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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

High Energy Benthic Boundary Layer Experiment (HEBBLE): Preliminary program plan and conceptual design

Deep sea processes of flow-sediment interaction, particularly the role of high energy ocean bottom current events in forming the seafloor topography, transporting material, and mixing the bottom of the water column are examined. A series of observations at and near the sea bottom, in water depths of 4 to 5 km, in areas of the western North Atlantic where high energy current events occur, include site surveys and physical reconnaissance to identify suitable areas and positions, and one or more six month experiments to investigate temporal and spatial variations of high energy events within the boundary layer and their interaction with the seabed. Descriptions of proposed HEBBLE activities are included, with emphasis on technology transfer to the oceanographic community through design, fabrication, testing, and operation of an instrumented ocean bottom lander.

Frewing, K.↗
NASA NTRS1979

Color changes at the Viking landing sites over the course of a Mars year

Color changes at the Viking landing sites over the past Mars year are examined. Both sites exhibited an increased red to blue ratio of the soil due to the accumulation of a thin layer of bright red dust from the two global dust storms. The generation of bright areas on Mars and the formation of dark areas by stripping of bright dust layers are explained by the lander observations before the dust storm activity which show isolated bright red drifts which may be regions where material deposited during previous storms was protected from wind erosion.

Guinness, E. A.↗
NASA NTRS1980

High-resolution Lyman-alpha filtergrams of the sun

The results of an experiment, conducted jointly by the Lockheed Palo Alto Research Laboratory and the Laboratoire de Physique Stellaire et Planetaire du CNRS, which investigated the transition-region plasma and the geometry of coronal active regions, in relation to models of the high-temperature layers, are presented. A Black Brant rocket was used to obtain 1-arc sec resolution L-alpha pictures of the sun, which revealed small scale features not seen previously at this wavelength, that delineate the geometry of the magnetic field in the chromosphere and in the corona. It is concluded that these observations might provide a new way of observing the upper chromosphere and corona, and that they provide direct evidence of the inhomogeneous character of the chromosphere and of the dominant role of the magnetic field

Bonnet, R. M.↗
NASA NTRS1982

Vertical eddy diffusion coefficient from the LANDSAT imagery

Analysis of five stable cases of the smoke plumes that originated in eastern Cabo Frio (22 deg 59'S; 42 deg 02'W), Brazil using LANDSAT imagery is presented for different months and years. From these images the lateral standard deviation (sigma sub y) and the lateral eddy diffusion coefficient (K sub y) are obtained from the formula based on Taylor's theory of diffusion by continuous moment. The rate of kinetic energy dissipation (e) is evaluated from the diffusion parameters sigma sub y and K sub y. Then, the vertical diffusion coefficient (K sub z) is estimated using Weinstock's formulation. These results agree well with the previous experimental values obtained over water surfaces by various workers. Values of e and K sub z show the weaker mixing processes in the marine stable boundary layer. The data sample is apparently to small to include representative active turbulent regions because such regions are so intermittent in time and in space. These results form a data base for use in the development and validation of mesoscale atmospheric diffusion models.

Viswanadham, Y.↗
NASA NTRS1986

Surface modification strategies for (100)3C-SiC

Several surface modificaation techniques were performed in situ in an ultrahigh vacuum as part of a program to develop electrical contacts on the (100) face of cubic SiC. The Auger electron spectroscopy line shapes and peak-to-peak heights of the Si LVV and CKLL transitions indicated changes in surface stoichiometry, bonding, and short range order. Changes in the low-energy electron diffraction pattern identified changes in the symmetry of long range ordering on the surface. Heating above 1050 C depleted the surface of Si with an activation energy of 120 kcal/mol, resulting eventually in a disordered graphitic layer which was several atomic layers thick. Bombardment by Ar ions of energies greater than 1000 eV enhanced the Si to C ratio on the surface and destroyed the LEED pattern. Long range order was recovered by simultaneous heating and ion bombardment. Finally, adsorption of Cr on the Ar ion damaged surface and subsequent desorption left a reconstructed, p(2 x 1)Si enriched surface. Similar treatments with Al did not produce a surface reconstruction.

