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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Basement Fault Activation before Larger Earthquakes in Oklahoma and Kansas

Oklahoma and Kansas experienced unprecedented seismic activity over the past decade due to earthquakes associated with unconventional hydrocarbon development. The modest natural seismicity and incomplete knowledge of the fault network in the region made it difficult to anticipate the locations of earthquakes with larger magnitudes (M w ≥ 4). Here, we show that monitoring of microearthquakes at regional scale using a pretrained neural phase picker and an earthquake relocation algorithm can illuminate unknown fault structures, and deliver information that can be synthesized for earthquake forecasting. We found that 80% of the larger earthquakes that occurred in the past decade could have been anticipated based on the spatial extent of the seismicity clusters that were formed before these earthquakes occurred. We also found that once a seismicity cluster with a length scale enough to host a larger earthquake was formed, there was a ~5% chance that it would host one or more larger earthquakes within a year. This probability is nearly an order of magnitude higher than one based on Gutenberg–Richter statistics and preceding seismicity. Applying our approach in practice can provide critical information on seismic hazards for risk management and regulatory decision making.

58 GEOSCIENCES↗
DOE OSTI2022

Computing Thermodynamic Properties of Fluids Augmented by Nanoconfinement: Application to Pressurized Methane

Nanoconfined fluids exhibit remarkably different thermodynamic behavior compared to the bulk phase. These confinement effects render predictions of thermodynamic quantities of nanoconfined fluids challenging. In particular, confinement creates a spatially varying density profile near the wall that is primarily responsible for adsorption and capillary condensation behavior. Significant fluctuations in thermodynamic quantities, inherent in such nanoscale systems, coupled to strong fluid–wall interactions give rise to this near-wall density profile. Empirical models have been proposed to explain and model these effects, yet no first-principles based formulation has been developed. We present a statistical mechanics framework that embeds such a coupling to describe the effect of the fluid–wall interaction in amplifying the near-wall density behavior for compressible gases at elevated pressures such as pressurized methane in confinement. We show that the proposed theory predicts accurately the adsorbed layer thickness as obtained with small-angle neutron scattering measurements. Furthermore, the predictions of density under confinement from the proposed theory are shown to be in excellent agreement with available experimental and atomistic simulations data for a range of temperatures for nanoconfined methane. While the framework is presented for evaluating the near-wall density, owing to its rigorous foundation in statistical mechanics, the proposed theory can also be generalized for predicting phase-transition and nonequilibrium transport of nanoconfined fluids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Using Temporal Deep Learning Models to Estimate Daily Snow Water Equivalent Over the Rocky Mountains

Abstract In this study we construct and compare three different deep learning (DL) models for estimating daily snow water equivalent (SWE) from high‐resolution gridded meteorological fields over the Rocky Mountain region. To train the DL models, Snow Telemetry (SNOTEL) station‐based SWE observations are used as the prediction target. All DL models produce higher median Nash‐Sutcliffe Efficiency (NSE) values than a conceptual SWE model and interpolated gridded data sets, although mean squared errors also tend to be higher. Sensitivity of the SWE prediction to the model's input variables is analyzed using an explainable artificial intelligence (XAI) method, yielding insight into the physical relationships learned by the models. This method reveals the dominant role precipitation and temperature play in snowpack dynamics. In applying our models to estimate SWE throughout the Rocky Mountains, an extrapolation problem arises since the statistical properties of SWE (e.g., annual maximum) and geographical properties of individual grid points (e.g., elevation) differ from the training data. This problem is solved by normalizing the SWE with its historical maximum value to alleviate extrapolation for all tested DL models. Our work shows that the DL models are promising tools for estimating SWE, and sufficiently capture relevant physical relationships to make them useful for spatial and temporal extrapolation of SWE values.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Time-domain theory of transient heat conduction in the local limit

