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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Characterizing the LANDSAT Global Long-Term Data Record

The effects of global climate change are fast becoming politically, sociologically, and personally important: increasing storm frequency and intensity, lengthening cycles of drought and flood, expanding desertification and soil salinization. A vital asset in the analysis of climate change on a global basis is the 34-year record of Landsat imagery. In recognition of its increasing importance, a detailed analysis of the Landsat observation coverage within the US archive was commissioned. Results to date indicate some unexpected gaps in the US-held archive. Fortunately, throughout the Landsat program, data have been downlinked routinely to International Cooperator (IC) ground stations for archival, processing, and distribution. These IC data could be combined with the current US holdings to build a nearly global, annual observation record over this 34-year period. Today, we have inadequate information as to which scenes are available from which IC archives. Our best estimate is that there are over four million digital scenes in the IC archives, compared with the nearly two million scenes held in the US archive. This vast pool of Landsat observations needs to be accurately documented, via metadata, to determine the existence of complementary scenes and to characterize the potential scope of the global Landsat observation record. Of course, knowing the extent and completeness of the data record is but the first step. It will be necessary to assure that the data record is easy to use, internally consistent in terms of calibration and data format, and fully accessible in order to fully realize its potential.

Arvidson, T.↗
DOE OSTI2024

Efficient distributed continual learning for steering experiments in real-time

Deep learning has emerged as a powerful method for extracting valuable information from large volumes of data. However, when new training data arrives continuously (i.e., is not fully available from the beginning), incremental training suffers from catastrophic forgetting (i.e., new patterns are reinforced at the expense of previously acquired knowledge). Training from scratch each time new training data becomes available would result in extremely long training times and massive data accumulation. Rehearsal-based continual learning has shown promise for addressing the catastrophic forgetting challenge, but research to date has not addressed performance and scalability. To fill this gap, we propose an approach based on a distributed rehearsal buffer that efficiently complements data-parallel training on multiple GPUs to achieve high accuracy, short runtime, and scalability. It leverages a set of buffers (local to each GPU) and uses several asynchronous techniques for updating these local buffers in an embarrassingly parallel fashion, all while handling the communication overheads necessary to augment input minibatches using unbiased, global sampling. We further propose a generalization of rehearsal buffers to support both classification and generative learning tasks, as well as more advanced rehearsal strategies (notably Dark Experience Replay, leveraging knowledge distillation). We illustrate this approach with a real-life HPC streaming application from the domain of ptychographic image reconstruction. Furthermore, we run extensive experiments on up to 128 GPUs of the ThetaGPU supercomputer to compare our approach with baselines representative of training-from-scratch (the upper bound in terms of accuracy) and incremental training (the lower bound). Results show that rehearsal-based continual learning achieves a top-5 validation accuracy close to the upper bound, while simultaneously exhibiting a runtime close to the lower bound.

Asynchronous data management↗
DOE OSTI2022

Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms

A recent expert elicitation showed that model validation remains one of the largest barriers for commercial wind farm control deployment. The Gaussian-shaped wake deficit model has grown in popularity in wind farm field experiments, yet its validation for larger farms and throughout annual operation remains limited. This article addresses this scientific gap, providing a model comparison of the Gaussian wind farm model with historical data of three offshore wind farms. The energy ratio is used to quantify the model’s accuracy. We assume a fixed turbulence intensity of $I_∞$ = 6% and a standard deviation on the inflow wind direction of $σ_{wd}$ = 3° in our Gaussian model. First, we demonstrate the non-uniqueness issue of $I_∞$ and $σ_{wd}$, which display a waterbed effect when considering the energy ratios. Second, we show excellent agreement between the Gaussian model and historical data for most wind directions in the Offshore Windpark Egmond aan Zee (OWEZ) and Westermost Rough wind farms (36 and 35 wind turbines, respectively) and wind turbines on the outer edges of the Anholt wind farm (110 turbines). Turbines centrally positioned in the Anholt wind farm show larger model discrepancies, likely due to deep-array effects that are not captured in the model. A second source of discrepancy is hypothesized to be inflow heterogeneity. In future work, the Gaussian wind farm model will be adapted to address those weaknesses.

