of Pionless Neutrino-Argon Scattering in GENIE

Neutrino event generators require a delicate balance between theory and empirically-driven models to achieve reliable simulations. GENIE is the most commonly used generator, bridging theory and experiment in modern neutrino physics. Its flexible framework makes it ideal for comparing different models across all aspects of neutrino interactions—from the nuclear ground state and primary vertex to final-state interactions. Recently, GENIE has incorporated several state-of-the-art, theory-driven models, including spectral-function descriptions of the nuclear ground state, axial form factors from first-principles lattice QCD calculations, and the Liège intranuclear cascade model for simulating final-state interactions. Compared to the previous baseline models in GENIE—such as the local Fermi gas, dipole-like axial form factor, and hA final-state interaction model—the new implementations are more physically realistic and incorporate more complete physics, including nuclear de-excitation. This poster presents the implementation of new models in GENIE and their performance in comparisons among these models against recent MicroBooNE cross-section measurements.

Liu, Liang [Fermilab] (ORCID:000000026753925X)↗
DOE OSTI2026

Machine Learning Neutrino-Nucleus Cross Sections

Neutrino-nucleus scattering cross sections are critical theoretical inputs for long-baseline neutrino oscillation experiments. However, robust modeling of these cross sections remains challenging. For a simple but physically motivated toy model of the DUNE experiment, we demonstrate that an accurate neural-network model of the cross section—leveraging only Standard-Model symmetries— can be learned from near-detector data. We perform a neutrino oscillation analysis with simulated far-detector events, finding that oscillation analysis results enabled by our data-driven cross-section model approach the theoretical limit achievable with perfect prior knowledge of the cross section. We further quantify the effects of flux shape and detector resolution uncertainties as well as systematics from cross-section mismodeling. This proof-of-principle study highlights the potential of future neutrino near-detector datasets and data-driven cross-section models.

Tame-Narvaez, Karla [Fermilab] (ORCID:000000022249↗
DOE OSTI2026

Benchmarking State-of-the-Art Theory and Empirical Models of Pionless Neutrino-Argon Scattering in GENIE

Neutrino event generators require a delicate balance between theory and empirically-driven models to achieve reliable simulations. GENIE is the most commonly used generator, bridging theory and experiment in modern neutrino physics. Its flexible framework makes it ideal for comparing different models across all aspects of neutrino interactions—from the nuclear ground state and primary vertex to final-state interactions. Recently, GENIE has incorporated several state-of-the-art, theory-driven models, including spectral-function descriptions of the nuclear ground state, axial form factors from first-principles lattice QCD calculations, and the Liège intranuclear cascade model for simulating final-state interactions. Compared to the previous baseline models in GENIE—such as the local Fermi gas, dipole-like axial form factor, and hA final-state interaction model—the new implementations are more physically realistic and incorporate more complete physics, including nuclear de-excitation. This poster presents the implementation of new models in GENIE and their performance in comparisons among these models against recent MicroBooNE cross-section measurements.

Liu, Liang [Fermilab] (ORCID:000000026753925X)↗
NASA NTRS1978

The decay of coronal loops brightened by flares and transients

Observations of X-ray emitting loops derived from Skylab S-054 photographs, and combined with temperature and brightness estimates from Solrad data, are used to determine brightness decay times resulting from various coronal energy loss mechanisms. Conductive losses are found to be more rapid than radiative losses. Attention is given to the role of geometrical inhibition of conduction as a possible mechanism of brightness decay. Soft X-ray observations are consistent with the continuation of the 'evaporation' driven by thermal conduction late into the decay phase of the event.

Krieger, A. S.↗
NASA NTRS1987

Transient rotor dynamic rub phenomena - Theory and test

This paper develops an implicit integration scheme for transient rotor dynamic rub prediction and includes a correlation study with actual test results. A Nordsieck-like numerical integration scheme is applied directly to the second-order equations of motion. The assumption that forces and torques on the rotor are functions of the position and velocity at the point of application and its nearest neighbor is made in order to make the computational time proportional to the number of elements in the rotor dynamics model rather than the cube of the number. The test rig consists of a turbine driven, flexible shaft supported by squeeze film dampers. The blade loss event occurs due to collision of a balance bolt on one of the disks with a high speed plunger. The rotor is seen to spiral outward and contact against a stationary assemblage of seal shoes.