Ultrafast and nanoscale heat conduction demands a unified theoretical framework that rigorously bridges macroscopic transport equations with microscopic material properties derived from statistical physics. Existing empirical generalizations of Fourier's law often lack a solid microscopic foundation, failing to connect observed non-Fourier behavior with underlying atomic-scale mechanisms. In this work, we present a time-domain theory of transient heat conduction rooted in Zwanzig's statistical theory of irreversible processes. Central to this framework is the time-domain transport function $\overleftrightarrow{𝑍}$⁡(𝑡) defined through equilibrium time-correlation functions of heat fluxes. This function generalizes the conventional concept of steady-state thermal conductivity, governing the transition of conduction dynamics from onset second sound type wave propagation at finite speeds to diffusion-dominated behavior across broad temporal and spatial scales. Unlike phonon hydrodynamic models that rely on mesoscopic constructs such as phonon drift velocity, our approach provides a quantitative and microscopic description of intrinsic memory effects in transient heat fluxes and applies universally to bulk materials at any temperature or length scale. By integrating atomistic-scale first-principles calculations with continuum-level macroscopic equations, this framework offers a robust foundation for numerical simulations of transient temperature fields. Furthermore, it facilitates the interpretation and design of transient thermal grating experiments using nanometer-scale heat sources and ultrafast laser systems in the extreme ultraviolet and x-ray wavelength ranges, advancing our understanding of heat dissipation dynamics.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Segmental Dynamics and Vitrification in Associating Copolymer Melts: Role of Cluster Formation, Microdomains, and Cross-Linking

A microscopic statistical mechanical theory of the structure, self-assembly, and activated segmental relaxation is employed to study associating copolymer melts with high attractive sticker fractions, local clustering, and disordered microphase ordering. The stickers are dynamically pinned in a manner that does not affect equilibrium structure which mimics the much slower physical bond breaking process or postassembly cross-linking of sticky monomers. Local sticker clustering and microdomain spatial correlations significantly modify the activated relaxation of nonstickers and glass transition temperature, T g . A re-entrant glass-melting feature is predicted as sticker attraction strength is initially increased corresponding to a speed up of segmental relaxation, and hence reduction of T g relative to the cross-linked homopolymer network. A mechanistic analysis reveals three competing effects: a purely kinetic slowing down of nonstickers down due to cross-linking, disordering of the nonsticker local cage and weakening of effective forces they experience due to sticker physical clustering, and a longer range impact of microdomain scale correlations that results in nonmonotonic dynamical effects. At high enough attraction strength, a qualitative change emerges corresponding to a sticker fraction dependent elevation of T g , which eventually surpasses that of the cross-linked homopolymer network. Dynamically, the new physics arises from a complex evolution of the amplitude of the collective elastic field that dresses the large amplitude mobile segment hopping within a coupled local-nonlocal description of the alpha relaxation. Furthermore, the results are qualitatively consistent with recent experiments on associating PDMS and PPG telechelics of fixed sticker fraction but with chemically different end groups of variable attraction strengths. Finally, possible tests using simulation and the influence of material or model specific interaction potentials and other real world complications are discussed.

Copolymers↗
DOE OSTI2024

A Stereo Camera Simulator for Large-Eddy Simulations of Continental Shallow Cumulus Clouds Based on Three-Dimensional Path-Tracing

The complex spatial and temporal structure of cumulus clouds complicates their representation in weather and climate models. Classic meteorological instrumentation struggles to fully capture these features. Networks of multiple high-resolution hemispheric cameras are increasingly used to fill this data gap, and provide information on this missing multi-dimensional spatial information. In this study, a path-tracing algorithm is used to generate virtual camera images of resolved clouds in large-eddy simulations (LES). These images are then used as a camera network simulator, allowing reconstructions of three-dimensional cloud edges from the model output. Because the actual LES cloud field is fully known, the combined path-tracing and reconstruction method can be statistically analyzed. The method is applied to LES realizations of summertime shallow cumulus at the Jülich Observatory for Cloud Evolution (JOYCE), Germany, which also routinely operates a camera network. We find that the path-tracing method allows accurate reconstruction of up to 70% of the visible cloud edges. Additional sensitivity tests show that the method is robust for changes in its hyperparameters. The sensitivity to cloud optical thickness is also investigated, finding a cloud boundary placement error of approximately 182 m. This error can be considered typical for cloud boundary reconstruction using real stereo camera imagery. The results provide proof of principle for future use of the method for evaluating LES clouds against camera network imagery, and for further optimizing the configuration of such camera networks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Modes and Mechanisms of Pacific Decadal-Scale Variability

