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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 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

It's All Relative: A Validation of Radiation Quality Comparison Metrics

Historically, the relative biological effectiveness (RBE) has been calculated to quantify the difference between heavy ion and gamma ray radiation. The RBE is then applied to gamma ray data to predict the effects of heavy ions in humans. The RBE is an iso-effect dose-to-dose ratio which, due to its counterintuitive nature, has been commonly miscalculated as an iso-dose effect-to-effect ratio. A paper recently published by Shuryak et al described this second measure intentionally for the first time in 2017, referring to it as the radiation effects ratio (RER). In this study, we utilized simulations to test the ability of both the RBE and the RER to predict known heavy ion effects. RBEs and RERs were calculated using mouse data from Chang et al, and the ability of the RBE and RER to predict the heavy ion data from which they were calculated was verified. Statistical transformations often utilized during data analysis were applied to the gamma and heavy ion data to determine whether RBE and RER are each uniquely defined measures. Scale changes are expected when translating effects from mice to humans and between human populations; gamma and heavy ion data were transformed to represent potential scale changes. The ability of the RBE and RER to predict the transformed heavy ion data from the transformed gamma data was then tested. The RBE but not the RER was uniquely defined after all statistical transformations. The RBE correctly predicted the scale-transformed heavy ion data, while the RER did not. This presentation describes potential implications for both metrics in light of these findings.

Chappell, L. J.↗
DOE OSTI2022

Using soil library hyperspectral reflectance and machine learning to predict soil organic carbon: Assessing potential of airborne and spaceborne optical soil sensing

Soil organic carbon (SOC) is a key variable to determine soil functioning, ecosystem services, and global carbon cycles. Spectroscopy, particularly optical hyperspectral reflectance coupled with machine learning, can provide rapid, efficient, and cost-effective quantification of SOC. However, how to exploit soil hyperspectral reflectance to predict SOC concentration, and the potential performance of airborne and satellite data for predicting surface SOC at large scales remain relatively underknown. Here, this study utilized a continental-scale soil laboratory spectral library (37,540 full-pedon 350–2500 nm reflectance spectra with SOC concentration of 0–780 g·kg –1 across the US) to thoroughly evaluate seven machine learning algorithms including Partial-Least Squares Regression (PLSR), Random Forest (RF), K-Nearest Neighbors (KNN), Ridge, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) along with four preprocessed spectra, i.e. original, vector normalization, continuum removal, and first-order derivative, to quantify SOC concentration. Furthermore, by using the coupled soil-vegetation-atmosphere radiative transfer model, we simulated twelve airborne and spaceborne hyper/multi-spectral remote sensing data from surface bare soil laboratory spectra to evaluate their potential for estimating SOC concentration of surface bare soils. Results show that LSTM achieved best predictive performance of quantifying SOC concentration for the whole data sets (R 2 = 0.96, RMSE = 30.81 g·kg –1 ), mineral soils (SOC ≤ 120 g·kg –1 , R 2 = 0.71, RMSE = 10.60 g·kg –1 ), and organic soils (SOC > 120 g·kg –1 , R 2 = 0.78, RMSE = 62.31 g·kg –1 ). Spectral data preprocessing, particularly the first-order derivative, improved the performance of PLSR, RF, Ridge, KNN, and ANN, but not LSTM or CNN. We found that the SOC models of mineral and organic soils should be distinguished given their distinct spectral signatures. Finally, we identified that the shortwave infrared is vital for airborne and spaceborne hyperspectral sensors to monitor surface SOC. This study highlights the high accuracy of LSTM with hyperspectral/multispectral data to mitigate a certain level of noise (soil moisture <0.4 m 3 ·m –3 , green leaf area < 0.3 m 2 ·m –2 , plant residue <0.4 m 2 ·m –2 ) for quantifying surface SOC concentration. Forthcoming satellite hyperspectral missions like Surface Biology and Geology (SBG) have a high potential for future global soil carbon monitoring, while high-resolution satellite multispectral fusion data can be an alternative.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The 2021 update of the EPA’s adverse outcome pathway database