Bellina, J. J., Jr.↗
NASA NTRS1992

Earth System science

Recent research has solidified a view of the Earth as a global-scale interactive system with complex chemical, physical, biological and dynamical processes that link the ocean, atmosphere, land (soils, ice, snow) and marine and terrestrial living organisms. These processes both within and between the major parts of the system help determine global and regional climate and control the biogeochemical and hydrologic cycles essential to life. The study of the Earth System requires measurements ranging from the scales of the smallest processes to the global scale. An ambitious satellite observational program, the Earth Observing System (EOS), carried out along with the complementary and ongoing World Climate Research Program (WCRP) and International Geosphere-Biosphere Program (IGBP) represents a major international effort to understand this System and predict its future changes. The complex and intriguing nature of the Earth System is discussed along with a number of closely coupled processes occurring within it. These are: clouds, precipitation and vegetation; ocean circulation, sea-surface temperature and phytoplankton; coupled oceanic and atmospheric circulation (the Southern Oscillation); biological activity, atmospheric chemistry and climate; and biological emissions and the ozone layer.

Prinn, R. G.↗
NASA NTRS2007

Cross Section Measurements Using the Zero Degree Detector

The Zero Degree Detector (ZDD) is an instrument that has been used in accelerator exposures to measure the angular dependence of particles produced in heavy ion fragmentation experiments. The ZDD uses two identical layers of pixelated silicon detectors that make coincident measurements over the active area of the instrument. The angular distribution of secondary particle produced in nuclear interactions for several heavy ions: and target materials will be presented along with performance characteristic of the instrument.

Christl, M. J.↗
NASA NTRS2015

NASA Space Science Day Events-Engaging Students in Science

The NASA Space Science Day Event follows the same format of planning and execution at all host universities and colleges. These institutions realized the importance of such an event and sought funding to continue hosting NSSD events. In 2014, NASA Johnson Space Center ARES team has supported the following universities and colleges that have hosted a NSSD event; the University of Texas at Brownsville, San Jacinto College, Georgia Tech University and Huston-Tillotson University. Other universities and colleges are continuing to conduct their own NSSD events. NASA Space Science Day Events are supported through continued funding through NASA Discovery Program. Community Night begins with a NASA speaker and Astromaterials display. The entire community surrounding the host university or college is invited to the Community Night. This year at the Huston-Tillotson (HTU) NSSD, we had Dr. Laurie Carrillo, a NASA Engineer, speak to the public and students. She answered questions, shared her experiences and career path. The speaker sets a tone of adventure and discovery for the NSSD event. After the speaker, the public is able to view Lunar and Meteorite samples and ask questions from the ARES team. The students and teachers from nearby schools attended the NSSD Event the following day. Students are able to see the university or college campus and the university or college mentors are available for questions. Students rotate through hour long Science Technology Engineering and Mathematics (STEM) sessions and a display area. These activities are from the Discovery Program activities that tie in directly with k- 12 instruction. The sessions highlight the STEM in exploration and discovery. The Lunar and Meteorite display is again available for students to view and ask questions. In the display area, there are also other interactive displays. Angela Green, from San Jacinto College, brought the Starlab for students to watch a planetarium exhibit for the NSSD at Huston-Tillotson University. Many HTU mentors were leading activities in the display room such as build a comet, volcano layering and robotics manipulation. Students were exposed to a variety STEM career possibilities and information. The students could relate the displays and sessions to what they were learning in school. The HTU mentors made the connection clear for the students. The students ended the event with a mission design presentation. They were able to take what they had learned during the day and were able to create a mission. Students presented their Mission Design and gained confidence in STEM. Conclusion: NASA Space Science Day Events provides an out of school experiential learning environment for students to enhance their STEM curriculum and let students see a college campus. The experiences students gain from attending NSSD gives them the confidence to see themselves on a college campus, possibly majoring in a STEM degree, and understand the importance of completing school.

