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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

Oceanic geoid and tides derived from GEOS 3 satellite data in the Northwestern Atlantic Ocean

Two sets of GEOS 3 altimeter data which fall within about a 2.5-deg width are analyzed for ocean geoid and tides. One set covers a path from Newfoundland to Cuba, and the other a path from Puerto Rico to the North Carolina coast. Forty different analyses using various parameters are performed in order to investigate convergence. Profiles of the geoid and four tides, M2, O1, S2, and K1, are derived along the two strips. While the analyses produced convergent solutions for all 40 cases, the uncertainty caused by the linear orbital bias error of the satellite is too large to claim that the solutions represent the true ocean tides in the area. A spot check of the result with the Mode deep-sea tide gauge data shows poor agreement. A positive conclusion of this study is that despite the uncertain orbital error the oceanic geoid obtained through this analysis can improve significantly the short-wavelength structure over existing spherical harmonic geoid models.

Won, I. J.↗
NASA NTRS2014

A Geosynchronous Orbit Optical Communications Relay Architecture

NASA is planning to fly a Next Generation Tracking and Data Relay Satellite (TDRS) next decade. While the requirements and architecture for that satellite are unknown at this time, NASA is investing in communications technologies that could be deployed on the satellite to provide new communications services. One of those new technologies is optical communications. The Laser Communications Relay Demonstration (LCRD) project, scheduled for launch in December 2017 as a hosted payload on a commercial communications satellite, is a critical pathfinder towards NASA providing optical communications services on the Next Generation TDRS. While it is obvious that a small to medium sized optical communications terminal could be flown on a GEO satellite to provide support to Near Earth missions, it is also possible to deploy a large terminal on the satellite to support Deep Space missions. Onboard data processing and Delay Tolerant Networking (DTN) are two additional technologies that could be used to optimize optical communications link services and enable additional mission and network operations. This paper provides a possible architecture for the optical communications augmentation of a Next Generation TDRS and touches on the critical technology work currently being done at NASA. It will also describe the impact of clouds on such an architecture and possible mitigation techniques.

optics↗
NASA NTRSDate not supplied

Enabling Mission Flexibility to Battery Driven Deep Space Endeavors With Generalized Battery-Health-Monitoring Using Physics-Based and Data-Driven Reduced-Order Models

The needs and requirements for an electrochemical energy storage for deep space exploration is well explored. It is often understood that different mission sites and environmental conditions require different battery chemistries or technologies. Additionally, various engineering solutions are deployed to overcome specific chemical challenges. One often overlooked need is the “health” monitoring of an electrochemical storage system. The term generalized health monitoring, as envisioned in this work, refers to the monitoring of various aspects such as electrode health, electrolyte health, reaction pathway health, cooling system health, sensor health, and BMS health [1]. Generalized health monitoring allows mission leads, engineers, and scientists to incorporate flexibility in mission designs, make on-the-fly mission changes, and extend the duration of science missions. Moreover, it enables automation and data-driven decision-making without compromising safety and performance. Recently, our group developed a hierarchy of thermal reduced-order models (TROM) by combining a physics-based modeling approach and data-driven model reduction techniques applied to flight data [2]. The resulting TROMs were found to be not only accurate but also identifiable from the flight data. Consequently, the coefficient of variance of the model parameters is small over the course of hundreds of flights, allowing for monitoring the parameter evolution trajectories as the battery ages and degrades. These parameters constitute the metrics of the generalized health of a battery. Monitoring their evolution allows such models to be used for anomaly detection and prognostics, improving early detection of abnormal behavior and thus enabling timely maintenance, longer battery life, and enhanced battery safety. For this presentation, the practicality of the thermal model will be validated on a pack of 14cells under various topology configurations such as 1S14P, 2P7S, 7S2P, and 1P14S. It is well known that manufacturing and non-uniform aging lead to variability in the performance of a cell, which is exacerbated by cell balancing during active load. Additionally, in extreme scenarios, the paramount objective is to complete the mission, regardless of the stresses on the battery. Topology-induced balancing issues further stress the battery. The goal of this study is to determine if the noise (identifiability) in the reduced-order thermal model parameters is sensitive to topology, cell spacing, cooling strategy, and manufacturing or age variability. The variability in cells is considered by assuming a multimodal distribution for microscopic parameters of a cell (such as porosity, tortuosity, reaction kinetics, volumetric thermal conductivity, and volumetric heat capacity). The compounded effect of manufacturing variability, topological selection, cooling strategies, and cell balancing ages each cell in a battery differently. The study aims to clarify whether the challenge in extracting maximum information depends on the minimum number of sensors or models used for data extraction.

