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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Numerical Reanalyses as a Gateway to Arctic Synthesis

Reanalyses are regularly gridded, retrospective depictions of the physical earth system, which are produced through the correction of a short-term forecast to available observations. In the Arctic, reanalyses are particularly well suited to marshal the sparse observing network to provide a plausible, multivariate representation of conditions. Atmospheric reanalyses such as MERRA-2 (NASA Modern-Era Retrospective analysis for Research and Applications, version 2) and ocean reanalyses such as SODA3 (Univ. Maryland Simple Ocean Data Assimilation version 3) are widely used in Arctic research for diagnostic studies of circulation, model evaluation, and as boundary conditions for a variety of process models. Here, we provide examples that illustrate the utility of reanalyses for providing information on the spatial and temporal scales of recent, rapid changes in the Arctic. Recent trends in Arctic surface temperatures, surface melt over Greenland and Arctic glaciers, and evolving freshwater conditions in the Arctic Ocean are examples where reanalyses can provide information that cannot easily be obtained via other means. These examples provide information on the scale, magnitude, and the uncertainty of recent Arctic change and provide a context for future scenarios. We further quantify uncertainties in key reanalyses variables and approaches for addressing these issues.

Arctic↗
DOE OSTI2024

Machine Learning for DUNE Supernova Trigger

One of the major scientific goals of the Deep Underground Neutrino Experiment (DUNE) is to detect and measure the neutrino flux originating from galactic core-collapse supernovae. These neutrinos provide an opportunity to study the end of life evolution of massive stars, and reveal information about the structure of core-collapse that is not visible in observations of the electromagnetic spectrum. Because of the rarity of these events, it is crucial that DUNE is able to detect supernova neutrino interactions when they occur. However, this will require sifting through a large quantity of data, motivating the development of a trigger algorithm to identify significant events and discard irrelevant data. Machine learning provides a potential approach to building this trigger. This project generates ADC and ground truth images of simulated neutrino interactions in a LArTPC detector to be used for machine learning, and uses them to train a sparse Convolutional Neural Network (C NN). The performance of this model when applied to the task of pixel classification based on interaction type is examined. This project found that the sparse CNN approach has the potential to have high accuracy in pixel classification, meaning it may be highly relevant to the development of a supernova neutrino trigger for the DUNE far detector.

Damish, S.↗
NASA NTRS1986

Rotational modulation in IUE spectra of cool dwarf chromospheres: Data analysis and period search techniques

The IUE spectra of bright F-K dwarfs were used to investigate rotational modulation in chromospheric and transition region emission lines, with Stellingwerf's phase dispersion minimization (PDM) technique as the basis of time series analysis. Sampling-related aliases are found to dominate the PDM spectra in these sparsely sampled, irregularly spaced time series. The star chosen to illustrate the techniques is epsilon Eridani, a young, chromospherically active K2 dwarf. Strong indications of a rotation period close to 2.8 days in epsilon Eridani from 2 seasons of observations, with evidence for the persistence in longitude of the active area(s) over several hundred rotation cycles are found.

Altner, B.↗
NASA NTRS2021

Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations

The ocean mixed layer plays an important role in the coupling between the upper ocean and atmosphere across a wide range of time scales. Estimation of the variability of the ocean mixed layer is therefore important for atmosphere-ocean prediction and analysis. The increasing coverage of in situ Argo profile data allows for an increasingly accurate analysis of the mixed layer depth (MLD) variability associated with deviations from the seasonal climatology. However, sampling rates are not sufficient to fully resolve subseasonal (<90 day) MLD variability. Yet, many multivariate observations-based analyses include implicit modeled subseasonal MLD variability. One analysis method is optimal interpolation of in situ data, but the interior analysis can be improved by leveraging surface data with regression or variational approaches. Here, we demonstrate how machine learning methods and satellite sea surface temperature, salinity, and height facilitate MLD estimation in a pilot study of two regions: the mid-latitude southern Indian and the eastern equatorial Pacific Oceans. We construct multiple machine learning architectures to produce weekly 1/2° gridded MLD anomaly fields (relative to a monthly climatology) with uncertainty estimates. We test multiple traditional and probabilistic machine learning techniques to compare both accuracy and probabilistic calibration. We validate our methodology by applying it to ocean model simulations. We find that incorporating sea surface data through a machine learning model improves the performance of spatiotemporal MLD variability estimation compared to optimal interpolation of Argo observations alone. These preliminary results are a promising first step for the application of machine learning to MLD prediction.