The EPA developed the Adverse Outcome Pathway Database (AOP-DB) to better characterize adverse outcomes of toxicological interest that are relevant to human health and the environment. Here we present the most recent version of the EPA Adverse Outcome Pathway Database (AOP-DB), version 2. AOP-DB v.2 introduces several substantial updates, which include automated data pulls from the AOP-Wiki 2.0, the integration of tissue-gene network data, and human AOP-gene data by population, semantic mapping and SPARQL endpoint creation, in addition to the presentation of the first publicly available AOP-DB web user interface. Potential users of the data may investigate specific molecular targets of an AOP, the relation of those gene/protein targets to other AOPs, cross-species, pathway, or disease-AOP relationships, or frequencies of AOP-related functional variants in particular populations, for example. Version updates described herein help inform new testable hypotheses about the etiology and mechanisms underlying adverse outcomes of environmental and toxicological concern.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Aboveground Biomass Estimation Using NISAR Simulated ALOS-2 Time Series Data

Aboveground biomass (AGB) is a critical parameter to better understand the global carbon cycle and to develop sustainable forest management. However, a large uncertainty prevails. L-band SAR data have demonstrated strong potential to accurately retrieve AGB over low-biomass regions (<100 Mg ha-1). The upcoming NASA-ISRO Synthetic Aperture Radar mission will collect data at L- and S-band over earth’s landmass with a repeat period of 12 days, allowing us to have ample data for monitoring biomass and its dynamics. One of the key science requirements of the mission is to produce annual AGB maps at 1-ha resolution with RMS accuracy of 20 Mg/ha for 80 percentage of area over low-biomass regions in Calibration/Validation sites. The NISAR biomass algorithm will generate AGB maps based on the parameterization of semi-empirical model along with NISAR time-series dual pol data (HH and HV). To calibrate and validate the model for mission requirements, the mission will use reference estimates of AGB produced from ground inventory plots and airborne LiDAR data collected over selected sites distributed across different global ecoregions. This paper presents the initial results of the calibration/validation of the NISAR AGB retrieval algorithm over the Lenoir Landing (LENO), Alabama, USA site using NISAR simulated ALOS-2 time series data. Five multi-temporal dual-pol HH and HV NISAR Simulated ALOS 2 data collections were used as input to assess the performance of the model. The model AGB retrieval results shows that the NISAR model was able to achieve RMS accuracy within 20 Mg/ha.

Ramachandran, Naveen [Jet Propulsion Laboratory, C↗
NASA NTRS1974

Coding/modulation trade-offs for Shuttle wideband data links

This paper describes various modulation and coding schemes which are potentially applicable to the Shuttle wideband data relay communications link. This link will be capable of accommodating up to 50 Mbps of scientific data and will be subject to a power constraint which forces the use of channel coding. Although convolutionally encoded coherent binary PSK is the tentative signal design choice for the wideband data relay link, FM techniques are of interest because of the associated hardware simplicity and because an FM system is already planned to be available for transmission of television via relay satellite to the ground. Binary and M-ary FSK are considered as candidate modulation techniques, and both coherent and noncoherent ground station detection schemes are examined. The potential use of convolutional coding is considered in conjunction with each of the candidate modulation techniques.

