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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Spectrally Resolved Flux Derived from Collocated AIRS and CERES Observations and its Application in Model Validation: Clear-Sky Over the Tropic Oceans - Part I

Spectrally resolved outgoing IR flux, the integrand of the outgoing longwave radiation (OLR), has its unique value in evaluating model simulations. Here we describe an algorithm of deriving such clear-sky outgoing spectral flux through the whole IR region from the collocated Atmospheric Infrared Sounder (AIRS) and the Clouds & the Earth's Radiant Energy System (CERES) measurements over the tropical oceans. Based on the scene types and corresponding angular distribution models (ADMs) used in the CERES Single Satellite Footprint (SSF) dataset, spectrally-dependent ADMs are developed and used to estimate the spectral flux at each AIRS channel. A multivariate linear prediction scheme is then used to estimate spectral fluxes at frequencies not covered by the AIRS instrument. The whole algorithm is validated using synthetic spectra as well as the CERES OLR measurements. Using the GFDL AM2 model simulation as a case study, the application of the derived clear-sky outgoing spectral flux in model evaluation is illustrated. By comparing the observed and simulated spectral flux in 2004, compensating errors in the simulated OLR from different absorption bands can be revealed, so does the errors from frequencies within a given absorption band. Discrepancies between the simulated and observed spatial distributions and seasonal evolutions of the spectral fluxes at different spectral ranges are further discussed. The methodology described in this study can be applied to other surface types as well as cloudy-sky observations and corresponding model evaluations.

Huang, Xianglei↗
NASA NTRS2012

Detecting Falling Snow from Space

There is an increased interest in detecting and estimating the amount of falling snow reaching the Earth's surface in order to fully capture the atmospheric water cycle. An initial step toward global spaceborne falling snow algorithms includes determining the thresholds of detection for various active and passive sensor channel configurations, snow event cloud structures and microphysics, snowflake particle electromagnetic properties, and surface types. In this work, cloud resolving model simulations of a lake effect and synoptic snow event were used to determine the minimum amount of snow (threshold) that could be detected by the following instruments: the W -band radar of CloudSat, Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) Ku and Ka band, and the GPM Microwave Imager (GMI) channels from 10 to 183 plus or minus 7 GHz. Eleven different snowflake shapes were used to compute radar reflectivities and passive brightness temperatures. Notable results include: (1) the W-Band radar has detection thresholds more than an order of magnitude lower than the future GPM sensors, (2) the cloud structure macrophysics influences the thresholds of detection for passive channels, (3) the snowflake microphysics plays a large role in the detection threshold for active and passive instruments, (4) with reasonable assumptions, "the passive 166 GHz channel has detection threshold values comparable to the GPM DPR Ku and Ka band radars with approximately 0.05 g per cubic meter detected at the surface, or an approximately 0.5-1 millimeter per hr. melted snow rate (equivalent to 0.5-2 centimeters per hr. solid fluffy snowflake rate). With detection levels of falling snow known, we can focus current and future retrieval efforts on detectable storms and concentrate advances on achievable results. We will also have an understanding of the light snowfall events missed by the sensors and not captured in the global estimates.

Gail Skofronick-Jackson↗
DOE OSTI2020

Enhancement of Photoemission on p-Type GaAs Using Surface Acoustic Waves

We demonstrate that photoemission properties of p-type GaAs can be altered by surface acoustic waves (SAWs) generated on the GaAs surface due to dynamical piezoelectric fields of SAWs. Multiphysics simulations indicate that charge-carrier recombination is greatly reduced, and electron effective lifetime in p-doped GaAs may increase by a factor of 10× to 20×. It implies a significant increase, by a factor of 2× to 3×, of quantum efficiency (QE) for GaAs photoemission applications, like GaAs photocathodes. Conditions of different SAW wavelengths, swept SAW intensities, and varied incident photon energies were investigated. Essential steps in SAW device fabrication on a GaAs substrate are demonstrated, including deposition of an additional layer of ZnO for piezoelectric effect enhancement, measurements of current–voltage (I–V) characteristics of the SAW device, and ability to survive high-temperature annealing. Results obtained and reported in this study provide the potential and basis for future studies on building SAW-enhanced photocathodes, as well as other GaAs photoelectric applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Electrocatalytic Reduction of CO 2 to CO over Ag(110) and Cu(211) Modeled by Grand-Canonical Density Functional Theory