Machine Learning↗
DOE OSTI2024

Weak-Form Latent Space Dynamics Identification

This software showcases the enhanced capabilities of the Latent Space Dynamics Identification (LaSDI) algorithm through the application of the weak form, resulting in WLaSDI. WLaSDI first compresses the data, then projects it onto test functions, and subsequently learns the local latent space models. Notably, WLaSDI demonstrates significantly improved robustness to noise. Using weak-form equation learning techniques, WLaSDI achieves local latent space modeling. Compared to the standard sparse identification of nonlinear dynamics (SINDy) used in LaSDI, the variance reduction of the weak form ensures robust and precise latent space recovery, enabling fast, robust, and accurate simulations. We demonstrate the efficacy of WLaSDI against LaSDI using several common benchmark examples, including viscid and inviscid Burgers', radial advection, and heat conduction. For instance, in 1D inviscid Burgers' simulations with up to 100% Gaussian white noise, WLaSDI maintains relative errors consistently below 6%, whereas LaSDI errors can exceed 10,000%. Similarly, in radial advection simulations, WLaSDI keeps relative errors below 16%, compared to potential errors of up to 10,000% with LaSDI. Additionally, WLaSDI achieves significant speedups, such as a 140X speedup in 1D Burgers' simulations compared to the corresponding full order model.

Choi, Youngsoo↗
NASA NTRS2017

The Effectiveness of Shrouding on Reducing Meshed Spur Gear Power Loss - Test Results

Gearbox efficiency is reduced at high rotational speeds due to windage drag and viscous effects on rotating, meshed gear components. A goal of NASA aeronautics rotorcraft research is aimed at propulsion technologies that improve efficiency while minimizing vehicle weight. Specifically, reducing power losses to rotorcraft gearboxes would allow gains in areas such as vehicle payload, range, mission type, and fuel consumption. To that end, a gear windage rig has been commissioned at NASA Glenn Research Center to measure windage drag on gears and to test methodologies to mitigate windage power losses. One method used in rotorcraft gearbox design attempts to reduce gear windage power loss by utilizing close clearance walls to enclose the gears in both the axial and radial directions. The close clearance shrouds result in reduced drag on the gear teeth, and reduced power loss. For meshed spur gears, the shrouding takes the form of metal side plates and circumferential metal sectors. Variably positioned axial and radial shrouds are incorporated in the NASA rig to study the effect of shroud clearance on gearbox power loss. A number of researchers have given experimental and analytical results for single spur gears, with and without shrouding. Shrouded meshed spur gear test results are sparse in the literature. Windage tests were run at NASA Glenn using meshed spur gears at four shroud configurations: unshrouded, shrouded (max. axial, max radial), and two intermediate shrouding conditions. Results are compared to available meshed spur gear power loss data analyses as well as single spur gear data/analyses. Recommendations are made for future work.

power loss↗
NASA NTRS2017

The Effectiveness of Shrouding on Reducing Meshed Spur Gear Power Loss Test Results

Gearbox efficiency is reduced at high rotational speeds due to windage drag and viscous effects on rotating, meshed gear components. A goal of NASA aeronautics rotorcraft research is aimed at propulsion technologies that improve efficiency while minimizing vehicle weight. Specifically, reducing power losses to rotorcraft gearboxes would allow gains in areas such as vehicle payload, range, mission type, and fuel consumption. To that end, a gear windage rig has been commissioned at NASA Glenn Research Center to measure windage drag on gears and to test methodologies to mitigate windage power losses. One method used in rotorcraft gearbox design attempts to reduce gear windage power loss by utilizing close clearance walls to enclose the gears in both the axial and radial directions. The close clearance shrouds result in reduced drag on the gear teeth and reduced power loss. For meshed spur gears, the shrouding takes the form of metal side plates and circumferential metal sectors. Variably positioned axial and radial shrouds are incorporated in the NASA rig to study the effect of shroud clearance on gearbox power loss. A number of researchers have given experimental and analytical results for single spur gears, with and without shrouding. Shrouded meshed spur gear test results are sparse in the literature. Windage tests were run at NASA Glenn using meshed spur gears at four shroud configurations: unshrouded, shrouded (max. axial, max. radial), and two intermediate shrouding conditions. Results are compared to available meshed spur gear power loss data analyses as well as single spur gear data analyses.