Foxworth, S.↗
NASA NTRSDate not supplied

GeoAI advances in specific landform mapping

Landform mapping (also referred to as geomorphology or geomorphometry) can be divided into two domains: general and specific (Evans 2012). Whereas general landform mapping categorizes all elements of the study area into landform classes, such as ridges, valleys, peaks, and depressions, the mapping of specific landforms requires the delineation (even if fuzzy) of individual landforms. The former is mainly driven by physical properties such as elevation, slope, and curvature. The latter, however, must consider the cognitive (human) reasoning that discriminates individual landforms in addition to these physical properties (Arundel and Sinha 2018). Both mapping forms are important. General geomorphometry is needed to understand geological and ecological processes and as boundary layer input to climate and environmental models. Specific geomorphometry supports such activities as disaster management and recovery, emergency response, transportation, and navigation. In the United States, individual landforms of interest are named in the U.S. Geological Survey (USGS) Geographic Names Information System, a point dataset captured specifically to digitize geographic names from the USGS Historical Topographic Map Collection (HTMC). Named landform extent is represented only by the name placement in the HTMC. Recent work has investigated CNN-based deep learning methods to capture these extents in machine-readable form. These studies first relied on physical properties (Arundel et al. 2020) and then included the HTMC as a band in RGB images in limited testing (Arundel et al. 2023). Results from the HTMC dataset surpassed those using just physical properties and using the HTMC alone performed best due to the hillshading and elevation (contour) data incorporated into the topographic maps. However, results fell short of an operational capacity to map all named landforms in the United States. Thus, our current work expands upon past research by focusing on the HTMC and physical information as inputs and the named landform label extents. Specifically, we propose to leverage pre-trained foundation models for segmentation and optical character recognition (OCR) models to jointly map landforms in the United States. Our approach aims to bridge the disparities among the independent information sources to facilitate informed decision-making. The modeling pipeline performs (1) segmentation using the physical information and (2) information extraction using OCR, in parallel. Then a computer vision approach merges the two branches into a labeled segmentation. References: Arundel, Samantha T., Wenwen Li, and Sizhe Wang. 2020. “GeoNat v1.0: A Dataset for Natural Feature Mapping with Artificial Intelligence and Supervised Learning.” Transactions in GIS 24 (3): 556–72. https://doi.org/10.1111/tgis.12633. Arundel, Samantha T, and Gaurav Sinha. 2018. “Validating GEOBIA Based Terrain Segmentation and Classification for Automated Delineation of Cognitively Salient Landforms BT - Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017).” In Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017), Lecture Notes in Geoinformation and Cartography, edited by Paolo Fogliaroni, Andrea Ballatore, and Eliseo Clementini, 9–14. Cham: Springer International Publishing. Arundel, Samantha T., Gaurav Sinha, Wenwen Li, David P. Martin, Kevin G. McKeehan, and Philip T. Thiem. 2023. “Historical Maps Inform Landform Cognition in Machine Learning.” Abstracts of the ICA 6 (August): 1–2. https://doi.org/10.5194/ica-abs-6-10-2023. Evans, Ian S. 2012. “Geomorphometry and Landform Mapping: What Is a Landform?” Geomorphology 137 (1): 94–106. https://doi.org/10.1016/j.geomorph.2010.09.029.