17 WIND ENERGY↗
DOE OSTI2026

Leveraging generative artificial intelligence to bridge domain gaps in wind turbine research

A central challenge in wind turbine health monitoring is the scarcity of real-world data due to limited instrumentation, leading researchers to rely on simulation models that often suffer from reduced fidelity. However, even within simulation environments, discrepancies arise because of modeling assumptions, and configuration fidelities, creating domain gaps that limit the transferability of learned representations. Here, to investigate domain translation under controlled conditions, this project explores the use of generative artificial intelligence, specifically cycle-consistent generative adversarial networks (CGANs), to bridge the gap between OpenFAST simulation models representing 1.5 MW and 5 MW wind turbines. A physics-informed CGAN architecture is introduced, where a simplified turbine tower dynamics model is incorporated into the training loss to ensure physically consistent outputs. Quantitative results showed moderate to high agreement in frequency-domain features. Incorporating the physics-informed loss function improved the R 2 values by 30%, reduced the RMSE from 1.39 to 1.1 m/s 2 , and reduced training time by 82%. Furthermore, under increased turbulence intensity (IEC Category A), the RMSE remained stable at approximately 1.1 m/s 2 . While the present study is entirely simulation-based, it establishes a pipeline for evaluating physics-informed generative domain translation, which may serve as a foundation for future simulation-to-reality validation studies.

17 WIND ENERGY↗
DOE OSTI2020

Sensitivity Studies, Gap Analysis, and Benchmark Experiment Optimization for Reactor Physics and Criticality Safety Applications

Many new reactor designs, such as advanced reactors and micro reactors, have materials that lack nuclear data validation. This is also true for many other applications in criticality safety and global security. Both differential and integral experiments are needed to validate cross-section data. Without this, a user cannot have confidence in the predicted results of a radiation-transport code. This work describes an approach called ARCHIMEDES (Application Relevant Critical/Subcritical HEU/Pu-based Integral Measurements for Enhancing Data and Evaluating Sensitivities) to design new criticality experiments that have similar k eff cross-section sensitivities to an application of interest. This process involves simulations to generate cross-section sensitivities to a parameter of interest (such as k eff ), a gap analysis to determine which existing benchmarks are most similar to the application, and an experiment optimization. Recently, there has been a great deal of interest in the reactor physics community on advanced reactors, micro reactors, and accelerator driven systems (ADS). This work will apply the described method to specific examples in this area. The focus of this work will be on the sensitivity study and gap analysis, while future work will include experiment design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1992

Comparison of modeled backscatter with SAR data at P-band

In recent years several analytical models were developed to predict microwave scattering by trees and forest canopies. These models contribute to the understanding of radar backscatter over forested regions to the extent that they capture the basic interactions between microwave radiation and tree canopies, understories, and ground layers as functions of incidence angle, wavelength, and polarization. The Santa Barbara microwave model backscatter model for woodland (i.e. with discontinuous tree canopies) combines a single-tree backscatter model and a gap probability model. Comparison of model predictions with synthetic aperture radar (SAR) data and L-band (lambda = 0.235 m) is promising, but much work is still needed to test the validity of model predictions at other wavelengths. The validity of the model predictions at P-band (lambda = 0.68 m) for woodland stands at our Mt. Shasta test site was tested.

Wang, Yong↗
DOE OSTI2020

Measurement of the cross section for electroweak production of a Z boson, a photon and two jets in proton-proton collisions at $\sqrt{s} =$ 13 TeV and constraints on anomalous quartic couplings

A measurement is presented of the cross section for electroweak production of a Z boson and a photon in association with two jets (Zγjj) in proton-proton collisions. The Z boson candidates are selected through their decay into a pair of electrons or muons. The process of interest, electroweak Zγjj production, is isolated by selecting events with a large dijet mass and a large pseudorapidity gap between the two jets. The measurement is based on data collected at the CMS experiment at $ \sqrt{s} $ = 13 TeV, corresponding to an integrated luminosity of 35.9 fb$^{−1}$. The observed significance of the signal is 3.9 standard deviations, where a significance of 5.2 standard deviations is expected in the standard model. These results are combined with published results by CMS at $ \sqrt{s} $ = 8 TeV, which leads to observed and expected respective significances of 4.7 and 5.5 standard deviations. From the 13 TeV data, a value is obtained for the signal strength of electroweak Zγjj production and bounds are given on quartic vector boson interactions in the framework of dimension-eight effective field theory operators.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Adaptive anomaly detection for identifying attacks in cyber-physical systems: A systematic literature review

Modern cyberattacks in cyber-physical systems (CPS) rapidly evolve and cannot be deterred effectively with most current methods, which focus on characterizing past threats. Adaptive anomaly detection (AAD) is among the most promising techniques to detect evolving cyberattacks, with an emphasis on fast data processing and model adaptation. AAD has been researched extensively; however, to the best of our knowledge, our work is the first systematic literature review (SLR) on current research in this field. We present a comprehensive SLR, gathering 397 relevant papers and systematically analyzing 65 of them (47 research and 18 survey papers) on AAD in CPS from 2013 to November 2023. We introduce a novel taxonomy considering attack types, CPS application, learning paradigm, data management, and algorithms. Our findings show that most studies addressed either model adaptation or data processing, but rarely both simultaneously. This indicates a research gap in fully adaptive solutions. We also categorize algorithms, datasets, and attack characteristics, and summarize strengths and weaknesses across the literature. Our review provides a structured and accessible reference for researchers and practitioners, offering insights into key trends and highlighting limitations in current approaches. Finally, we outline several future research directions, including the need for integrated real-time processing and adaptive learning, explainability, and uncertainty quantification in AAD for CPS.