Batson, B. H.↗
DOE OSTI2015

Catching the role of anisotropic electronic distribution and charge transfer in halogen bonded complexes of noble gases

The systems studied in this work are gas-phase weakly bound adducts of the noble-gas (Ng) atoms with CCl{sub 4} and CF{sub 4}. Their investigation was motivated by the widespread current interest for the intermolecular halogen bonding (XB), a structural motif recognized to play a role in fields ranging from elementary processes to biochemistry. The simulation of the static and dynamic behaviors of complex systems featuring XB requires the formulation of reliable and accurate model potentials, whose development relies on the detailed characterization of strength and nature of the interactions occurring in simple exemplary halogenated systems. We thus selected the prototypical Ng-CCl{sub 4} and Ng-CF{sub 4} and performed high-resolution molecular beam scattering experiments to measure the absolute scale of their intermolecular potentials, with high sensitivity. In general, we expected to probe typical van der Waals interactions, consisting of a combination of size (exchange) repulsion with dispersion/induction attraction. For the He/Ne-CF{sub 4}, the analysis of the glory quantum interference pattern, observable in the velocity dependence of the integral cross section, confirmed indeed this expectation. On the other hand, for the He/Ne/Ar-CCl{sub 4}, the scattering data unravelled much deeper potential wells, particularly for certain configurations of the interacting partners. The experimental data can be properly reproduced only including a shifting of the repulsive wall at shorter distances, accompanied by an increased role of the dispersion attraction, and an additional short-range stabilization component. To put these findings on a firmer ground, we performed, for selected geometries of the interacting complexes, accurate theoretical calculations aimed to evaluate the intermolecular interaction and the effects of the complex formation on the electron charge density of the constituting moieties. It was thus ascertained that the adjustments of the potential suggested by the analysis of the experiments actually reflect two chemically meaningful contributions, namely, a stabilizing interaction arising from the anisotropy of the charge distribution around the Cl atom in CCl{sub 4} and a stereospecific electron transfer that occurs at the intermolecular distances mainly probed by the experiments. Our model calculations suggest that the largest effect is for the vertex geometry of CCl{sub 4} while other geometries appear to play a minor to negligible role.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Benchmark Datasets Task 2 Report: Use Case Development and Prioritization Results June 2019

The Benchmark Datasets project is a four-year effort to collect and curate building operational and environmental data, with the goal of addressing data gaps that have prevented stakeholders from achieving greater levels of energy efficiency and load flexibility. The effort is led by the U.S. Department of Energy (DOE) Building Technologies Office (BTO) with performers from National Renewable Energy Laboratory (NREL), Lawrence Berkeley National Laboratory (LBNL), Oak Ridge National Laboratory (ORNL), and Pacific Northwest National Laboratory (PNNL). Task 2 of the FY19 project involved identifying and prioritizing use cases that would benefit from targeted data collection and curation. NREL led this task with support from LBNL, ORNL, and PNNL. This report, which was submitted as an internal document to DOE in June 2019, summarizes the method used by the Task 2 team to select high-priority use cases and data needs that formed the focus of the remainder of the project. The high-priority use cases continue to inform subsequent efforts to down-select candidate data sources and engage potential partners.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Molecular Simulation of Lithium Carbonate Reactive Vapor–Liquid Equilibria Using a Deep Potential Model

We developed a first-principles machine learning model for the reactive vapor–liquid phase behavior of molten Li 2 CO 3 . The model was trained on ab initio electronic density functional theory data using the Deep Potential (DP) methodology, and its accuracy was evaluated by comparing model predictions of density and viscosity to experimental measurements. Direct coexistence simulations with the DP model over time scales of tens of nanoseconds were used to observe equilibrium dissociation of Li 2 CO 3 into CO 2 residing primarily in the vapor phase and Li 2 O which remains dissolved in the liquid. The simulations covered a range of temperatures, overall system sizes, and vapor-to-liquid volume ratios. Results were analyzed in terms of the observed chemical composition of the liquid and vapor phases, product structure, and CO 2 partial pressures. In addition, we calculated equilibrium constants for the dissociation reaction by assuming ideal-solution behavior for the liquid. As expected on the basis of thermodynamic arguments and prior experiments for this system, the observed partial pressure of CO 2 in the gas phase depends on both the temperature and the ratio of vapor to liquid volumes, while the calculated equilibrium constants only depend on temperature. DP model predictions for the equilibrium constant of the reaction are generally consistent with the available experimental measurements. Furthermore, the present study establishes the validity of the DP methodology for the description of reactive, multiphase equilibria from first principles, with possible applications to many other systems of scientific and technological interest even in the absence of relevant experimental measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Geothermal Play Fairway Analysis of the Snake River Plain: Phase 2