machine learning↗
NASA NTRSDate not supplied

GeoAI Advances in Specific Landform Mapping

Landform mapping (also referred to as geomorphology or geomorphometry) can be divided into two domains: general and specific (Evans 2012). Whereas general landform mapping categorizes all elements of the study area into landform classes, such as ridges, valleys, peaks, and depressions, the mapping of specific landforms requires the delineation (even if fuzzy) of individual landforms. The former is mainly driven by physical properties such as elevation, slope, and curvature. The latter, however, must consider the cognitive (human) reasoning that discriminates individual landforms in addition to these physical properties (Arundel and Sinha 2018). Both mapping forms are important. General geomorphometry is needed to understand geological and ecological processes and as boundary layer input to climate and environmental models. Specific geomorphometry supports such activities as disaster management and recovery, emergency response, transportation, and navigation. In the United States, individual landforms of interest are named in the U.S. Geological Survey (USGS) Geographic Names Information System, a point dataset captured specifically to digitize geographic names from the USGS Historical Topographic Map Collection (HTMC). Named landform extent is represented only by the name placement in the HTMC. Recent work has investigated CNN-based deep learning methods to capture these extents in machine-readable form. These studies first relied on physical properties (Arundel et al. 2020) and then included the HTMC as a band in RGB images in limited testing (Arundel et al. 2023). Results from the HTMC dataset surpassed those using just physical properties and using the HTMC alone performed best due to the hillshading and elevation (contour) data incorporated into the topographic maps. However, results fell short of an operational capacity to map all named landforms in the United States. Thus, our current work expands upon past research by focusing on the HTMC and physical information as inputs and the named landform label extents. Specifically, we propose to leverage pre-trained foundation models for segmentation and optical character recognition (OCR) models to jointly map landforms in the United States. Our approach aims to bridge the disparities among the independent information sources to facilitate informed decision-making. The modeling pipeline performs (1) segmentation using the physical information and (2) information extraction using OCR, in parallel. Then a computer vision approach merges the two branches into a labeled segmentation.

machine learning↗
DOE OSTI2022

A Classification of G -invariant Shallow Neural Networks

When trying to fit a deep neural network (DNN) to a G-invariant target function with G a group, it only makes sense to constrain the DNN to be G-invariant as well. However, there can be many different ways to do this, thus raising the problem of “G-invariant neural architecture design”: What is the optimal Ginvariant architecture for a given problem? Before we can consider the optimization problem itself, we must understand the search space, the architectures in it, and how they relate to one another. In this paper, we take a first step towards this goal; we prove a theorem that gives a classification of all G-invariant single-hidden-layer or “shallow” neural network (G-SNN) architectures with ReLU activation for any finite orthogonal group G, and we prove a second theorem that characterizes the inclusion maps or “network morphisms” between the architectures that can be leveraged during neural architecture search (NAS). The proof is based on a correspondence of every G-SNN to a signed permutation representation of G acting on the hidden neurons; the classification is equivalently given in terms of the first cohomology classes of G, thus admitting a topological interpretation. The G-SNN architectures corresponding to nontrivial cohomology classes have, to our knowledge, never been explicitly identified in the literature previously. Using a code implementation, we enumerate the G-SNN architectures for some example groups G and visualize their structure. Lastly, we prove that architectures corresponding to inequivalent cohomology classes coincide in function space only when their weight matrices are zero, and we discuss the implications of this for NAS.

Agrawal, Devanshu↗
DOE OSTI2025

EVALUATION OF OPERATION TEMPERATURES UNDER NATURAL CONVECTION HELIUM FLOW IN A CONFINED CAVITY

The Spallation Neutron Source (SNS) is a high-power accelerator-based pulsed neutron source led by Oak Ridge National Laboratory (ORNL) to achieve high fluxes of neutrons for scientific experiments. Active and passive cooling of the systems and parts forming the SNS have been considered to warrant the safe operation of the facility. The diverse cooling systems make use of conjugated heat transfer mechanisms to provide a stable operation temperature for all components in the machine. Thermal power deposited into stainless-steel piping lines due to particle radiation may reach values of up to 1.2 W/cc in the regions located closer to the center of the lower IRP. These energy deposition levels, in not actively cooled components, such as the transfer-line-outer-vacuum-layer may increase the temperature of the component beyond design requirement limitations. The evaluation of the operation temperatures for the former components relies in the assumption that a low-pressure helium atmosphere provides enough heat removal capacity based on natural convection phenomena. In this work, the evaluation of steady state temperatures in components such as the CMS transfer lines has been evaluated using computational fluid dynamics (CFD), analytical correlations and experimental measurements. The companion experiments were conducted in a closed helium system at pressures varying from 1.1 to 1.5 bar. A copper rod was affixed horizontally between viewing windows and heated at constant power, and measurements were made of both the rod temperature and ambient temperature via a system fiberoptic distributed temperature sensors and RTDs. It was found that the measured heat transfer coefficients agree well with the predictions of Churchill and Chu correlations across the range of cases considered. Additionally, the ambient helium volume above the rod was imaged via background oriented schlieren (BOS), and these data was used to determine the line-averaged density gradients in this region. These gradients were compared to simulation data to validate the predictions of natural convection simulations.