Automation↗
NASA NTRS1975

A distributed data base management system

Major system design features of a distributed data management system for the NASA Deep Space Network (DSN) designed for continuous two-way deep space communications are described. The reasons for which the distributed data base utilizing third-generation minicomputers is selected as the optimum approach for the DSN are threefold: (1) with a distributed master data base, valid data is available in real-time to support DSN management activities at each location; (2) data base integrity is the responsibility of local management; and (3) the data acquisition/distribution and processing power of a third-generation computer enables the computer to function successfully as a data handler or as an on-line process controller. The concept of the distributed data base is discussed along with the software, data base integrity, and hardware used. The data analysis/update constraint is examined.

Bryan, A. I.↗
NASA NTRS1985

The galactic cosmic ray intensity minimum in the inner and outer heliosphere in solar cycle 21

The occurrence of the cosmic ray intensity minimum during solar cycle 21 at widely separated locations in the heliosphere is investigated. The data include in situ measurements by cosmic-ray detectors on the earth-orbiting satellite IMP 8, on the Voyagers 1 and 2 spacecraft, and on the Pioneers 10 and 11 spacecraft, located at distances from 1 to 27 AU. It is noted that Pioneer 10 and the other three spacecraft (Pioneer 11 and Voyagers 1 and 2) are on the opposite sides of the sun, with the latter moving toward the tail of the heliosphere. The data used in the study have been suitably corrected by removing solar flare particle contributions, Forbush decreases, and contamination by the radioisotope thermoelectric generator. It is evident that there exists a time delay in the occurrence of the cosmic ray intensity minimum registered by the IMP 8 and Pioneer 10 detectors, the latter being located at approximately 27 AU. It is shown that the data from the four spacecraft in deep space are on an average consistent with the IMP 8 data at 1 AU when suitably corrected, using 3 percent/AU for the radial gradient and 500 km/s for the average speed of propagation. An overview of the intensity profiles reveals reasonable agreement among them. However, specific intensity features appear at times to propagate with speeds in excess of the average value of about 500 km/s.

Venkatesan, D.↗
DOE OSTI2019

Application of Deep Learning on Integrating Prediction, Provenance, and Optimization

In this research, we investigated two approaches to detect job anoma- lies and/or contention for large scale computing efforts: 1. Preemptive job scheduling using binomial classification long short-term memory networks 2. Forecasting intra-node computing loads from the active jobs and addi- tional job(s) For approach 1, we achieved a 14% improvement in computational resources utilization and an overall classification accuracy of 85% on real tasks executed in a High Energy Physics computing workflow. For this paper, we present the preliminary results used in second approach.

Anomaly Detection, Belle II Computing, data proven↗
DOE OSTI2019

SDN-NGenIA, a Software Defined Next Generation Integrated Architecture for HEP and Data Intensive Science

This project aims to enable the LHC and other leading programs in high energy physics and other global science domains funded by the DOE to operate with a new level of efficiency and control, through the development of a Next Generation Integrated Architecture (NGenIA) based on intelligent software defined network (SDN)-driven network systems coupled to high throughput applications. While the initial focus is on the challenging LHC use case, the systems and products being developed are general, and apply to many fields of data intensive science ranging from astrophysical sky surveys to bioinformatics and earth observation, as well as other organizations facing the challenges of extracting knowledge from distributed multi-petabyte data stores. A central concept in this development program is a new paradigm ‘consistent network operations’ among widely distributed computing and storage facilities, where stable high throughput flows, at set rates, cross load-balanced network paths, up to flexible high water marks that are adjusted in real time to accommodate other network traffic. The large smooth flows are launched and managed by SDN services that act in concert with the experiments’ site-resident data distribution and management systems, to meet the expanding needs of the science programs. The technical goals include the construction of autonomous, intelligent site-resident services that interact dynamically with network-resident services, and with the science programs’ principal data distribution and management tools, to request or command network resources in support of high throughput petascale to exascale workflow Specific work items include: 1. Developing compact Data Transfer Nodes (DTNs) with auto-tuning functions that support data transfer rates in the 100 Gbps to the 1 Tbps range when used with high throughput data transfer applications 2. Deep site orchestration among virtualized clusters, storage subsystems and subnets to successfully coschedule CPU, storage and network resources 3. Science-program designed site architectures, operational modes, and policy and resource usage priorities, adjudicated across multiple network domains and virtual organizations, using the orchestration functions and methods 4. Seamlessly extending end-to-end operation across both extra-site and intra-site boundaries through the use of Open vSwitch (OVS), FireQoS + next generation Science DMZs 5. Novel methods of system integration that enable granular control of extreme scale long distance transfers through flow matching of scattered source-destination address pairs to multi-domain dynamic circuits 6. Funneling massive sets of streams to DTNs at the site edge hosting petascale buffer pools configured for flows of 100 Gbps and up, exploiting state of the art data transfers 7. Adaptive scheduling based on pervasive end-to-end monitoring, including DTN or compute-node resident agents providing comprehensive end-system profiling 8. Developing unsupervised and supervised machine learning and modeling methods to optimize the workflow involving terabyte to multi-petabyte datasets.