Play Fairway Analysis (PFA) is a methodology adapted from the petroleum industry that integrates data at the regional or basin scale to define favorable plays for exploration in a systematic fashion. Phase 2 of our Play Fairway Analysis of the Western Snake River Plain (WSRP) province in southern Idaho had three primary goals: first, to fill data gaps in critical areas in order to better define potential prospects, second, to integrate these data into new thermal and structural models, and finally, to infer the location of potential resources and drilling targets that could be validated during Phase 3. Prospects in the WSRP identified as potential target resources for Phase 3 validation include the Mountain Home region close to the Air Force Base, and the Camas Prairie. The Mountain Home region represents a blind geothermal resource in an area of high heat flow and young volcanism. The Camas Prairie is a semi-blind, structurally controlled resource in an area with indicators of magmatic heat. New geophysical data acquired at these sites includes reflection seismic, gravity and magnetic surveys, and a magnetotelluric field survey. New geochemical data collection focused on the Camas Prairie, and included the aqueous and isotope geochemistry of hot springs, cold springs, and wells (geothermal, groundwater, and irrigation). New field mapping, sampling, and basalt flow chronology was also conducted at Camas Prairie. Integrated results from Phase 1 and 2 studies suggest that the blind system near the Mountain Home Air Force Base is located at ~1.5– 2.3 km depth, and the structurally-controlled system at Camas Prairie is shallower, with upper reservoir depths perhaps only ~0.5–0.7 km.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Data Analytics for Catalysis Predictions: Are We Ready Yet?

Catalysis informatics has received tremendous attention in recent years as a tool to design catalysts and discover unique descriptors that capture the relationships between chemical properties and catalytic performance. One of the stop-gaps in understanding catalytic effects, which is often ignored and limits the deployment of data science tools, relates to the lack of uniform data. The catalytic cleavage of C–X (X= H, C, N, and O) bonds is relevant to many fundamental catalytic processes. In this Perspective, we performed data analytics on four groups of C–X cleavage reactions that are common in production, upcycling, or reactive separation: the C–C cleavage in cyclopropyl alcohol, the C–H cleavage in hydroacylation reactions, the C–O cleavage in β-O-4 linkages, and the C–N cleavage in amides, using experimental data collected from the literature to understand their underlying correlations. Experimental variables of high impact are identified for each reaction by dimensionality reduction methods. We highlight the urgent need for experimental data sets that include full details on the reaction conditions, such as reagent concentration, reaction temperature, or time in machine-readable forms. We discuss the potential improvement of the data of these reactions and promising approaches such as autonomous experiments to fill the gaps in unbiased experimental data. Finally, we also address the early stage consideration of separation aspects in the experimental design of efficient catalytic systems for these fundamental examples of chemical reactivity.

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

Milestone M6 Report: Reducing Excess Data Movement Part 1

This is the second in a sequence of three Hardware Evaluation milestones that provide insight into the following questions: What are the sources of excess data movement across all levels of the memory hierarchy, going out to the network fabric? What can be done at various levels of the hardware/software hierarchy to reduce excess data movement? How does reduced data movement track application performance? The results of this study can be used to suggest where the DOE supercomputing facilities, working with their hardware vendors, can optimize aspects of the system to reduce excess data movement. Quantitative analysis will also benefit systems software and applications to optimize caching and data layout strategies. Another potential avenue is to answer cost-benefit questions, such as those involving memory capacity versus latency and bandwidth. This milestone focuses on techniques to reduce data movement, quantitatively evaluates the efficacy of the techniques in accomplishing that goal, and measures how performance tracks data movement reduction. We study a small collection of benchmarks and proxy mini-apps that run on pre-exascale GPUs and on the Accelsim GPU simulator. Our approach has two thrusts: to measure advanced data movement reduction directives and techniques on the newest available GPUs, and to evaluate our benchmark set on simulated GPUs configured with architectural refinements to reduce data movement.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