The modes of Pacific decadal-scale variability (PDV), traditionally defined as statistical patterns of variance, reflect to first order the ocean's integration (i.e., reddening) of atmospheric forcing that arises from both a shift and a change in strength of the climatological (time-mean) atmospheric circulation. While these patterns concisely describe PDV, they do not distinguish among the key dynamical processes driving the evolution of PDV anomalies, including atmospheric and ocean teleconnections and coupled feedbacks with similar spatial structures that operate on different timescales. In this review, we synthesize past analysis using an empirical dynamical model constructed from monthly ocean surface anomalies drawn from several reanalysis products, showing that the PDV modes of variance result from two fundamental low-frequency dynamical eigenmodes: the North Pacific–central Pacific (NP-CP) and Kuroshio–Oyashio Extension (KOE) modes. Both eigenmodes highlight how two-way tropical–extratropical teleconnection dynamics are the primary mechanisms energizing and synchronizing the basin-scale footprint of PDV. While the NP-CP mode captures interannual- to decadal-scale variability, the KOE mode is linked to the basin-scale expression of PDV on decadal to multidecadal timescales, including contributions from the South Pacific.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Self-organized criticality in multi-pulse gamma-ray bursts

The variability in multi-pulse gamma-ray bursts (GRBs) may help to reveal the mechanism of underlying processes from the central engine. To investigate whether the self-organized criticality (SOC) phenomena exist in the prompt phase of GRBs, we statistically study the properties of GRBs with more than 3 pulses in each burst by fitting the distributions of several observed physical variables with a Markov Chain Monte Carlo approach, including the isotropic energy E iso , the duration time T, and the peak count rate P of each pulse. Our sample consists of 454 pulses in 93 GRBs observed by the CGRO/BATSE satellite. The best-fitting values and uncertainties for these power-law indices of the differential frequency distributions are: α$^{d}_{E}$ = 1.54±0.09, α$^{d}_{T}$ = 1.82±$^{+0.14}_{0.15}$ and α$^{d}_{P}$ = 2.09$^{+0.18}_{–0.19}$, while the power-law indices in the cumulative frequency distributions are: α$^{c}_{E}$ = 1.44$^{+0.08}_{–0.10}$, α$^{c}_{T}$ = 1.75$^{+0.141}_{–0.13}$ and α$^{c}_{P}$ = 1.99$^{+0.16}_{–0.19}$. We find that these distributions are roughly consistent with the physical framework of a Fractal-Diffusive, Self-Organized Criticality (FD-SOC) system with the spatial dimension S = 3 and the classical diffusion β=1. The results of this study support that the jet responsible for the GRBs should be magnetically dominated and magnetic instabilities (e.g., kink model, or tearing-model instability) lead the GRB emission region into the SOC state.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Grain size estimation in fluvial gravel bars using uncrewed aerial vehicles: A comparison between methods based on imagery and topography