47 OTHER INSTRUMENTATION↗
NASA NTRS2024

VIIRS Version 2 Deep Blue Aerosol Products

NASA's Deep Blue aerosol project has developed global aerosol data records using consistent retrieval algorithms applied to various satellite sensors. The primary components of these data records are derived from the series of Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (SNPP) and the National Oceanic and Atmospheric Administration or NOAA-20+ satellites as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), among others. These instruments provide over 23 years of measurements with similar radiometric characteristics for aerosol retrievals. The algorithms used for the initial Version 1 SNPP VIIRS data set were based on the MODIS Collection 6.1 Deep Blue algorithm over land and Satellite Ocean Aerosol Retrieval (SOAR) algorithm over water. For VIIRS Version 2 data reprocessing, major updates have been made to the algorithm suite, including better accounting for effects of surface pressure, improved determination of surface reflectance, and the inclusion of fine-mode aerosol optical models to better represent anthropogenic aerosols over land. Cross-calibration gain factors are derived for the NOAA-20 VIIRS measurements to be consistent with the SNPP VIIRS, which allows the use of a unified algorithm package for both instruments. Comparisons against AERONET observations indicate that the Version 2 AOD data from SNPP VIIRS are significantly better than the Version 1 counterpart over land and slightly degraded over water in exchange for better spatial coverage. The AOD data from SNPP and NOAA-20 VIIRS are comparable, indicating that cross-calibration enables the creation of consistent aerosol data records using the series of VIIRS sensors.

Deep Blue↗
DOE OSTI2016

Search for gamma-ray emission from AE Aquarii with seven years of Fermi LAT observations

AE Aquarii (AE Aqr) is a cataclysmic binary hosting one of the fastest rotating (P{sub spin} = 33.08 s) white dwarfs (WDs) known. Based on seven years of Fermi Large Area Telescope (LAT) Pass 8 data, we report on a deep search for gamma-ray emission from AE Aqr. Using X-ray observations from ASCA, XMM-Newton, Chandra, Swift, Suzaku, and NuSTAR, spanning 20 years, we substantially extend and improve the spin ephemeris of AE Aqr. Using this ephemeris, we searched for gamma-ray pulsations at the spin period of the WD. No gamma-ray pulsations were detected above 3σ significance. Neither phase-averaged gamma-ray emission nor gamma-ray variability of AE Aqr is detected by Fermi LAT. We impose the most restrictive upper limit to the gamma-ray flux from AE Aqr to date: 1.3×10{sup −12} erg cm{sup −2} s{sup −1} in the 100 MeV–300 GeV energy range, providing constraints on models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A deep generative model enables automated structure elucidation of novel psychoactive substances

Over the past decade, the illicit drug market has been reshaped by the proliferation of clandestinely produced designer drugs. These agents, referred to as new psychoactive substances (NPSs), are designed to mimic the physiological actions of better-known drugs of abuse while skirting drug control laws. The public health burden of NPS abuse obliges toxicological, police and customs laboratories to screen for them in law enforcement seizures and biological samples. However, the identification of emerging NPSs is challenging due to the chemical diversity of these substances and the fleeting nature of their appearance on the illicit market. Here, in this study, we present DarkNPS, a deep learning-enabled approach to automatically elucidate the structures of unidentified designer drugs using only mass spectrometric data. Our method employs a deep generative model to learn a statistical probability distribution over unobserved structures, which we term the structural prior. We show that the structural prior allows DarkNPS to elucidate the exact chemical structure of an unidentified NPS with an accuracy of 51% and a top-10 accuracy of 86%. Our generative approach has the potential to enable de novo structure elucidation for other types of small molecules that are routinely analysed by mass spectrometry.

Cheminformatics↗
DOE OSTI2015

YOUNG STELLAR OBJECTS IN THE MASSIVE STAR-FORMING REGION W49

We present the initial results of our investigation of the star-forming complex W49, one of the youngest and most luminous massive star-forming regions in our Galaxy. We used Spitzer/Infrared Array Camera (IRAC) data to investigate massive star formation with the primary objective of locating a representative set of protostars and the clusters of young stars that are forming around them. We present our source catalog with the mosaics from the IRAC data. In this study we used a combination of IRAC, MIPS, Two Micron All Sky Survey, and UKIRT Deep Infrared Sky Survey (UKIDSS) data to identify and classify the young stellar objects (YSOs). We identified 232 Class 0/I YSOs, 907 Class II YSOs, and 74 transition disk candidate objects using color–color and color–magnitude diagrams. In addition, to understand the evolution of star formation in W49, we analyzed the distribution of YSOs in the region to identify clusters using a minimal spanning tree method. The fraction of YSOs that belong to clusters with ≥7 members is found to be 52% for a cutoff distance of 96″, and the ratio of Class II/I objects is 2.1. We compared the W49 region to the G305 and G333 star-forming regions and concluded that W49 has the richest population, with seven subclusters of YSOs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Systematic uncertainties from higher-twist corrections in DIS at large x