Dominguez-Ontiveros, Elvis [ORNL] (ORCID:000000018↗
DOE OSTI2020

Microscopy Studies of the Catalyst Inks for PEM Fuel Cells

The microstructure of the catalyst layer in proton exchange membrane fuel cells (PEMFC) is one of the key factors that determine fuel cell performance. The ink preparation, including dispersion solvent, mixing time, and mixing power, are among the less reported parameters that would influence the ionomer distribution on the catalyst surface and the continuity of the carbon network. In this work, novel characterization approaches have been used to investigate the effect of the Pt/C catalyst ink preparation conditions on the morphology and structures of the catalyst layer. Microscopy analysis revealed that longer mixing time led to better performing electrode structures than shorter mixing time. The longer mixing time enabled improved continuity of the ionomer network and high porosity in the cathode layer that contributes to improved proton conductivity and mass transport. This has been reflected in performance and durability tests, where the electrode made from a 5-day catalyst ink displayed improved performance compared to the one made from a 3-day ink. Durability studies showed 26% and 43% loss of the initial mass activity for 5 days and 3 days mixing, respectively. Finally, in addition, the catalyst layer prepared with ethylene glycol as the dispersion solvent showed better durability than water/1-propanol based solution.

25 ENERGY STORAGE↗
NASA NTRS2012

Sofia Observatory Performance and Characterization

The Stratospheric Observatory for Infrared Astronomy (SOFIA) has recently concluded a set of engineering flights for Observatory performance evaluation. These in-flight opportunities have been viewed as a first comprehensive assessment of the Observatory's performance and will be used to address the development activity that is planned for 2012, as well as to identify additional Observatory upgrades. A series of 8 SOFIA Characterization And Integration (SCAI) flights have been conducted from June to December 2011. The HIPO science instrument in conjunction with the DSI Super Fast Diagnostic Camera (SFDC) have been used to evaluate pointing stability, including the image motion due to rigid-body and flexible-body telescope modes as well as possible aero-optical image motion. We report on recent improvements in pointing stability by using an Active Mass Damper system installed on Telescope Assembly. Measurements and characterization of the shear layer and cavity seeing, as well as image quality evaluation as a function of wavelength have been performed using the HIPO+FLITECAM Science Instrument configuration (FLIPO). A number of additional tests and measurements have targeted basic Observatory capabilities and requirements including, but not limited to, pointing accuracy, chopper evaluation and imager sensitivity. SCAI activities included in-flight partial Science Instrument commissioning prior to the use of the instruments as measuring engines. This paper reports on the data collected during the SCAI flights and presents current SOFIA Observatory performance and characterization.

Temi, Pasquale↗
DOE OSTI2021

Oxygen Electrocatalysis on Mixed-Metal Oxides/Oxyhydroxides: From Fundamentals to Membrane Electrolyzer Technology