Adaptation↗
DOE OSTI2023

The role of social support on midwestern farmers’ willingness to grow perennial bioenergy crops

The lack of farmers' willingness to grow perennial bioenergy crops (PBCs) presents a critical barrier to the emergence of cellulosic biofuel production. The willingness relies on a complex network of economic, environmental, and social drivers, among which the influence of social factors (e.g., the influence of neighborhood, community, and communication) is less understood. This study addresses this knowledge gap via a survey analysis of midwestern farmers. The survey data are analyzed through ordinary least square regression and structural equation model, which together investigate the individual and interactive impacts of multiple factors on farmers' decisions to adopt PBCs. Based on a farm-scale analysis, six statistically significant predictors of farmer willingness to grow PBCs are identified: perception of PBCs' environment benefits, education level, willingness to take risks, familiarity with PBCs, portion of peers already growing PBCs, and support of biorefineries locating in the local community. Among these, the latter three predictors are social support variables. It is found that familiarity with the crops is the most significant predictor of willingness; familiarity is also an important intermediate variable that mediates the influence of many other predictors. In addition, peer adoption can both directly and indirectly affect willingness via its influence on familiarity. Furthermore, these findings suggest that it is a pressing need to improve farmers’ knowledge of PBCs to promote the adoption of such crops.

09 BIOMASS FUELS↗
DOE OSTI2020

Onset of magma ocean solidification on Mars inferred from Mn-Cr chronometry

The mantle of Mars probably differentiated through the crystallization of a magma ocean during the first tens of million years (Ma) of Solar System evolution. However, the exact timescale of large-scale silicate differentiation of the martian mantle is debated, and in particular, it remains unclear when differentiation commenced. In this paper, we applied the short-lived 53 Mn- 53 Cr system to martian meteorites in order to date the onset of large-scale mantle differentiation on Mars. The new Cr isotope data demonstrate that martian meteorites exhibit no resolvable radiogenic 53 Cr variations, and instead have a uniform +20.2±1.2 (95% conf.) parts-per-million excess in 53 Cr/ 52 Cr relative to the terrestrial mantle. The investigated groups of martian meteorites are lithologically varied and derive from diverse mantle sources that probably had variable Mn/Cr. Hence, the lack of 53 Cr variability among martian meteorites demonstrates that silicate differentiation on Mars occurred after the extinction of 53 Mn. Provided that the sources of the martian meteorites have Mn/Cr variations that are typical of the terrestrial planets, this result implies that the onset of large-scale silicate differentiation must have occurred later than 20±5 Ma after Solar System formation. The onset of silicate differentiation on Mars inferred here is significantly later than time estimates for segregation of the martian core which conservatively occurred within <10 Ma after Solar System formation. Thus, the new Mn-Cr data imply that there was a small, but resolvable, time gap of at least 5 Ma between core formation and magma ocean solidification on Mars. If the age of core segregation is taken at face value, our results imply that the martian magma ocean remained mostly molten over several Ma. This inferred longevity of the magma ocean is inconsistent with thermal models predicting rapid (<1 Ma) solidification of the martian magma ocean. Although there is currently no unique solution to this conundrum, our results can potentially be explained by a protracted history of impact bombardment that delayed differentiation in a shallow magma ocean on Mars, or perhaps more readily, by the presence of an early and dense atmosphere that acted as an insulator and prevented the magma ocean from cooling quickly.