windage↗
NASA NTRS1994

Globular cluster photometry with the Hubble Space Telescope. 3: Blue stragglers and variable stars in the core of M3

This paper describes Hubble Space Telescope (HST)/Planetary Camera-I images of the core of the dense globular cluster M3 (NGC 5272). Stellar photometry in the F555W (V) and F785LP (I) bands, with a 1-sigma photometric accuracy of about 0.1 mag, has been used to construct color-magnitude diagrams of about 4700 stars above the main-sequence turnoff within r less than or approximately equal to 1 min of the cluster center. We have also analyzed archival HST F336W (U) images of M3 obtained by the Wide Field/Planetary Camera-I Instrument Definition Team. The UVI data are used to identify 28 blue straggler (BS) stars within the central 0.29 sq. arcmin. The specific frequency of BSs in this region of M3, N(sub BS)/N(sub V less than (V(HB)+2)) = 0.094 +/- 0.019, is about a factor of 2 - 3 higher than that found by Bolte et al. in a recent ground-based study of the same region, but comparable to that seen in the sparse outer parts of the same cluster and in HST observations of the core of the higher density cluster 47 Tuc. The BSs in M3 are slightly more centrally concentrated than red giant branch stars while horizontal branch stars are somewhat less concentrated red giants. The radial distribution of V-selected subgiant and turnoff stars is well fit by a King model with a core radius r(sub core) = 28 arcmin +/- 2 arcmin (90% confidence limits), which corresponds to 1.4 pc. Red giant and horizontal branch stars selected in the ultraviolet data (U less than 18) have a somewhat more compact distribution (r(sub core) = 22.5 arcmin). The HST U data consist of 17 exposures acquired over a span of three days. We have used these data to isolate 40 variable stars for which relative astrometry, brightnesses, colors, and light curves are presented. A Kolmogorov-Smirnov test indicates that, typically, the variability for each star is significant at the 95% level. We identify two variable BS candidates (probably of the SX Phe type), out of a sample of approximately 25 BSs in which variability could have been detected. Most of the variables are RR Lyrae stars on the horizontal branch. All of them have periods P greater than or approximately equal 8 h.

Guhathakurta, Puragra↗
DOE OSTI2026

High-resolution modeling of indoor radon exposure with uncertainty quantification in Utah

Indoor radon accounts for 37% of population-level exposure to ionizing radiation in the United States. However, radon metrics are typically reported at coarse spatial scales, potentially obscuring meaningful local variation. We developed a high-resolution modeling framework to estimate indoor radon concentrations across Utah while explicitly quantifying predictive uncertainty. A total of 19,497 residential radon measurements collected between 2006 and 2017 were combined with environmental and housing characteristics and analyzed using a geospatial neural network that accommodates spatial dependence and nonlinear associations. Predictions were generated on a uniform hexagonal grid at 0.73 km2 resolution (H3 level 8). Out-of-sample predictions aggregated to the H3 level 8 grid showed good agreement with observed concentrations (Pearson r=0.64), while household-level predictions exhibited more moderate agreement (r=0.45). The model produced well-calibrated uncertainty estimates, with 24.1% of held-out observations exceeding the predicted 75th-percentile threshold. Maps of predicted radon concentrations and the probability of exceeding the U.S. EPA action level of 148 Bq/m3 (4 pCi/L) revealed substantial fine-scale spatial heterogeneity that was not apparent in conventional coarse-resolution summaries, with greater local variability observed in densely monitored urban counties than in sparsely sampled regions. High-resolution radon models that explicitly quantify uncertainty provide a useful framework for characterizing the spatial distribution of indoor radon and identifying areas of elevated exceedance risk. These findings highlight the value of fine-scale monitoring data and uncertainty-aware modeling approaches for radon exposure assessment, environmental risk characterization, and radon-related health research.

