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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Determination of surface albedos and aerosol extinction characteristics from satellite imagery

A procedure is developed for calculating atmospheric extinction characteristics (optical thickness, scattering height, single scattering albedo) and surface albedo from radiometric images made at satellite altitudes. The procedure - a fast, computerized method - is suitable for the high-volume processing of satellite imagery data and thus can be used to map temporal and spatial distributions of aerosol parameters. Based on an analytical approximate solution to the equation of radiative transfer in a plane parallel atmosphere, the procedure is primarily applicable to the 0.4-micron to 0.8-micron wavelength range for solar zenith angles from 10 deg to 60 deg, surface albedos between 0.03 and about 0.5, and atmospheric optical thicknesses from 0.2 to 2. With a step-like change in the surface albedo (for example, a seashore or river bank or other similar change of terrain), both surface reflectivity and atmospheric optical thickness can be derived from radiance measurements. In this case, the resultant optical thickness is not based on a known surface reflectivity and is in essence independent of the radiometer calibration.

Kaufman, Y. J.↗
NASA NTRS1990

Ulysses mission design after Challenger

The delay of the Ulysses launch from May 1986 to October 1990, because of the Challenger disaster, has altered both the constraints under which the mission must be designed and the timing of several mission critical events. Safety and launch reliability concerns from the Shuttle have increased the effective launch window to durations greater than one hour. Fortuitously high declinations of the launch asymptote (DLA), of the order of the launch site latitude, ameliorate the impact of the new constraints on the launch window. Target overlays in the first hour of the launch window provide higher departure energies that improve mission performance and avoid a science schedule conflict at second opposition near the time of closest Jupiter approach. The mission design starts with the maximum earth departure energy that the upper stage can deliver within the launch constraints. The Jupiter arrival asymptotes are chosen from the optimal point of mission performance in the mission space defined in the Jupiter B-plane by contours mapped by the science and spacecraft constraints. More than half the orbital energy of the earth-to-Jupiter transfer orbit is lost in the Jupiter flyby, and the Jupiter gravitational assist rotates the orbit plane out of the ecliptic to an inclination of about 80 degrees.

Luthey, Joe L.↗
NASA NTRS1992

Growth and characterization of binary and pseudo-binary 3-5 compounds exhibiting non-linear optical behavior. Undergraduate research opportunities in microgravity science and technology

In line with the specified objectives, a Bridgman-type growth configuration in which unavoidable end effects - conventionally leading to growth interface relocation - are compensated by commensurate input-power changes is developed; the growth rate on a microscale is predictable and unaffected by changes in heat transfer conditions. To permit quantitative characterization of the growth furnace cavity (hot-zone), a 3-D thermal field mapping technique, based on the thermal image, is being tested for temperatures up to 1100 C. Computational NIR absorption analysis was modified to now permit characterization of semi-insulating single crystals. Work on growth and characterization of bismuth-silicate was initiated. Growth of BSO (B12SiO20) for seed material by the Czochralski technique is currently in progress. Undergraduate research currently in progress includes: ground based measurements of the wetting behavior (contact angles) of semiconductor melts on substrates consisting of potential confinement materials for solidification experiments in a reduced gravity environment. Hardware modifications required for execution of the wetting experiments in a KC-135 facility are developed.

Witt, August F.↗
NASA NTRS1994

Participation in TOMS missions

In the past year we have been investigating a new algorithm for the derivation of column sulfur dioxide, column ozone, and aerosol optical depth from the Total Ozone Mapping Spectrometer (TOMS) High Density TOMS (HDT) archived albedos. Simulations of the measured albedos were made using a radiative transfer model, and a technique developed to separate the effects of the aerosol optical depth from the absorption optical depth. The retrieval adopted obtained values from the three parameters with high accuracy. This retrieval was then applied to the TOMS data.

Hudson, Robert D.↗
NASA NTRS2010

Ko Displacement Theory for Structural Shape Predictions

The development of the Ko displacement theory for predictions of structure deformed shapes was motivated in 2003 by the Helios flying wing, which had a 247-ft (75-m) wing span with wingtip deflections reaching 40 ft (12 m). The Helios flying wing failed in midair in June 2003, creating the need to develop new technology to predict in-flight deformed shapes of unmanned aircraft wings for visual display before the ground-based pilots. Any types of strain sensors installed on a structure can only sense the surface strains, but are incapable to sense the overall deformed shapes of structures. After the invention of the Ko displacement theory, predictions of structure deformed shapes could be achieved by feeding the measured surface strains into the Ko displacement transfer functions for the calculations of out-of-plane deflections and cross sectional rotations at multiple locations for mapping out overall deformed shapes of the structures. The new Ko displacement theory combined with a strain-sensing system thus created a revolutionary new structure- shape-sensing technology.