Kascak, A. F.↗
NASA NTRS2002

Purification of the small mechanosensitive channel of Escherichia coli (MscS): the subunit structure, conduction, and gating characteristics in liposomes

The small mechanosensitive channel, MscS, is a part of the turgor-driven solute efflux system that protects bacteria from lysis in the event of osmotic downshift. It has been identified in Escherichia coli as a product of the orphan yggB gene, now called mscS (Levina et al., 1999, EMBO J. 18:1730). Here I show that that the isolated 31-kDa MscS protein is sufficient to form a functional mechanosensitive channel gated directly by tension in the lipid bilayer. MscS-6His complexes purified in the presence of octylglucoside and lipids migrate in a high-resolution gel-filtration column as particles of approximately 200 kDa. Consistent with that, the protein cross-linking patterns predict a hexamer. The channel reconstituted in soybean asolectin liposomes was activated by pressures of 20-60 mm Hg and displayed the same asymmetric I-V curve and slight anionic preference as in situ. At the same time, the single-channel conductance is proportional to the buffer conductivity in a wide range of salt concentrations. The rate of channel activation in response to increasing pressure gradient across the patch was slower than the rate of closure in response to decreasing steps of pressure gradient. Therefore, the open probability curves were recorded with descending series of pressures. Determination of the curvature of patches by video imaging permitted measurements of the channel activity as a function of membrane tension (gamma). Po(gamma) curves had the midpoint at 5.5 +/- 0.1 dyne/cm and gave estimates for the energy of opening DeltaG = 11.4 +/- 0.5 kT, and the transition-related area change DeltaA = 8.4 +/- 0.4 nm(2) when fitted with a two-state Boltzmann model. The correspondence between channel properties in the native and reconstituted systems is discussed.

NASA Discipline Cell Biology↗
NASA NTRS2013

Comparison Between Path Lengths Traveled by Solar Electrons and Ions in Ground-Level Enhancement Events

We have examined the Wind/3DP/SST electron and Wind/EPACT/LEMT ion data to investigate the path length difference between solar electrons and ions in the ground-level enhancement (GLE) events in solar cycle 23. Assuming that the onset time of metric type II or decameter-hectometric (DH) type III radio bursts is the solar release time of non-relativistic electrons, we have found that within an error range of plus or minus 10% the deduced path length of low-energy (approximately 27 keV) electrons from their release site near the Sun to the 1 AU observer is consistent with the ion path length deduced by Reames from the onset time analysis. In addition, the solar longitude distribution and IMF topology of the GLE events examined are in favor of the coronal mass ejection-driven shock acceleration origin of observed non-relativistic electrons.We have also found an increase of electron path lengths with increasing electron energies. The increasing rate of path lengths is correlated with the pitch angle distribution (PAD) of peak electron intensities locally measured, with a higher rate corresponding to a broader PAD. The correlation indicates that the path length enhancement is due to the interplanetary scattering experienced by first arriving electrons. The observed path length consistency implies that the maximum stable time of magnetic flux tubes, along which particles transport, could reach 4.8 hr.

electrons↗
DOE OSTI2025

Data-driven model validation for neutrino-nucleus cross section measurements

Neutrino-nucleus cross section measurements are needed to improve interaction modeling to meet the precision needs of neutrino experiments in efforts to measure oscillation parameters and search for physics beyond the Standard Model. We review the difficulties associated with modeling neutrino-nucleus interactions that lead to a dependence on event generators in oscillation analyses and cross section measurements alike. We then describe data-driven model validation techniques intended to address this model dependence. The method relies on utilizing various goodness-of-fit tests and the correlations between different observables and channels to probe the model for defects in the phase space relevant for the desired analysis. These techniques shed light on relevant mismodeling, allowing it to be detected before it begins to bias the cross section results. We compare more commonly used model validation methods which directly validate the model against alternative ones to these data-driven techniques and show their efficacy with fake data studies. These studies demonstrate that employing data-driven model validation in cross section measurements represents a reliable strategy to produce robust results that will stimulate the desired improvements to interaction modeling.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