We report the results of modeling CO 2 reduction (CO 2 R) to CO over Ag(110) and Cu(211) surfaces at different applied potentials using grand-canonical density functional theory (GC-DFT), a method specifically designed to accurately model electrochemical systems. In addition to demonstrating GC-DFT’s ability to accurately model electrochemical processes, we also compare it with the computational hydrogen electrode (CHE) approach. GC-DFT predicts that the geometries of these reacting systems strongly depend on the applied potential and the Helmholtz free energies vary nonlinearly with the applied potential, which contradicts a central assumption of the CHE approach. The CHE approach neglects the change in the number of electrons on the electrode surface at different applied potentials, which reduces its accuracy as the potential changes from the potential of zero charge. Our results further demonstrate that the grand free energies of the reaction intermediates not only depend on the value of the applied potential but also on the metal surface type, adsorption site, and adsorbate. GC-DFT’s ability to predict the effect of the applied potential on adsorbate geometry enables it to evaluate different possible reaction mechanisms at different applied potentials. For instance, GC-DFT predicts that the first step of CO2R likely switches from proton-coupled electron transfer to sequential electron transfer and then proton transfer at more reducing potentials, a result that cannot be determined by the CHE because it assumes that all electron transfers are coupled to proton transfers and neglects the effect of the applied potential on the adsorbate geometry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Transition Metal Dichalcogenides as Effective Catalysts for High-Rate Lithium–Sulfur Batteries

In today’s renaissance of high energy density secondary batteries, lithium-sulfur (Li-S) batteries represent one of the most promising candidates for the next generation of renewable energy storage systems, due to sulfur’s high theoretical specific capacity of 1675 mAh g -1 and high earth abundance. However, despite decades of study, the issues associated with capacity fade via the polysulfide shuttle and sluggish kinetics remain. Through a rigorous and detailed electrochemical study of lithium polysulfides via rotating disk electrode voltammetry (RDE), we have investigated the kinetics of the redox reactions, and explored candidate catalysts to potentially overcome/mitigate the polysulfide shuttle effect. From these RDE studies, supported by comprehensive electronic-structure calculations of conversion-type surface reactions, we determined that WSe 2 can effectively catalyze the polysulfide redox reaction, though further studies are necessary to improve overall Li-S battery performance.

25 ENERGY STORAGE↗
DOE OSTI2022

Investigation of long-term light stability of negative charge injected into oxide-nitride-oxide passivation stack of crystalline silicon solar cells

A negatively charged oxide-nitride-oxide stack for field-effect passivation of crystalline silicon solar cells is discussed. The negative charge was injected into the stack by a plasma charge injection technology. Charge stability was studied by exposing samples to AM1.5 simulation visible light and full-spectrum light at temperatures ranging from 55 to 78 °C for up to 300 h. Charge injection and loss were quantified based on shifts in the flatband voltage of capacitance–voltage curves measured with a mercury probe. The most probable mechanism of charge loss was found to be diffusion of negative charged hydrogen atoms through nitride and bottom oxide. The optimum recipe for each layer of the stack was investigated to minimize the loss of injected charge. The flatband voltage decay of the optimized stack was found to fit a power-law trend, suggesting the dispersive transport of hydrogen atoms with a dispersion parameter of ~0.06–0.07. The optimized stack is projected to maintain a negative charge density of about 3.6 × 10 12 cm –2 or more after 25 years of field operation in an environment such as Arizona, which would be sufficient for field-effect passivation under one-sun illumination. Furthermore, the high stability of the negative injected charge makes the plasma charging technology a safer and lower cost alternative to Al 2 O 3 -passivation technology commonly used to passivate p-type surfaces.