58 GEOSCIENCES↗
DOE OSTI2023

Charge Carrier Transport in Iron Pyrite Thin Films: Disorder-Induced Variable-Range Hopping

Here, the origin of p-type conductivity and the mechanism responsible for low carrier mobility were investigated in pyrite (FeS 2 ) thin films. Temperature-dependent resistivity measurements (10-400 K) were performed on polycrystalline and nanostructured thin films prepared by three different methods: (1) spray pyrolysis, (2) hot-injection synthesized and spin-coated nanocubes, and (3) pulsed laser deposition. The films have a high hole density (10 18 -10 19 ) cm -3 and low mobility (0.1-4 cm 2 V -1 s -1 ) regardless of the method used for their preparation. The charge transport mechanism is determined to be thermally activated conduction (TAC) at near room temperature, with Mott-type variable-range hopping (VRH) of holes via localized states occurring at lower temperatures. The density functional theory (DFT) predicts that sulfur vacancy induces localized defect states within the band gap and the charge remains localized around the defect. The data indicates that the electronic properties including hopping transport in pyrite thin films can be correlated to sulfur vacancy-related defects. The results provide insights into the electronic properties of pyrite thin films and their implications for charge transport.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Plasmon-Induced Hot-Carrier Excited-State Dynamics in Plasmonic Semiconductor Nanocrystals

The variance of carrier relaxation pathways for WO 3–x plasmonic semiconductor nanocrystals (PSNCs) is monitored by transient absorption spectroscopy following excitation of the localized surface plasmon resonance (LSPR) versus the optical band gap (E g,opt ). Excitation of the LSPR leads to efficient hot carrier population above the Fermi level in WO 3–x via Landau damping, in analogy to noble metal LSPR relaxation mechanisms. Hot carrier depopulation occurs on the femtosecond timescale, observed as the concomitant recovery of an LSPR bleach with the appearance of discrete interband and intraband photoinduced absorption features. Further, by comparison, the direct excitation of E g,opt results in trion recombination at donor–acceptor sites within the WO 3–x NC, consistent with exciton decay dynamics observed for typical wide-band-gap semiconductor NCs. From the analysis of pump power dependency data, a hot-carrier electron–phonon coupling constant of 1.47 × 10 11 J K –1 s –1 cm –3 is extracted. The direct comparison of the decay dynamics following E g,opt versus LSPR excitation confirms that the observed plasmon in trioctylphosphine oxide passivated, spherical WO 3–x is a resonance state in which hot carriers are generated only from excitation on resonance with the LSPR frequency. This study on WO 3–x PSNCs provides a toolset that can be used to evaluate the role of hot carriers following LSPR excitation of n-type, plasmonic transparent conducting oxide NCs, where enhancement of photocatalysis, photovoltaic performance, and optical enhancement has been reported.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Rising infrastructure inequalities accompany urbanization and economic development

Impending global urban population growth is expected to occur with considerable infrastructure expansion. However, our understanding of attendant infrastructure inequalities is limited, highlighting a critical knowledge gap in the sustainable development implications of urbanization. Using satellite data from 2000 to 2019, we examine country-level population-adjusted biases in infrastructure distribution within and between regions of varying urbanization levels and derive four key findings. First, we find long-run positive associations between infrastructure inequalities and both urbanization and economic development. Second, our estimates highlight increasing infrastructure inequalities across most of the countries examined. Third, we find greater future infrastructure inequality increases in the global south, where inequalities will rise more in countries with substantial urban primacy. Fourth, we find that infrastructure inequality may evolve differently than economic inequalities. Overall, advancing sustainable development vis-à-vis urbanization and economic development will require intentional infrastructure planning for spatial equity.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Two excited-state datasets for quantum chemical UV-vis spectra of organic molecules

Abstract We present two open-source datasets that provide time-dependent density-functional tight-binding (TD-DFTB) electronic excitation spectra of organic molecules. These datasets represent predictions of UV-vis absorption spectra performed on optimized geometries of the molecules in their electronic ground state. The GDB-9-Ex dataset contains a subset of 96,766 organic molecules from the original open-source GDB-9 dataset. The ORNL_AISD-Ex dataset consists of 10,502,904 organic molecules that contain between 5 and 71 non-hydrogen atoms. The data reveals the close correlation between the magnitude of the gaps between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO), and the excitation energy of the lowest singlet excited state energies quantitatively. The chemical variability of the large number of molecules was examined with a topological fingerprint estimation based on extended-connectivity fingerprints (ECFPs) followed by uniform manifold approximation and projection (UMAP) for dimension reduction. Both datasets were generated using the DFTB+ software on the “Andes” cluster of the Oak Ridge Leadership Computing Facility (OLCF).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Reinterpreting π-stacking