Ko, William L.↗
NASA NTRS2011

Thermal Structure and Dynamics of Saturn's Northern Springtime Disturbance

This article combined several infrared datasets to study the vertical properties of Saturn's northern springtime storm. Spectroscopic observations of Saturn's northern hemisphere at 0.5 and 2.5 / cm spectral resolution were provided by the Cassini Composite Infrared Spectrometer (CIRS, 17). These were supplemented with narrow-band filtered imaging from the ESO Very Large Telescope VISIR instrument (16) to provide a global spatial context for the Cassini spectroscopy. Finally, nightside imaging from the Cassini Visual and Infrared Mapping Spectrometer (VIMS, 22) provided a glimpse of the undulating cloud activity in the eastern branch of the disturbance. Each of these datasets, and the methods used to reduce and analyse them, will be described in detail below. Spatial maps of atmospheric temperatures, aerosol opacity and gaseous distributions are derived from infrared spectroscopy using a suite of radiative transfer and optimal estimation retrieval tools developed at the University of Oxford, known collectively as Nemesis (23). Synthetic spectra created from a reference atmospheric model for Saturn and appropriate sources of spectroscopic line data (6, 24) are convolved with the instrument function for each dataset. Atmospheric properties are then iteratively adjusted until the measurements are accurately reproduced with physically-realistic temperatures, compositions and cloud opacities.

Fletcher, Leigh N.↗
NASA NTRS2013

Performance Measurement of Advanced Stirling Convertors (ASC-E3)

NASA Glenn Research Center (GRC) has been supporting development of the Advanced Stirling Radioisotope Generator (ASRG) since 2006. A key element of the ASRG project is providing life, reliability, and performance testing data of the Advanced Stirling Convertor (ASC). The latest version of the ASC (ASC-E3, to represent the third cycle of engineering model test hardware) is of a design identical to the forthcoming flight convertors. For this generation of hardware, a joint Sunpower and GRC effort was initiated to improve and standardize the test support hardware. After this effort was completed, the first pair of ASC-E3 units was produced by Sunpower and then delivered to GRC in December 2012. GRC has begun operation of these units. This process included performance verification, which examined the data from various tests to validate the convertor performance to the product specification. Other tests included detailed performance mapping that encompassed the wide range of operating conditions that will exist during a mission. These convertors were then transferred to Lockheed Martin for controller checkout testing. The results of this latest convertor performance verification activity are summarized here.

Stirling Cycle↗
DOE OSTI2026

Machine Learning Enabled Position Detection for 6.78 MHz UAV Wireless Power Transfer System

This paper presents a novel supervised machine learning (SML) approach for accurate position detection of the receiver coil in wireless power transfer (WPT) systems using only secondary-side electrical measurements, with applications in autonomous unmanned aerial vehicle (UAV) charging. The proposed method trains a supervised learning model to map measured secondary-side voltage and current features to the receiver’s spatial position with high precision. This enables an autonomous UAV to determine its location relative to the primary coil center, the optimal position for maximizing wireless charging efficiency. The sensing method is fully integrated into a standard WPT system, utilizing the same primary and secondary coils for both power transfer and position detection, thereby eliminating additional sensing hardware. The use of a 6.78 MHz operating frequency enhances positional sensitivity, as high-frequency near-field electromagnetic fields respond strongly to small spatial variations. Experimental validation is performed on a 30 W scaled prototype featuring a 210 mm × 140 mm primary coil, a 50 mm × 80 mm receiver coil, and a 15 mm air gap. Results demonstrate reliable position estimation and a strong correlation between predicted position and optimal coil alignment. This integrated framework unifying position detection and wireless charging offers a promising foundation for future autonomous electric vertical takeoff and landing (eVTOL) systems, enabling compact, hardware-efficient, and high-accuracy charging solutions.