14 SOLAR ENERGY↗
DOE OSTI2025

Unprecedented Beaufort Sea ice loss in late summer 2021 and its relationship to an extended period of unusually stormy weather

Previous case studies have linked cyclone-induced atmospheric forcing and/or upper-ocean processes to notable Arctic sea ice loss events in the summers of 2012 and 2016. This study examines a more recent and noteworthy case in late summer 2021 in which substantial sea ice loss followed a period of surface meteorological extremes in the Beaufort Sea region of the Arctic. We focus on the period from mid-August to mid-September 2021 that coincided with the Office of Naval Research THINICE Pilot Field Campaign and investigate stormy and windy conditions with respect to air-sea processes impacting sea ice conditions. We find that during the stormy first half of the campaign, cyclone-induced energy fluxes into the marginal ice zone and surrounding waters preconditioned the ice pack for more rapid melt later in the campaign. The second half of the campaign, in contrast, was marked by non-cyclone wind events that enhanced turbulent (namely sensible) heat fluxes into the ice and upper ocean that increased melt. Moreover, this latter period had enhanced advection of the Beaufort Sea ice pack into above-freezing waters, increasing bottom melt to >1 cm d −1 over the remainder of the campaign. While findings are shown to vary by surface type and at relatively small (i.e. ice-floe) scales, insights are offered on the roles of late summer coupled processes on rapid ice loss events in today’s Arctic environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Measurements of angle-resolved reflectivity of PTFE in liquid xenon with IBEX

Abstract Liquid xenon particle detectors rely on excellent light collection efficiency for their performance. This depends on the high reflectivity of polytetrafluoroethylene (PTFE) at the xenon scintillation wavelength of 178 nm, but the angular dependence of this reflectivity is not well-understood. IBEX is designed to directly measure the angular distribution of xenon scintillation light reflected off PTFE in liquid xenon. These measurements are fully described by a microphysical reflectivity model with few free parameters. Dependence on PTFE type, surface finish, xenon pressure, and wavelength of incident light is explored. Total internal reflection is observed, which results in the dominance of specular over diffuse reflection and a reflectivity near 100% for high angles of incidence.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Semi-Analytic Functions to Calculate the Deposition Coefficients for Ice Crystal Vapor Growth in Bin and Bulk Microphysical Models

Numerical cloud models require estimates of the vapor growth rate for ice crystals. Current bulk and bin microphysical parameterizations generally assume that vapor growth is diffusion limited, though some parameterizations include the influence of surface attachment kinetics through a constant deposition coefficient. A parameterization for variable deposition coefficients is provided herein. The parameterization is an explicit function of the ambient ice supersaturation and temperature, and an implicit function of crystal dimensions and pressure. The parameterization is valid for variable surface types including growth by dislocations and growth by step nucleation. Deposition coefficients are predicted for the two primary growth directions of crystals, allowing for the evolution of the primary habits. Comparisons with benchmark calculations of instantaneous mass growth indicate that the parameterization is accurate to within a relative error of 1%. Parcel model simulations using Lagrangian microphysics as a benchmark indicate that the bulk parameterization captures the evolution of mass mixing ratio and fall speed with typical relative errors of less than 10%, whereas the average axis lengths can have errors of up to 20%. The bin model produces greater accuracy with relative errors often less that 10%. The deposition coefficient parameterization can be used in any bulk and bin scheme, with low error, if an equivalent volume spherical radius is provided.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Integrating LEO and GEO Observations: Toward Optimal Summertime Satellite Precipitation Retrieval