Abstract Grain size assessments are necessary for understanding the various geomorphological, hydrological and ecological processes that occur within rivers. Recent research has shown that the application of Structure‐from‐Motion (SfM) photogrammetry to imagery from uncrewed aerial vehicles (UAVs) shows promise for rapidly characterising grain sizes along rivers in comparison to traditional field‐based methods. Here, we evaluated the applicability of different methods for estimating grain sizes in gravel bars along a study reach in the Olentangy River in Columbus, Ohio. We collected imagery of these gravel bars with a UAV and processed those images with SfM photogrammetry software to produce three‐dimensional point clouds and orthomosaics. Our evaluation compared statistical models calibrated on topographic roughness, which was computed from the point clouds, and to those based on image texture, which was computed from the orthomosaics. Our results showed that statistical models calibrated on image texture were more accurate than those based on topographic roughness. This might be because of site‐specific patterns of grain size, shape and imbrication. Such patterns would have complicated the detection of topographic signatures associated with individual grains. Our work illustrates that UAV‐SfM approaches show potential to be used as an accessible method for characterising surface grain sizes along rivers at higher spatial and temporal resolutions than those provided by traditional methods.

Wong, Tyler↗
DOE OSTI2025

Perspectives on Systematic Cloud Microphysics Scheme Development With Machine Learning

Cloud microphysics—the collection of processes that govern the small‐scale formation, evolution, and interactions of liquid droplets and ice crystals in clouds and precipitation—remains a major source of uncertainty in weather and climate models. Although too small in scale to be explicitly resolved in any large‐eddy simulation, weather, or climate model, the representation of cloud microphysical processes has significant impact at the climate scale. Current microphysical schemes are limited by both parametric uncertainty, linked to uncertainty in physical parameter values, and structural uncertainty, arising from incomplete physical understanding of the processes at play or approximations made for computational efficiency. Recent advances in the application of machine learning (ML) to the physical sciences show significant potential for minimizing these limitations by leveraging high‐fidelity simulations and observations. Here we outline the challenges that must be addressed to apply ML toward cloud microphysics scheme development. This perspectives paper synthesizes recent progress in using data‐driven methods, including ML, to improve cloud microphysics parameterizations and highlights opportunities to address key uncertainties. We discuss the roles of aleatoric (irreducible, or statistical) and epistemic (reducible, or systematic) errors in contributing to microphysics parameterization uncertainty. ML can leverage observations to improve microphysical schemes via bottom‐up and top‐down constraints. Methods such as differentiable programming and ML‐enhanced sampling strategies and the creation of large scale benchmark data sets promise to bridge the gap between observations and models and to improve the consistency of cloud microphysical representation across temporal and spatial scales.

Lamb, Kara D. [Columbia Univ., New York, NY (Unite↗
DOE OSTI2025

UrbanScaping: Community Spatial Data Visualization & Analytics

Evaluating the electrification potential of buildings through retrofitting is crucial for reducing carbon emissions and the carbon footprint of built environments. This study leverages the Automatic Building Energy Modeling (AutoBEM) software, integrating the Model America database to create an urban context-based spatial analysis platform for community engagement and development. We selected Camp Hill Borough, PA, as a case study to analyze building-specific energy performance and evaluate the electrification potential of each building by switching to different Heating, Ventilation, and Air Conditioning (HVAC) systems and measurement components. The simulation results generated by the workflow provide retrofitting suggestions to help mitigate the carbon footprint as well as energy saving statistics of buildings. Additionally, the developed web-based interface serves as a community engagement platform, allowing residents to provide feedback and further develop interactive communication protocols. The outcomes of this project offer a baseline for community electrification planning and contribute to the design of low-carbon communities.

Chowdhury, Shovan [ORNL]↗
DOE OSTI2021

Impact of solar geoengineering on temperatures over the Indonesian Maritime Continent