We investigate the systematic uncertainties and potential biases arising from the inclusion of large- x corrections to proton and deuteron deep inelastic scattering (DIS) data in global quantum chromodynamics (QCD) analyses. Using the CTEQ-JLab framework, we examine various approaches to implementing higher-twist corrections in nucleon structure functions and off-shell PDF modifications in deuteron targets. We analyze how these components interact and influence the determination of the d -quark PDF and the neutron structure function at large x . We find that it is very important to consider isospin-dependent higher-twist corrections in order to minimize implementation biases in the extracted quantities. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Multiple-objective Reinforcement Learning for Inverse Design and Identification

Inverse chemical design using generative deep learning (DL) models is an emerging tool, which has early success in generating molecular structures with desired properties. We enhance this tool by developing heuristics for curriculum-learning based multiple-objective (20+ objectives) reinforcement learning, and apply it to the context of chemical identi?cation. We develop a generative DL framework that utilizes constraints pertaining to the unknown molecule’s mass and functional group composition, and show that multiple-objective RL-based generative DL models can correctly identify unknown molecules with a 80% success rate, compared to the baseline approach of 0%. Lastly, the heuristics developed are not limited to just chemistry research challenges; we anticipate any problem that utilizes reinforcement learning with multiple-objectives will bene?t from it.

Artificial Intelligence, Data Science, deep learni↗
NASA NTRS1973

Earth's gravity field to the eighteenth degree and geocentric coordinates for 104 stations from satellite and terrestrial data

Geodetic parameters describing the earth's gravity field and the positions of satellite-tracking stations in a geocentric reference frame were computed. These parameters were estimated by means of a combination of five different types of data: routine and simultaneous satellite observations, observations of deep-space probes, measurements of terrestrial gravity, and surface-triangulation data. The combination gives better parameters than does any subset of data types. The dynamic solution used precision-reduced Baker-Nunn observations and laser range data of 25 satellites. Data from the 49-station National Oceanic and Atmospheric Administration BC-4 network, the 19-station Smithsonian Astrophysical Observatory Baker-Nunn network, and independent camera stations were employed in the geometrical solution. Data from the tracking of deep-space probes were converted to relative longitudes and distances to the earth's axis of rotation of the tracking stations. Surface-gravity data in the form of 550-km squares were derived from 19,328 1 deg X 1 deg mean gravity anomalies.

Gaposchkin, E. M.↗
DOE OSTI2022

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

Abstract This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1994

Influence of ocean surface conditions on atmospheric vertical thermodynamic structure and deep convection

The authors analyze the influence of Sea Surface Temperature (SST) and surface wind divergence on atmospheric thermodynamic structure and the resulting effects on the occurrence of deep convection using National Meteorological Center radiosonde data and International Satellite Cloud Climatology Program data for July 1983-July 1985. The onset of deep convection requires not only the existence of positive convective available potential energy (CAPE), but also an unstable planetary boundary layer (PBL). A stable PBL is observed to suppress deep convection even when CAPE is positive. Variations of SST have a major effect on CAPE, but surface wind divergence can also affect deep convection by changing the lapse rate in the lower troposphere and humidity in the PBL. Specifically, when SST is greater than or equal to 28 C, CAPE is always positive, and surface wind divergence does not qualitatively change the buoyancy profile above the PBL. Strong surface wind divergence, however, stabilizes the PBL so as to suppress the initiation of deep convection. In warm SST regions, CAPE is greater than 0 regardless of assumptions about condensate loading, although the pseudoadiabatic limit is more consistent with the observed deep convection than the reversible moist-adiabatic limit under these circumstances. When SST is less than 27 C, CAPE is usually negative and inhibits convection, but strong surface wind convergence can destabilize the inversion layer and moisten the PBL enough to make the atmosphere neutrally stable in the mean. As a result, deep convection is generally enhanced either when SST is greater than or equal to 28 C in the absence of strong surface wind divergence or when strong surface wind convergence occurs even if SST is less than 27 C. The anomalous suppression of deep convection in the warm area of the equatorial west Pacific lying between the intertropical convergence zone (ITCZ) and south Pacific convergence zone (SPCZ) is probably caused by dryness in the PBL and an inversion in that area. The seasonal cycles of deep convection and surface wind divergence are in phase with the maximum solar radiation and lead SST for one to three months in the central Pacific. The change of PBL relative humidity plays a critical role in the changeover to convective instability in this case. The seasonal change of deep convection and associated clouds seems not to have important effects on the seasonal change of local SST in the central Pacific.

Fu, Rong↗