Abstract Reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for understanding the Earth’s hydrological cycle. Precipitation estimation over land and coastal regions is necessary for addressing the high degree of spatial heterogeneity of water availability and demand, and for resolving the extremes that modulate and amplify hazards such as flooding and landslides. Advancements in computation power along with unique high spatiotemporal and spectral resolution data streams from passive meteorological sensors aboard geosynchronous Earth-orbiting (GEO) and low Earth-orbiting (LEO) satellites offer exciting opportunities to retrieve information about surface precipitation phenomena using data-driven machine learning techniques. In this study, the capabilities of U-Net–like architecture are investigated to map instantaneous, summertime surface precipitation intensity at the spatial resolution of 2 km. The calibrated brightness temperature products from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) radiometer are combined with multispectral images (visible, near-infrared, and infrared bands) from the Advanced Baseline Imager (ABI) aboard the GOES-R satellites as main inputs to the U-Net–like precipitation algorithm. Total precipitable water and 2-m temperature from the Global Forecast System (GFS) model are also used as auxiliary inputs to the model. The results show that the U-Net–like algorithm can capture fine-scale patterns and intensity of surface precipitation at high spatial resolution over stratiform and convective precipitation regimes. The evaluations reveal the potential of extracting relevant, high spatial features over complex surface types such as mountainous regions and coastlines. The algorithm allows users to interpret the inputs’ importance and can serve as a starting point for further exploration of precipitation systems within the field of hydrometeorology.

Meteorology & Atmospheric Sciences↗
DOE OSTI2022

Overview of the MOSAiC expedition: Snow and sea ice

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Python-Cubit ® Enhancement Scripts: 16.14

The Python-Cubit ® enhancement code base is intended to be used as an extension to already existing Cubit ® functionality. It provides the user with a number of functionalities that are either currently outside the realm of the python functions which Cubit ® supplies internally (such as vector math), or that are comprised of commonly used combinations of already existing python functionalities (such as removing a full round from a slot cut). The foreseen style of use for many of these scripts is to utilize volume names and geometric data such as surface area, surface type, etc. as a way to filter out geometries, and provide a powerful id-less method. These filters combined with a number of already existing python functionalities such as the set() operator and zip() function can be used to operate on many geometries at a single time without a need for the user to manually select them or use their ids. Please refer to the example given in the documents examples section for a demonstration of the work flow.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Areal-Averaged Surface Albedo (ArealAveAlb) Value-Added Product

The surface albedo plays an important role in the Earth's radiation balance. This report provides information on how to estimate areal-averaged surface albedo from ground-based measurements of solar radiation at five wavelengths (415,500,615,675, and 870 nm). The report first explains why this estimation is important and challenging. Then the report defines both the required and complimentary inputs for the corresponding Areal-Averaged Surface Albedo (ArealAveAlb) Value-Added Product (VAP) and highlights the major outputs of this VAP. The following section explains how estimation of the areal-averaged surface albedo at four wavelengths (500,615,675, and 870 nm) can be performed for different surface types, including those partly covered by snow. The final section provides three examples that illustrate the VAP’s performance and emphasizes the benefits of complementary inputs, such as distinct cloud types and precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

WHYMSIE-SGP Field Campaign Report

Through a partnership between the U.S. National Aeronautics and Space Administration (NASA) and the National Oceanic and Atmospheric Administration (NOAA), this field campaign demonstrated first-of-its-kind hyperspectral microwave airborne measurements (Conical Scanning Millimeter-wave Imaging Radiometer [CoSMIR-H]). It was complemented by other passive (infrared, visible) and active (lidar) sensors onboard the NASA ER-2 aircraft. Serving as a future NASA planetary boundary-layer (PBL) mission prototype, the Westcoast and Heartland Hyperspectral Microwave Intensive Experiment (WH 2 yMSIE, formerly WHyMSIE) aimed to capture a wide variety of thermodynamic, moisture, and PBL regimes across a variety of surface types. The intensive operating period of the campaign spanned from October 22, 2024, through November 15, 2024, and included a large network of collaborators.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Deep Learning and Photogrammetric Reconstruction for Automated Crack Detection and Dimensional Measurement in Mining Operations