Wu, Yunhan [ORNL] (ORCID:0000000178842994)↗
NASA NTRS1980

Microwave radiometric determination of oceanographic and meteorological parameters

The thermal microwave brightness from the atmosphere over an ocean in the range 5-40 GHz is influenced primarily by the distribution of vapor and liquid water in the atmosphere and the temperature and wind speed at the sea surface. The Scanning Multichannel Microwave Radiometer (SMMR), which was carried aboard both the Nimbus-7 and Seasat satellites (both launched in 1978), measures this radiation at five frequencies to find these four parameters. A retrieval algorithm has been developed which, on simulated data, yields accuracies of 1.5 C in sea surface temperature, 0.9 m/s in wind speed, 4 mg/ sq cm of cloud liquid water content and 0.14gm/sq cm of precipitable water vapor. Results from the Seasat SMMR, while both preliminary and sparse, are consistent with these simulations.

Wilheit, T. T.↗
NASA NTRS1982

Advanced multispectral remote sensing technology and applications; Proceedings of the Conference, Arlington, VA, May 6, 7, 1982

Sensor system concepts are discussed, taking into account a multispectral linear array instrument design, an improved earth resources sensing instrument, design tradeoffs for a multispectral linear array instrument, imaging spectrometer technologies for advanced earth remote sensing, and the design concept for an optimized earth radiation budget sensor. Topics related to supporting optics and signal processing methodologies are explored, giving attention to two mirror objective design for multispectral remote sensing, the measurement of optical modulation functions in sparsely sampled mosaic focal plane arrays, multispectral linear array focal plane signal processing, signal processing for the NASA multispectral linear array, the concept for a multiple resolution pushbroom sensor, and the evolution of the octal radiometer. Techniques and applications for multispectral data are considered along with aspects of focal plane technology.

Ando, K. J.↗
NASA NTRS2002

Land and Land-use Change in the Climate Sensitive High Plains: An Automated Approach with Landsat

The High Plains is an economically important and climatologically sensitive region of the United States and Canada. The High Plains contain 100,000 sq km of Holocene sand dunes and sand sheets that are currently stabilized by natural vegetation. Droughts and the larger threat of global warming are climate phenomena that could cause depletion of natural vegetation and make this region susceptible to sand dune reactivation. The original proposal was directed toward the use of Landsat TM data to establish the state and ongoing changes of the surface in the 1.2 million sq. km, semi-arid High Plains region of the central US, A key objective was to develop a model to predict the reactivation of the 100,000 sq. km of Holocene dunes found on the High Plains during an extended drought. At least one Landsat 5 image per year for 1985, 1988 and 1996 was obtained for 32 scenes on the High Plains to coincide with wet and dry years. Additional Landsat 7 data were acquired for 1999 and 2000 primarily for Colorado and Nebraska. As luck would have it, there was no severe drought during the study period 1985-2000. Attention was focused on developing methods for mapping dry vs. green vegetation on sparsely vegetated rangelands in sandy soils, since these were the areas most susceptible to surface reactivation during a drought.

Goetz, Alexander F.↗
NASA NTRS2006

Airborne Observations of the Spatial and Temporal Variability of Tropospheric Carbon Dioxide during the INTEX-B Campaign