The nature of π–π interactions has long been debated. The term “π-stacking” is considered by some to be a misnomer, in part because overlapping π-electron densities are thought to incur steric repulsion, and the physical origins of the widely-encountered “slip-stacked” motif have variously been attributed to either sterics or electrostatics, in competition with dispersion. Here, we use quantum-mechanical energy decomposition analysis to investigate π–π interactions in supramolecular complexes of polycyclic aromatic hydrocarbons, ranging in size up to realistic models of graphene, and for comparison we perform the same analysis on stacked complexes of polycyclic saturated hydrocarbons, which are cyclohexane-based analogues of graphane. Our results help to explain the short-range structure of liquid hydrocarbons that is inferred from neutron scattering, trends in melting-point data, the interlayer separation of graphene sheets, and finally band gaps and observation of molecular plasmons in graphene nanoribbons. Analysis of intermolecular forces demonstrates that aromatic π–π interactions constitute a unique and fundamentally quantum-mechanical form of non-bonded interaction. Not only do stacked π–π architectures enhance dispersion, but quadrupolar electrostatic interactions that may be repulsive at long range are rendered attractive at the intermolecular distances that characterize π-stacking, as a result of charge penetration effects. The planar geometries of aromatic sp 2 carbon networks lead to attractive interactions that are “served up on a molecular pizza peel”, and adoption of slip-stacked geometries minimizes steric (rather than electrostatic) repulsion. The slip-stacked motif therefore emerges not as a defect induced by electrostatic repulsion but rather as a natural outcome of a conformational landscape that is dominated by van der Waals interactions (dispersion plus Pauli repulsion), and is therefore fundamentally quantum-mechanical in its origins. This reinterpretation of the forces responsible for π-stacking has important implications for the manner in which non-bonded interactions are modeled using classical force fields, and for rationalizing the prevalence of the slip-stacked π–π motif in protein crystal structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Multilayer interface tracking model of pure tungsten oxidation

Here, we present a numerical model to predict oxide scale growth on tungsten surfaces under exposure to oxygen at high temperatures. The model captures the formation of four thermodynamically-compatible oxide sublayers, WO 2 , WO 2.72 , WO 2.9 , and WO 3 , on top of the metal substrate. Oxide layer growth is simulated by tracking the oxide/oxide and oxide/metal interfaces using a sharp-interface Stefan model coupled to diffusion kinetics. The model is parameterized using selected experimental measurements and electronic structure calculations of the diffusivities of all the oxide subphases involved. We simulate oxide growth at temperatures of 600°C and above, extracting the power law growth exponents in each case, which we find to deviate from classical parabolic growth in several cases. We conduct a comparison of the model predictions with an extensive experimental data set, with reasonable agreement at most temperatures. While many gaps in our understanding still exist, this work is a first attempt at embedding the thermodynamic and kinetic complexity of tungsten oxide growth into a comprehensive mesoscale kinetic model that attempts to capture the essential features of tungsten oxidation to fill existing knowledge gaps and guide and enhance future tungsten oxidation models.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Simulating image coaddition with the Nancy Grace Roman Space Telescope – I. Simulation methodology and general results

The upcoming Nancy Grace Roman Space Telescope will carry out a wide-area survey in the near-infrared. A key science objective is the measurement of cosmic structure via weak gravitational lensing. Roman data will be undersampled, which introduces new challenges in the measurement of source galaxy shapes; a potential solution is to use linear algebra-based coaddition techniques such as imcom that combine multiple undersampled images to produce a single oversampled output mosaic with a desired ‘target’ point spread function (PSF). We present here an initial application of imcom to 0.64 square degrees of simulated Roman data, based on the Roman branch of the Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) simulation. We show that imcom runs successfully on simulated data that includes features such as plate scale distortions, chip gaps, detector defects, and cosmic ray masks. We simultaneously propagate grids of injected sources and simulated noise fields as well as the full simulation. We quantify the residual deviations of the PSF from the target (the ‘leakage’), as well as noise properties of the output images; we discuss how the overall tiling pattern as well as Moiré patterns appear in the final leakage and noise maps. We include appendices on interpolation algorithms and the interaction of undersampling with image processing operations that may be of broader applicability. The companion paper (‘Paper II’) explores the implications for weak lensing analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Single-crystal growth and superconductivity in RbNi 2 Se 2

Herein we report the synthesis and characterization of RbNi 2 Se 2 , an analog of the iron chalcogenide superconductor Rb x Fe 2 Se 2 , via transport, angle-resolved photoemission spectroscopy, and density functional theory calculations. A superconducting transition at T c =1.20 K is identified. In the normal state, RbNi 2 Se 2 shows paramagnetic and Fermi-liquid behaviors. A large Sommerfeld coefficient yields an effective electron mass of m* ≈ 6m e . In the superconducting state, zero-field electronic specific-heat data C es can be described by a two-gap BCS model, indicating that RbNi 2 Se 2 is a possible multigap superconductor. Our density functional theory calculations and angle-resolved photoemission spectroscopy measurements demonstrate that RbNi 2 Se 2 exhibits relatively weak correlations and multiband characteristics, consistent with the multigap superconductivity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