Catalyzing the oxygen evolution reaction (OER) is important for key energy-storage technologies, particularly water electrolysis and photoelectrolysis for hydrogen fuel production. Under neutral-to-alkaline conditions, first-row transition-metal oxides/(oxy)hydroxides are the fastest-known OER catalysts and have been the subject of intense study for the past decade. Critical to their high performance is the intentional or accidental addition of Fe to Ni/Co oxides that convert to layered (oxy)hydroxide structures during the OER. Unraveling the role that Fe plays in the catalysis and the molecular identity of the true “active site” has proved challenging, however, due to the dynamics of the host structure and absorbed Fe sites as well as the diversity of local structures in these disordered active phases. In this Account, we highlight our work to understand the role of Fe in Ni/Co (oxy)hydroxide OER catalysts. We first discuss how we characterize the intrinsic activity of the first-row transition-metal (oxy)hydroxide catalysts as thin films by accounting for the contributions of the catalyst-layer thickness (mass loading) and electrical conductivity as well as the underlying substrate’s chemical interactions with the catalyst and the presence of Fe species in the electrolyte. We show how Fe-doped Ni/Co (oxy)hydroxides restructure during catalysis, absorb/desorb Fe, and in some cases degrade or regenerate their activity during electrochemical testing. We highlight the relevant techniques and procedures that allowed us to better understand the role of Fe in activating other first-row transition metals for OER. We find several modes of Fe incorporation in Ni/Co (oxy)hydroxides and show how those modes correlate with activity and durability. We also discuss how this understanding informs the incorporation of earth-abundant transition-metal OER catalysts in anion-exchange-membrane water electrolyzers (AEMWE) that provide a locally basic anode environment but run on pure water and have advantages over the more-developed proton-exchange-membrane water electrolyzers (PEMWE) that use platinum-group-metal (PGM) catalysts. We outline the key issues of introducing Fe-doped Ni/Co (oxy)hydroxide catalysts at the anode of the AEMWE, such as the oxidative processes triggered by Fe species traveling through the polymer membrane, pH-gradient effects on the catalyst stability, and possibly limited catalyst utilization in the compressed stack configuration. We also suggest possible mitigation strategies for these issues. Lastly, we summarize remaining challenges including the long-term stability of Fe-doped Ni/Co (oxy)hydroxides under OER conditions and the lack of accurate models of the dynamic active surface that hinder our understanding of, and thus ability to design, these catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Combining ToF‐SIMS and Multivariate Analysis to Resolve Active Sites on Ni‐Based HER Catalysts

Unambiguous identification of active sites in heterogeneous catalysis remains a major challenge, particularly for materials with ultrathin, chemically mixed surface layers. Here, we demonstrate a generalizable approach that combines time-of-flight secondary ion mass spectrometry (ToF-SIMS) with multivariate statistical analysis (principal component analysis [PCA] and multivariate curve resolution [MCR]) to resolve catalytically relevant motifs at the nanoscale. Using Ni electrodes as a model system, PCA distinguished hydroxide-enriched domains from oxide- and metal-rich regions, while MCR decomposed depth profiles and 3D images into hydroxide, oxide, and metallic layers with nanometer resolution. A unique secondary-ion fragment, NiO 3 H 3 − (m/z 108.94), emerged as a marker of hydroxide-rich environments and correlated with hydrogen evolution reaction (HER) activity across a series of Ni electrodes. Complementary density functional theory (DFT) calculations revealed that Ni(OH) 2 clusters adjacent to metallic Ni offer the most favorable water dissociation energetics, establishing the structural origin of the marker. While illustrated here for Ni-based HER, this workflow provides a broadly applicable framework to isolate and rank near-surface patterns that govern catalytic activity, thereby extending ToF-SIMS from a qualitative probe to a predictive tool for active site identification.

HER active sites↗
DOE OSTI2021

Atomic Layer Deposition with TiO2 for Enhanced Reactivity and Stability of Aromatic Hydrogenation Catalysts

Hydrogenation of aromatic molecules in fossil- and bio-derived fuels is essential for decreasing emissions of harmful combustion products and addressing growing concerns around urban air pollution. In this work, we used atomic layer deposition to significantly enhance the hydrogenation performance of a conventional supported Pd catalyst by applying an ultrathin coating of TiO2 in a scalable powder coating process. The TiO2-coated catalyst showed substantial gains in the conversion of multiple aromatic molecules, including a 5-fold improvement in turnover frequency versus the uncoated catalyst in the hydrogenation of naphthalene. This activity enhancement was maintained upon scaling the coating synthesis process from 3 to 100 g. Based on the results from Xray photoelectron spectroscopy, X-ray absorption spectroscopy, and computational modeling, the activity enhancement was attributed to ensemble effects resulting from partial TiO2 coverage of the Pd surface rather than fundamental changes to the Pd electronic structure. Additional durability testing confirmed that the TiO2 coating improved the thermal and hydrothermal stability of the catalyst as well as tolerance toward sulfur impurities in the reactant stream. Using an economic model of an industrial deep hydrogenation process, we found that an increase in catalyst activity or lifetime of 2× would justify even a relatively high estimate for the cost of TiO2 atomic layer deposition coatings at scale

atomic layer↗