The Intercontinental Chemical Transport Experiment-North America (INTEX-NA) is an international field campaign envisioned to investigate the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and assess their impact on air quality and climate. Phase B (INTEX-B) of the mission was conducted during a 10- week period from March 1 to May 15, 2006 and focused initially on pollution outflow from the Mexico City Metropolitan Area, later addressing the transport of pollution from Asia to North America during springtime meteorological conditions. During the deployment, fast-response (1-s resolution) CO2 measurements were recorded aboard the NASA DC-8 providing valuable regional-scale information on carbon sources and sinks over sparsely sampled areas of North America and adjacent ocean basins. When coupled with the enormously sophisticated chemistry payload on the DC-8, these measurements collectively afford extremely powerful multi-tracer constraints for carbon source/sink attribution. Preliminary examination of the two data sets from the INTEX-B campaign, acquired one month apart, reveals not only the influence of the CO2 seasonal cycle, but also the preponderance of human population and industrial activity in the northern hemisphere. In this presentation, a synergy of the ensemble of airborne and surface observations, bottomup emission inventories, as well as transport history are invoked in a GIS framework to elucidate the source/sink processes reflected in the observations. The airborne CO2 data, along with simultaneous surface measurements (e.g. NOAA ESRL), are examined to establish the vertical distribution and variability of CO2 as a function of location. The role of localized sources, long-range transport, the biosphere, stratospheric exchange, and dynamical processes on the CO2 spatial variability observed throughout the tropospheric column will be discussed.

Vay, Stephanie A.↗
NASA NTRS2017

Effective Use Of Scatterometer Winds In Current and Future GMAO Reanalysis

Scatterometer-derived near-surface ocean vector wind retrievals provide global measurements complementary to the sparse conventional observing system which primarily consists of ships and buoys over water surfaces. The RapidScat instrument was flown on the International Space Station as a quick and low cost replacement of QuikScat and as a continuation of the NASA scatterometry data record. A unique characteristic of RapidScat was that it flew in a non-sun synchronous orbit at an inclination of 51.6 degrees. This orbit allowed for the collocation of measurements with other scatterometers as well as an ability to sample diurnal signals. In the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis, the scatterometry record began with the ESA European Remote Sensing (ERS) scatterometer on 5 Aug 1991 and continued through today with the EUMETSAT Metop Advanced Scatterometer (ASCAT). RapidScat, however, was not used in the MERRA-2 system as development had been completed prior to the beginning of its data record. In this presentation, the RapidScat ocean vector winds will be compared to MERRA-2, both in terms of the analysis fields and in the context of its global observing system, to assess the viability of using the data in future reanalysis systems developed by the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center.

Chattopadhyay, Mohar↗
NASA NTRS2021

Mangrove Carbon Stocks in Pongara National Park, Gabon

Mangroves are recognized for their valued ecosystem services to coastal areas, and the functional linkages between those services and ecosystem carbon stocks have been established. However, spatially explicit inventories are necessary to facilitate management and protection of mangroves, as well as providing a foundation for payment for ecosystem service programs such as REDD+. We conducted an inventory of carbon stocks in mangroves within Pongara National Park (PNP), Gabon using a stratified random sampling design based on forest canopy height derived from TanDEM-X remote sensing data. Ecosystem carbon pools, including aboveground and belowground biomass and necromass, and soil carbon to a depth of 2 m were assessed using measurements and samples from plots distributed among three canopy height classes within the park. There were two mangrove species within the inventory area in PNP, Rhizophora racemosa and R. harrisonii. R. harrisonii was predominant in the sparse, low-stature stands that dominated the west side of the park. In the east side of the park, both species occurred in tall-stature stands, with tree height often exceeding 30 m. Canopy height was an effective means to stratify the inventory area, as biomass was significantly different among the height classes. Despite those differences in aboveground biomass, the soil carbon density was not significantly different among height classes. Soils were the main component of the ecosystem carbon stock, accounting for over 84% of the total. The ecosystem carbon density ranged from 644 to 943 Mg C ha−1 among the three height classes. The ecosystem carbon stock within PNP is estimated to be 40,588 Gg C. The combination of pre-inventory information about stand conditions and their spatial distribution within the assessment area obtained from remote sensing data and a spatial decision support system were fundamental to implementing this relatively large-scale field inventory. This work exemplifies how mangrove carbon stocks can be quantified to augment national C reporting statistics, provide a baseline for projects involving monitoring, reporting and verification (i.e., MRV), and provide data on the forest composition and structure for sustainable management and conservation practices.