Colak, Kerim [New York University]↗
NASA NTRS2003

Orbit Determination Support for the Microwave Anisotropy Probe (MAP)

NASA's Microwave Anisotropy Probe (MAP) was launched from the Cape Canaveral Air Force Station Complex 17 aboard a Delta II 7425-10 expendable launch vehicle on June 30, 2001. The spacecraft received a nominal direct insertion by the Delta expendable launch vehicle into a 185-km circular orbit with a 28.7deg inclination. MAP was then maneuvered into a sequence of phasing loops designed to set up a lunar swingby (gravity-assisted acceleration) of the spacecraft onto a transfer trajectory to a lissajous orbit about the Earth-Sun L2 Lagrange point, about 1.5 million km from Earth. Because of its complex orbital characteristics, the mission provided a unique challenge for orbit determination (OD) support in many orbital regimes. This paper summarizes the premission trajectory covariance error analysis, as well as actual OD results. The use and impact of the various tracking stations, systems, and measurements will be also discussed. Important lessons learned from the MAP OD support team will be presented. There will be a discussion of the challenges presented to OD support including the effects of delta-Vs at apogee as well as perigee, and the impact of the spacecraft attitude mode on the OD accuracy and covariance analysis.

Bauer, Frank↗
DOE OSTI2014

Enhancements to the SHARP Build System and NEK5000 Coupling

The SHARP project for the Department of Energy's Nuclear Energy Advanced Modeling and Simulation (NEAMS) program provides a multiphysics framework for coupled simulations of advanced nuclear reactor designs. It provides an overall coupling environment that utilizes custom interfaces to couple existing physics codes through a common spatial decomposition and unique solution transfer component. As of this writing, SHARP couples neutronics, thermal hydraulics, and structural mechanics using PROTEUS, Nek5000, and Diablo respectively. This report details two primary SHARP improvements regarding the Nek5000 and Diablo individual physics codes: (1) an improved Nek5000 coupling interface that lets SHARP achieve a vast increase in overall solution accuracy by manipulating the structure of the internal Nek5000 spatial mesh, and (2) the capability to seamlessly couple structural mechanics calculations into the framework through improvements to the SHARP build system. The Nek5000 coupling interface now uses a barycentric Lagrange interpolation method that takes the vertex-based power and density computed from the PROTEUS neutronics solver and maps it to the user-specified, general-order Nek5000 spectral element mesh. Before this work, SHARP handled this vertex-based solution transfer in an averaging-based manner. SHARP users can now achieve higher levels of accuracy by specifying any arbitrary Nek5000 spectral mesh order. This improvement takes the average percentage error between the PROTEUS power solution and the Nek5000 interpolated result down drastically from over 23 % to just above 2 %, and maintains the correct power profile. We have integrated Diablo into the SHARP build system to facilitate the future coupling of structural mechanics calculations into SHARP. Previously, simulations involving Diablo were done in an iterative manner, requiring a large amount manual work, and left only as a task for advanced users. This report will detail a new Diablo build system that was implemented using GNU Autotools, mirroring much of the current SHARP build system, and easing the use of structural mechanics calculations for end-users of the SHARP multiphysics framework. It lets users easily build and use Diablo as a stand-alone simulation, as well as fully couple with the other SHARP physics modules. The top-level SHARP build system was modified to allow Diablo to hook in directly. New dependency handlers were implemented to let SHARP users easily build the framework with these new simulation capabilities. The remainder of this report will describe this work in full, with a detailed discussion of the overall design philosophy of SHARP, the new solution interpolation method introduced, and the Diablo integration work. We will conclude with a discussion of possible future SHARP improvements that will serve to increase solution accuracy and framework capability.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

ATLAS: Story Maps for Communicating Groundwater Cleanup Status – 18510

The U.S. Department of Energy (DOE) manages one of the largest groundwater cleanup efforts in the world in terms of the number of plumes, diversity of contaminants, volume of affected groundwater, and diversity of hydrogeologic settings. The DOE Office of Environmental Management (EM) has been collecting site-specific data on plume sizes, remedial approaches, exit strategies, long-term stewardship requirements, and regulatory drivers at all DOE sites, and summarizing them in a single document to communicate high-level information on groundwater contamination and cleanup status to DOE managers. Recently, EM has sponsored an effort to transfer the document-based information to an online resource for collating information on groundwater plumes that is easily accessible by interested parties. Specifically, Web GIS story maps have been developed to provide a robust geospatial visualization of plumes at DOE sites using an intuitive interface that allows users to explore plume maps, explanatory text, photographs, and video provided within the story maps. This collection of story maps is called ATLAS (Assembly of Tools for Learning Actions and Status), and not only communicates groundwater plume data for each individual site, but also summarizes pertinent metrics across all EM sites. With ATLAS, sites with similar settings, contamination, or remedial approaches can be easily identified, facilitating effective communication among DOE sites and sharing of individual successes and lessons learned.