Climate change has been projected to increase the intensity and magnitude of extreme temperature in Indonesia. Solar radiation management (SRM) has been proposed as a strategy to temporarily combat global warming, buying time for negative emissions. Though the global impacts of SRM have been extensively studied in recent years, regional impacts, especially in the tropics, have received much less attention. This paper investigates the potential stratospheric sulfate aerosol geoengineering to modify mean and extreme temperature, as well as the relative humidity and wet bulb temperature (WBT) change over Indonesian Maritime Continent (IMC) based on simulations from three different earth system models. We applied statistical downscaling and corrected the bias of model output to reproduce historical temperatures and relative humidity over IMC. We compared SRM as specified by the Geoengineering Model Intercomparison Project (GeoMIP) G4 experiment – 5 Tg of SO 2 into the lower stratosphere between 2020 and 2069 – concurrent with the RCP4.5 emissions scenario. SRM is able to significantly reduce the temperature means and extremes, and although differences in magnitude of response and spatial pattern occur, there is a generally consistent response. Additionally, the spatial response of changes forced by RCP4.5 scenario and SRM impact is notably heterogeneous in the archipelago, highlighting uncertainties that would be critical in assessing socio-economic consequences of both doing, and not doing SRM. In general, SRM has bigger impacts in reducing temperatures over land than oceans, and the southern monsoon region shows more variability. SRM is also effective at reducing the likelihood of WBT > 27OC events compared to RCP4.5 after some years of SRM deployment as well as during the post-termination period of SRM. Regional downscaling may be an effective tool in obtaining policy-relevant information about local effects of different future scenarios involving SRM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Comparing multi-source urban flood indicators: satellite, simulation, and citizen-reported data

Urban flooding arises from complex mechanisms, making it challenging to capture accurately with a single detection method. This study evaluates three complementary approaches to detect flooding across three Chicago neighborhoods: (i) Sentinel-1 synthetic aperture radar (SAR), offering weather-independent, high-resolution (10 m) imagery of surface inundation; (ii) the storm water management model (SWMM), simulating combined sewer overflow and drainage performance; and (iii) citizen-generated 311 service requests, capturing observed flooding impacts. By analyzing six storms ranging from severe to mild, we examine how each source uniquely contributes to identifying urban flood events. SAR imagery effectively identifies standing water but can miss brief flooding due to satellite revisit constraints. SWMM provides detailed insights into system-wide drainage behavior yet may underestimate localized street-level flooding. Meanwhile, 311 calls reflect real-world flooding impacts but are vulnerable to underreporting. Statistical overlap analysis highlights chronic flood hotspots repeatedly identified across multiple detection methods, indicating persistent infrastructure and topographic vulnerabilities. Temporal analysis further reveals that while SWMM flooding aligns closely with rainfall peaks, 311 calls typically precede or persist beyond these peaks. Our findings emphasize the value of using satellite observations, hydrological modeling, and resident-reported data in a complementary manner to better interpret patterns in flood timing, severity, and spatial distribution—providing insights that can inform targeted infrastructure improvements and contribute to urban flood resilience planning.

311↗
DOE OSTI2021

Sustainability Trait Modeling of Field-Grown Switchgrass ( Panicum virgatum ) Using UAV-Based Imagery

Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this study, we used UAV-based remote sensing for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potentials for multiple traits associated with sustainable production of switchgrass, and one statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait. Also, for the first time, lignin content was estimated in switchgrass shoots via UAV-based multispectral image analysis and statistical analysis. The UAV-based models were verified by ground-truthing via correlation analysis between the traits measured manually on the ground-based with UAV-based data. The normalized difference red edge (NDRE) vegetation index outperformed the normalized difference vegetation index (NDVI) for rust disease and nitrogen content, while NDVI performed better than NDRE for chlorophyll and lignin content. Overall, linear models were sufficient for rust disease and chlorophyll analysis, but for nitrogen and lignin contents, nonlinear models achieved better results. As the first comprehensive study to model switchgrass sustainability traits from UAV-based remote sensing, these results suggest that this methodology can be utilized for switchgrass high-throughput phenotyping in the field.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Kaon Femtoscopy with Lévy-Stable Sources from $\sqrt{s_{NN}}$ = 200 GeV Au+Au Collisions at RHIC