An Imaging Interferometer for Terrestrial Remote Sensing

A prototype imaging interferometer called DASI (digital array scanned interferometer) is under development at our laboratories. Our objective is to design an instrument for remote sensing of Earth's atmosphere and surface. This paper describes the unusual characteristics of DASIs which make them promising candidates for ground and aircraft-based terrestrial measurements. These characteristics include superior signal-to-noise, design simplicity and compactness, relative to dispersion based imaging spectrometers. Perhaps one of the most notable features of DASIs is their ability to acquire an entire interferogram simultaneously without any moving optical elements. We also describe selected laboratory and ground based field measurements using the prototype DASI. A CCD detector array was placed at the DASI detector plane for wavelength coverage from 0.4 to 1.0 micron. A NICMOS MCT detector was used for coverage from 1.1 to 2.2 micron. The DASI was configured to have a spectral resolution of about 300 1/cm, a spatial field of view of 5 degrees, and a constant number of transverse spatial elements (detector dependent) for each exposure frame. Frame exposure rates were up to 0.6 Hz with the potential to achieve 5 Hz. Image cube measurements of laboratory targets and terrestrial scenes were obtained by multiple frame scanning over the field of view. These data sets reveal the potential science yields from obtaining simultaneous high resolution spatial and spectral information.

Hammer, Philip D.↗
NASA NTRS1987

The effect of photoelectrons on boom-satellite potential differences during electron beam ejection

Data taken on the SCATHA satellite at geosynchronous altitudes during periods of electron beam ejection in sunlight showed that the potential difference between an electrically isolated boom and the satellite main body was a function of beam current, energy, and boom-sun angle. The potential difference decreased as the boom area illuminated by the sun increased; the maximum and minimum potential differences were measured when minimum and maximum boom areas, respectively, were exposed to the sun. It is shown that photoelectrons, created on the boom, could be engulfed in the electrostatic field of the highly charged satellite main body. Theoretical calculations made using a simple current balance model showed that these electrons could provide a substantial discharging current to the main body and cause the observed variations in the potential difference between the main body and the booms.

Lai, Shu T.↗
DOE OSTI2019

Spatial scale affects novel and disappeared climate change projections in Alaska

The formation of novel and disappeared climates between the last glacial maximum (LGM) and the present is important to consider to understand the expansion and contraction of species niches and distributions, as well as the formation and loss of communities and ecological interactions over time. Our choice in climate data resolution has the potential to complicate predictions of the ecological impacts of climate change, since climate varies from local to global scales and this spatial variation is reflected in climate data. To address this issue, we downscaled LGM and modern (1975–2005) 30-year averaged climate data to 60-m resolution for the entire state of Alaska for 10 different climate variables, and then upsampled each variable to coarser resolutions (60 m to 12 km). We modeled the distributions of novel and disappeared climates to evaluate the locations and fractional area of novel and disappeared climates for each of our climate variables and resolutions. Generally, novel and disappeared climates were located in southern Alaska, although there were cases where some disappeared climates existed within coastal and interior Alaska. Climate resolution affected the fractional area of novel and disappeared climates in three patterns: As the spatial resolution of climate became coarser, the fractional area of novel and disappeared climates (a) increased, (b) decreased, or (c) had no explainable relationship. Overall, we found the use of coarser climate data increased the fractional area of novel and disappeared climates due to decreased environmental variability and removal of climate extremes. Our results reinforce the importance of downscaling coarse climate data and suggest that studies analyzing the effects of climate change on ecosystems may overestimate or underestimate their conclusions when utilizing coarse climate data.