Freedman, Vicky L.↗
DOE OSTI2018

ATLAS: Story Maps for Communicating Groundwater Cleanup Status - 18510

The U.S. Department of Energy (DOE) manages one of the largest groundwater cleanup efforts in the world in terms of the number of plumes, diversity of contaminants, volume of affected groundwater, and diversity of hydrogeologic settings. The DOE Office of Environmental Management (EM) has been collecting site-specific data on plume sizes, remedial approaches, exit strategies, long-term stewardship requirements, and regulatory drivers at all DOE sites, and summarizing them in a single document to communicate high-level information on groundwater contamination and cleanup status to DOE managers. Recently, EM has sponsored an effort to transfer the document-based information to an online resource for collating information on groundwater plumes that is easily accessible by interested parties. Specifically, Web GIS story maps have been developed to provide a robust geospatial visualization of plumes at DOE sites using an intuitive interface that allows users to explore plume maps, explanatory text, photographs, and video provided within the story maps. This collection of story maps is called ATLAS (Assembly of Tools for Learning Actions and Status), and not only communicates groundwater plume data for each individual site, but also summarizes pertinent metrics across all EM sites. With ATLAS, sites with similar settings, contamination, or remedial approaches can be easily identified, facilitating effective communication among DOE sites and sharing of individual successes and lessons learned. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

AN INDUCTIVE MAPPING WITH CONVOLUTIONAL REPRESENTATIONS FOR HUMAN SETTLEMENT DETECTION: PRELIMINARY RESULTS

Undoubtedly, deep convolutional learning methods continue to improve performance in image-level classification for computer vision and remote sensing applications. However, the spatio-temporal nature of remote sensing imagery offers other interesting challenges in multiscale image understanding. Emerging opportunities include seeking fine-grained and neighborhood mapping with overhead imagery. Limitations due to lack of relevant scale ground-truth often mandates that these challenges are pursued as disjoint. We test this premise and explore representation spaces from a single deep convolutional network and their visualization to argue for a unified feature extraction framework. The objective is to utilize and re-purpose trained feature extractors without the need for network retraining on three remote sensing tasks i.e. superpixel mapping, pixel-level segmentation and semantic based image visualization. By leveraging the same convolutional feature extractors and viewing them as visual information extractors that encode different image representation spaces, we demonstrate surprising potential on multiscale experiments that incorporate edge-level details up to semantic-level informationWe test this premise and explore representation spaces from a single deep convolutional network and their visualization to argue for a novel unified feature extraction framework. The objective is to utilize and re-purpose trained feature extractors without the need for network retraining on three remote sensing tasks i.e. superpixel mapping, pixel-level segmentation and semantic based image visualization. By leveraging the same convolutional feature extractors and viewing them as visual information extractors that encode different image representation spaces, we demonstrate a preliminary inductive transfer learning potential on multiscale experiments that incorporate edge-level details up to semantic-level informationWe test this premise and explore representation spaces from a single deep convolutional network and their visualization to argue for a novel unified feature extraction framework. The objective is to utilize and re-purpose trained feature extractors without the need for network retraining on three remote sensing tasks i.e. superpixel mapping, pixel-level segmentation and semantic based image visualization. By leveraging the same convolutional feature extractors and viewing them as visual information extractors that encode different image representation spaces, we demonstrate a preliminary inductive transfer learning potential on multiscale experiments that incorporate edge-level details up to semantic-level information.

Lunga, Dalton↗
DOE OSTI2023

High–Resolution Maps of Near–Surface Permafrost for Three Watersheds on the Seward Peninsula, Alaska Derived From Machine Learning

Permafrost soils are a critical component of the global carbon cycle and are locally important because they regulate the hydrologic flux from uplands to rivers. Furthermore, degradation of permafrost soils causes land surface subsidence, damaging infrastructure that is crucial for local communities. Regional and hemispherical maps of permafrost are too coarse to resolve distributions at a scale relevant to assessments of infrastructure stability or to illuminate geomorphic impacts of permafrost thaw. Here we train machine learning models to generate meter–scale maps of near–surface permafrost for three watersheds in the discontinuous permafrost region. The models were trained using ground truth determinations of near–surface permafrost presence from measurements of soil temperature and electrical resistivity. We trained three classifiers: extremely randomized trees (ERTr), support vector machines (SVM), and an artificial neural network (ANN). Model uncertainty was determined using k–fold cross validation, and the modeled extents of near–surface permafrost were compared to the observed extents at each site. At–a–site near–surface permafrost distributions predicted by the ERTr produced the highest accuracy (70%–90%). However, the transferability of the ERTr to the sites outside of the training data set was poor, with accuracies ranging from 50% to 77%. The SVM and ANN models had lower accuracies for at–a–site prediction (70%–83%), yet they had greater accuracy when transferred to the non–training site (62%–78%). These models demonstrate the potential for integrating high–resolution spatial data and machine learning models to develop maps of near–surface permafrost extent at resolutions fine enough to assess infrastructure vulnerability and landscape morphology influenced by permafrost thaw.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Development of in-use engine speed/torque heat maps across multiple heavy-duty commercial vehicle vocations