Surface crack detection and dimensional measurement at active mining sites present significant safety and operational challenges. Manual inspection methods are labor-intensive, spatially incomplete, and expose personnel to hazardous environments, while existing automated approaches have been developed primarily for concrete civil infrastructure and have not been validated on the complex, variable surfaces characteristic of mining environments. This dissertation presents an automated pipeline that integrates deep learning semantic segmentation with Structure-from-Motion photogrammetry to detect surface cracks and measure their aperture, length, and vertical displacement from standard RGB imagery acquired during routine Uncrewed Aerial Vehicle (UAV) survey operations, without requiring additional sensor hardware or manual measurement. The pipeline combines a U-Net architecture with an EfficientNet-B0 encoder, pretrained on the SDNET2018 concrete crack dataset and fine-tuned on a mining-specific dataset spanning laboratory concrete specimens, coal refuse impoundment embankments, and post-blast limestone quarry benches. Photogrammetric reconstruction is performed using COLMAP Structure-from-Motion and Multi-View Stereo, with crack segmentation masks projected into the reconstructed point cloud to enable three-dimensional vertical displacement measurement through local plane fitting and bimodal surface detection. The pipeline was validated across 36 controlled laboratory specimens at three imaging distances and four vertical displacement levels, achieving aperture measurement RMSE of 0.047 cm and R² of 0.954, and vertical displacement RMSE of 0.140 cm and R² of 0.966, against independent caliper measurements. Field application at a coal refuse impoundment in southwestern Pennsylvania detected 71 crack components across the embankment crest, with a dominant longitudinal crack exhibiting aperture values reaching 28 cm and a 95th percentile vertical displacement of 35.53 cm, consistent in magnitude and spatial distribution with simultaneously acquired LiDAR-derived estimates. Application across four post-blast limestone quarry bench datasets in California successfully characterized blast-induced fracture networks at ground sampling distances ranging from 0.59 to 1.23 cm/pixel, with detected crack geometries physically consistent with observable surface conditions at each site. The results demonstrate that deep learning-based crack detection and photogrammetric measurement can be integrated into routine UAV inspection workflows at mining sites, providing repeatable, scalable, and quantitative crack characterization across surface types, crack scales, and displacement magnitudes not previously addressed in the literature. The pipeline requires no dedicated surveying equipment beyond the UAV platforms already deployed at mine sites for survey and monitoring purposes, supporting practical adoption within existing operational workflows.

Crack detection, Dimensional Measurement↗
DOE OSTI2025

Best estimate of the planetary boundary layer height from multiple remote sensing measurements

Remote sensing measurements have been widely used to estimate the planetary boundary layer height (PBLHT). Each remote sensing approach offers unique strengths and faces different limitations. In this study, we use machine learning (ML) methods to produce a best-estimate PBLHT (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements at the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observatory. Three ML models – random forest (RF) classifier, RF regressor, and light gradient-boosting machine (LightGBM) – were trained on a dataset from 2017 to 2023 that included radiosonde, various remote sensing PBLHT estimates, and atmospheric meteorological conditions. Evaluations indicated that PBLHT-BE-ML from all three models improved alignment with the PBLHT derived from radiosonde data (PBLHT-SONDE), with LightGBM demonstrating the highest accuracy under both stable and unstable boundary layer conditions. Feature analysis revealed that the most influential input features at the SGP site were the PBLHT estimates derived from (a) potential temperature profiles retrieved using Raman lidar (RL) and atmospheric emitted radiance interferometer (AERI) measurements (PBLHT-THERMO), (b) vertical velocity variance profiles from Doppler lidar (PBLHT-DL), and (c) aerosol backscatter profiles from micropulse lidar (PBLHT-MPL). The trained models were then used to predict PBLHT-BE-ML at a temporal resolution of 10 min, effectively capturing the diurnal evolution of PBLHT and its significant seasonal variations, with the largest diurnal variation observed over summer at the SGP site. We applied these trained models to data from the ARM Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) field campaign (EPC), where the PBLHT-BE-ML, particularly with the LightGBM model, demonstrated improved accuracy against PBLHT-SONDE. Analyses of model performance at both the SGP and EPC sites suggest that expanding the training dataset to include various surface types, such as ocean and ice-covered areas, could further enhance ML model performance for PBLHT estimation across varied geographic regions.

Zhang, Damao [Pacific Northwest National Laborator↗