Alaska↗
NASA NTRSDate not supplied

Populating a Graph Database to Run a Usage-Based Discovery Tool

Most dataset discovery tools for Earth Observation data rely on descriptions and other metadata of the datasets, using keyword searches or attribute filtering to determine relevance. However, these descriptions often do not include the potential uses of the data. Thus, a user working on floods will rarely see few if any rainfall datasets show up in such a search. The Usage Based Discovery tool, on the other hand, offers usage instances to the user, either research articles or applications, along with the datasets that those usage instances used. This allows a user, particularly one new to the world of Earth Observation data, to investigate which datasets are used in similar cases. The information that powers Usage-Based Discovery is a graph database of relationships of usage to dataset and usage to topic, allowing the user to narrow their search for similar cases. In order to scale out to a graph database rich enough to provide a satisfactory user experience, we combine manual and automated processes to populate the graph. The initial content of the graph has been seeded primarily via human-aided data curation methods, using sites like Google Scholar. To scale up this effort, we’ve employed crowdsourcing. It is easy for anyone to contribute to our graph using their Open Researcher and Contributor Identifier for authorization. We’re now experimenting with Machine Learning and Natural Language Processing to help automate population of the graph, starting with the classification of research articles by topic. Finding adequate training data in the absence of a comprehensive and open research article API continues to be a significant challenge.

Vincent Inverso↗
DOE OSTI2025

Basin-Scale Structural Features Database

The Basin-Scale Structural Features database provides spatial datasets of faults, fractures, folds, and earthquakes compiled from public, authoritative sources (e.g., U.S. Geological Survey and State Geological Surveys) and aggregated into derivative forms to support subsurface assessments. Recognizing that characterizing basin-scale structural features requires interpreting data that are often ambiguous or lack key information, the source data were evaluated using a knowledge-data framework and geospatial fuzzy logic method (Justman et al., 2020) to represent both measured (observed) and predicted (inferred or potential) structural features as derivative datasets. This workflow employs conceptual models for known structural features and predicted structural features, incorporating geospatial data to estimate potential, even with limited data. The aim is to aid and support an understanding of basin-scale features and identify potential gaps in data and knowledge. As of 4/30/2025, the database includes resources for nine sedimentary basins: Appalachian, Denver, U.S. Gulf Coast, Illinois, Michigan, Permian, Sacramento, San Joquin and Williston. The database is organized by basin and then data category: 1) Faults, fractures, folds, 2) Earthquakes, 3) Topographic, 4) Structural contours and isopachs, 5) Geophysical, and 6) Structural feature density assessment maps.

basin scale↗
NASA NTRSDate not supplied

Analyzing Federal Agency Earth Observation Needs: NASA’s 2022 Satellite Needs Working Group Assessment

Every two years, the National Aeronautics and Space Administration (NASA) leads an assessment of Federal civilian agency Earth observation needs submitted through the Satellite Needs Working Group (SNWG) survey. Nearly 30 agencies participated in the 2022 SNWG survey, submitting 115 high-priority satellite data needs that span Earth Science and represent a wide variety of potential applications for Earth observation data. Analysis of multiple SNWG survey cycles reveals trends in agency needs toward more frequent, higher resolution data that can inform agency decision-making. NASA and partners at the National Oceanic and Atmospheric Administration (NOAA) and U.S. Geological Survey (USGS) evaluated the agency surveys during an eight-month assessment period. Assessment teams comprised of subject matter experts, technology specialists, and agency managers conducted an in-depth interview with each submitting agency to fully understand the need and discuss relevant current and upcoming satellite missions. The teams then proposed over 100 potential solutions, or new activities that NASA, NOAA, and/or USGS could undertake to help meet agency needs. A few cross-cutting potential solutions that are projected to be most valuable to SNWG agencies are under consideration for implementation by NASA in the coming years.

Katrina Virts↗