The U.S. Department of Energy (DOE) established the SuperTruck program with the goal of achieving brake thermal efficiency (BTE) greater than or equal to 55% as demonstrated in an operational heavy-duty (HD) diesel engine at a 65-miles-per-hour (mph) cruise point. Beyond the line-haul application, HD engines operate in a wide range of speed and torque conditions that are unlikely to yield the same efficiency under real-world operation. Thereby, the in-use engine heat maps described in this paper are a valuable tool to illustrate whether the engine-efficiency “sweet spot” matches the most frequent operating conditions. In this study, NREL developed engine heat maps to quantify the important operating points for various vocations using our Fleet DNA database of commercial fleet vehicle operations data. These heat maps clearly show that high-frequency operating points vary significantly according to vehicle vocation, while only a few of them match the sweet spot. Beyond the illustration, engine in-use heat maps can also be leveraged to build up reduced-order engine-efficiency models, needed by many rapid powertrain simulations. As case studies, nine reduced-order models – including line-haul truck, transfer truck, transit bus, transit bus with compressed natural gas (CNG) engine, drayage, refuse pickup, local delivery, utility truck, and school bus with CNG engine – using a trust-region reflective algorithm to fit the on-road data extracted based on the engine in-use heat maps.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Anaerobic fungi contain abundant, diverse, and transcriptionally active Long Terminal Repeat retrotransposons

Long Terminal Repeat (LTR) retrotransposons are a class of repetitive elements that are widespread in the genomes of plants and many fungi. LTR retrotransposons have been associated with rapidly evolving gene clusters in plants and virulence factor transfer in fungal-plant parasite-host interactions. We report here the abundance and transcriptional activity of LTR retrotransposons across several species of the early-branching Neocallimastigomycota, otherwise known as the anaerobic gut fungi (AGF). The ubiquity of LTR retrotransposons in these genomes suggests key evolutionary roles in these rumen-dwelling biomass degraders, whose genomes also contain many enzymes that are horizontally transferred from other rumen-dwelling prokaryotes. Up to 10% of anaerobic fungal genomes consist of LTR retrotransposons, and the mapping of sequences from LTR retrotransposons to transcriptomes shows that the majority of clusters are transcribed, with some exhibiting expression greater than 104 reads per kilobase million mapped reads (rpkm). Many LTR retrotransposons are strongly differentially expressed upon heat stress during fungal cultivation, with several exhibiting a nearly three-log10 fold increase in expression, whereas growth substrate variation modulated transcription to a lesser extent. We show that some LTR retrotransposons contain carbohydrate-active enzymes (CAZymes), and the expansion of CAZymes within genomes and among anaerobic fungal species may be linked to retrotransposon activity. We further discuss how these widespread sequences may be a source of promoters and other parts towards the bioengineering of anaerobic fungi.

Genetics & Heredity↗
DOE OSTI2018

Quantitative Attachment of Bimetal Combinations of Transition-Metal Ions to the Surface of TiO 2 Nanorods

Here we report the sequential, quantitative loading of transition-metal ions (Cr 3+ , Mn 2+ , Fe 2+ , Co 2+ , Ni 2+ , and Cu 2+ ) onto the surface of rod-shaped anatase TiO 2 nanocrystals in bimetallic combinations ( 6 C 2 = 15) to form M,M'-TiO 2 nanocrystals. The materials were characterized with transmission electron microscopy (TEM), powder X-ray diffraction (XRD), elemental analysis, X-ray photoelectron spectroscopy (XPS), and UV–visible spectroscopy. TEM and XRD data indicate that the sequential adsorption of metal ions occurs with the retention of the phase and morphology of the nanocrystal. Atomistic models of the M,M'-TiO 2 nanocrystals were optimized with density functional theory calculations. Calculated UV–visible absorption spectra and partial charge density maps of the donor and acceptor states for the electronic transitions indicate the importance of metal-to-metal charge transfer (MMCT) processes.

36 MATERIALS SCIENCE↗