Carl C Trettin↗
NASA NTRSDate not supplied

Retrospective Observations of the Solar System Planets with Interstellar Probe

Retrospective Observations of the Solar System Planets with Interstellar Probe The Interstellar Probe (ISP) mission concept could simultaneously explore a number of long-standing solar system and exoplanetary science objectives. ISP’s long mission lifetime, in combination with the large separations from the solar system objects it could observe (Fig. 1), affords a truly unique dataset. This data could be leveraged to validate models of solar system and extrasolar planets and would be directly analogous to observations we can expect to make for exoplanets. Taken together, these opportunities suggest that ISP’s mission is a critical and necessary component for future planetary science endeavors. This abstract aims to address some of the clear synergies between ISP’s mission profile and the gaps in solar system science that are necessarily gaps in our ability to wholly bound our expectations for exoplanet observations (partly discussed in several white papers, including Zemcov et al., 2019; Harman et al., 2020). Notably, no single platform has yet returned near-complete phase curves for the majority of solar system planets. This is partly due to observational constraints (e.g., ground-based observatories can observe a maximum phase angle of Jupiter, Saturn, Uranus, and Neptune of 12º, 6º, 3º, and 2º, respectively; Mallama and Hilton, 2018), but also because of the sparse nature of observations captured by spacecraft over the last 40 years (e.g., Pollack et al., 1986). Additionally, these observations come from disparate instruments that have their own biases and limitations, whereas observations by ISP’s instrumentation would provide almost uniform measurements of nearly every solar system object, removing much of the uncertainty when it comes to data intercomparisons. The biggest hurdles for making these measurements from ISP are likely to be the tight mass and energy limitations, as well as the technical challenge of looking as close as possible to the Sun without peering directly at it. This is potentially complicated by the nature of the larger astrophysical mission requirements, including whether the spacecraft is spinning, but integrating observations on board before returning them to Earth serves to both partly mitigate both the possibility of a spinning spacecraft and the downlink volumes for lookback data (although it may make data disaggregation more technically challenging). Ultimately, ISP could return truly innovative observational data of our solar system, in furtherance of a number of planetary and solar system science goals.

Sonny Harman↗
NASA NTRS1979

FORTRAN subroutines for out-of-core solutions of large complex linear systems

The design and usage of two main subprograms using direct methods to solve large linear complex systems, of the form Ax = b, whose coeffficient matrices are too large to be stored in core are described. The first main subprogram is for systems whose coefficient matrices are of a particular sparse structure, namely, the matrix A can be written in the form B + D, where B is a block-banded system, and D has only a few columns of nonzeros. Key elements of the algorithms used in the subprograms include: the data structure, the strategy for preserving numerical stability, the adaptability of the algorithms for dense systems as well as for block-profile systems.

Yip, E. L.↗
NASA NTRS2008

A Data Type for Efficient Representation of Other Data Types

A self-organizing, monomorphic data type denoted a sequence has been conceived to address certain concerns that arise in programming parallel computers. A sequence in the present sense can be regarded abstractly as a vector, set, bag, queue, or other construct. Heretofore, in programming a parallel computer, it has been necessary for the programmer to state explicitly, at the outset, what parts of the program and the underlying data structures must be represented in parallel form. Not only is this requirement not optimal from the perspective of implementation; it entails an additional requirement that the programmer have intimate understanding of the underlying parallel structure. The present sequence data type overcomes both the implementation and parallel structure obstacles. In so doing, the sequence data type provides unified means by which the programmer can represent a data structure for natural and automatic decomposition to a parallel computing architecture. Sequences exhibit the behavioral and structural characteristics of vectors, but the underlying representations are automatically synthesized from combinations of programmers advice and execution use metrics. Sequences can vary bidirectionally between sparseness and density, making them excellent choices for many kinds of algorithms. The novelty and benefit of this behavior lies in the fact that it can relieve programmers of the details of implementations. The creation of a sequence enables decoupling of a conceptual representation from an implementation. The underlying representation of a sequence is a hybrid of representations composed of vectors, linked lists, connected blocks, and hash tables. The internal structure of a sequence can automatically change from time to time on the basis of how it is being used. Those portions of a sequence where elements have not been added or removed can be as efficient as vectors. As elements are inserted and removed in a given portion, then different methods are utilized to provide both an access and memory strategy that is optimized for that portion and the use to which it is put.

James, Mark↗