Femtoscopy has the capacity to probe the space-time geometry of the particle-emitting source in heavy-ion collisions. In particular, femtoscopy of like-sign kaon pairs may shed light on the origin of non-Gaussianity of the spatial emission probability density. The momentum correlations between like-sign kaon pairs are measured in data recorded by the STAR experiment, from $\sqrt{s_{NN}}$ = 200 GeV Au+Au collisions at RHIC, BNL. Preliminary results hint at the possible existence of non-Gaussian, Lévy-stable sources, with the likely presence of an anomalous diffusion process in the signal for the identically charged kaon pairs so produced. More statistically significant studies at lower centre-of-mass energies may contribute to the search for the critical end point of QCD.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Conversions between gas-phase metallicities in MaNGA

ABSTRACT We present polynomial conversions between each of 11 different strong line gas-phase metallicity calibrations, each based on ∼1.1 million star-forming spaxels in the public Sloan Digital Sky Survey (SDSS) Data Release 15 (DR15) Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. For this sample, which is ∼20 times larger than previous works, we present fifth-order polynomial fits for each of 110 possible calibration conversions, for both Small Magellanic Cloud-type and Milky Way-type dust corrections. The typical 2σ scatter around our polynomial fits is 0.1 dex; we present the range over which the metallicities are valid. Conversions between metallicities which rely on the same set of line ratios, or a heavily shared set of emission lines, have reduced scatter in their conversions relative to those conversions with little overlap in required emission lines. Calibration conversions with less consistent sets of emission lines also have increased galaxy-to-galaxy variability, and this variability can account for up to 35 per cent of the total scatter. We also compare our conversions to previous work with the single fibre SDSS DR7 spectra along with higher spatial resolution data from the TYPHOON Integral Field Spectroscopy survey, resulting in comparison samples with spatial resolutions from several kpc down to ∼100 pc. Our metallicity conversions, obtained with the large sample of MaNGA, are robust against the influence of diffuse ionized gas, redshift, effective radius, and spatial blurring, and are therefore consistent across both integrated spectra and the high-resolution integral field spectroscopy data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Integrated Modeling of U.S. Agricultural Soil Emissions of Reactive Nitrogen and Associated Impacts on Air Pollution, Health, and Climate

Agricultural soils are leading sources of reactive nitrogen (Nr) species including nitrogen oxides (NO x ), ammonia (NH 3 ), and nitrous oxide (N 2 O). Here, the propensity of NO x and NH 3 to generate ozone and fine particulate matter (PM) and associated impacts on health are highly variable, whereas the climate impacts of long-lived N 2 O are independent of emission timing and location. However, these impacts have rarely been compared on a spatially-resolved, monetized basis. In this study, we update the nitrogen scheme in an agroecosystem model to simulate the agricultural emissions of Nr from across the United States. We then apply a reduced-form air pollution health effects model to assess air quality impacts from NO x and NH 3 and a social cost of N 2 O to assess the climate impacts. Assuming an $\$$8.2 million Value of a Statistical Life (VSL) and a $\$$13,100/ton social cost of N 2 O, the air quality impacts are a factor of ~7 to 15 times as large as the climate impacts in heavily populated coastal regions, whereas the ratios are closer to 2.5 in sparsely populated regions. Our results show that air pollution, health, and climate should be considered jointly in future assessments of how farming practices affect Nr emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Dynamic self-assembly of compartmentalized DNA nanotubes

Abstract Bottom-up synthetic biology aims to engineer artificial cells capable of responsive behaviors by using a minimal set of molecular components. An important challenge toward this goal is the development of programmable biomaterials that can provide active spatial organization in cell-sized compartments. Here, we demonstrate the dynamic self-assembly of nucleic acid (NA) nanotubes inside water-in-oil droplets. We develop methods to encapsulate and assemble different types of DNA nanotubes from programmable DNA monomers, and demonstrate temporal control of assembly via designed pathways of RNA production and degradation. We examine the dynamic response of encapsulated nanotube assembly and disassembly with the support of statistical analysis of droplet images. Our study provides a toolkit of methods and components to build increasingly complex and functional NA materials to mimic life-like functions in synthetic cells.

59 BASIC BIOLOGICAL SCIENCES↗