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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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NASA NTRS2008

The NASA NEESPI Data Portal to Support Studies of Climate and Environmental Changes in Non-Boreal Europe

NASA NEESPI (Northern Eurasia Earth Science Partnership Initiative) data portal is a NASA funded project that focuses on collecting satellite remote sensing data, providing tools, information, and services in support of NEESPI scientific objectives (Leptoukh, et al., 2007). The data can be accessed online through anonymous ftp, through an advanced data searching and ordering system Mirador that uses keywords to find data quickly in a Google-like interface, and through the Goddard Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni). The portal provides preprocessed data from different satellite sensors and numerical models to the same spatial and temporal resolution and the same projection so that the data can be used easily to perform inter-comparison or relationship studies. In addition, it provides parameter and spatially subsetted data for regional studies. Studies of regional carbon, hydrology, aerosols in non-boreal Europe and their interactions with global climate are very challenging research topics. The NASA NEESPI data portal makes many satellite data available for such studies, including information on land cover types, fire, vegetation index, aerosols, land surface temperature, soil moisture, precipitation, snow/ice, and other parameters. This paper will introduce the features and products available in the system, focusing on the online data 1 tool, Giovanni NEESPI. An example that explores different data through Giovanni NEESPI in temperate region of non-boreal Europe will be presented.

Shen, Suhung↗

NASA NTRS2012

MINK Workshop Presentations for SERVIR

Topics include: MINX Document 1 MISR : The instrument, its orbit and data products. MINX Document 2 MISR : Tools for ordering and viewing data. MINX Document 3 : MINX : Overview and plume case studies. MINX Document 4 : MINX basic features. MINX Document 5 : Measuring aerosol height and motion with MINX. MINX Document 5 : Handling complexities in height retrievals. Plume boundaries, Wind direction, Low Optical Thickness, Other complexities. Digitizing options. Managing plume projects.

MINX↗

NASA NTRSDate not supplied

PALMO: An OVERFLOW Machine Learning Airfoil Performance Database

The OVERFLOW Machine Learning Airfoil Performance (PALMO) database has been created to enable robust modeling of airfoil performance in a variety of applications. The PALMO database uses OVERFLOW simulation data second-order accurate in time and fourth-order accurate in space with Spalart-Allmaras turbulence closure. The foundation of the in-development PALMO database is the airfoil base cube. Each base cube includes simulation data parametrized over a range of Mach numbers, Reynolds numbers, and angles-of-attack. This database includes the NACA 4-series airfoils, with parametrization in airfoil thickness and camber from an NACA 0006 to an NACA 4424. In total, 52,480 NACA 4-series OVERFLOW calculations were run on the NASA High-End Compute Capability (HECC) supercomputer. This provides high-order-accurate simulation data covering a wide range of aerospace design applications, which enables users to develop accurate airfoil performance look-up tables without additional high-performance computing. In addition to engineering design and analysis of aerospace vehicles, PALMO is well suited to be a benchmark dataset for the development and testing of machine learning methods in aerospace engineering. This work presents an example PALMO surrogate model that enables accurate airfoil performance predictions for any arbitrary combination of camber, thickness, Mach number, Reynolds number, and angle of attack within the bounds of the database. Airfoil performance tables predicted for an airfoil not used in training the model are used in three-dimensional OVERFLOW simulations to quantify the downstream accuracy on aggregate rotor performance metrics. For the NACA 3415 airfoil, which had no common thickness or camber with the training data, the surrogate predicted and CFD generated tables were within 2.1% of each other in the forward flight lift to drag metric. This suggests that performance tables generated for airfoils within the bounds of the PALMO database will yield aggregate rotor performance predictions on par with tables generated from directly running OVERFLOW airfoil calculations. The PALMO airfoil performance coefficients are available publicly.

Database↗

NASA NTRS2024

PALMO: An OVERFLOW Machine Learning Airfoil Performance Database

The OVERFLOW Machine Learning Airfoil Performance (PALMO) database has been created to enable robust modeling of airfoil performance in a variety of applications. The database uses OVERFLOW simulation data second-order accurate in time and fourth-order accurate in space with Spalart-Allmaras turbulence closure. The foundation of the in-development PALMO database is the airfoil base cube. Each base cube includes simulation data parametrized over a range of Mach numbers, Reynolds numbers, and angles-of-attack. This first release of the database includes the NACA 4-series airfoils, with parametrization in airfoil thickness and camber from an NACA 0006 to an NACA 4424. In total, 52,480 NACA 4-series calculations were run on the NASA High-End Compute Capability (HECC) supercomputer and the corresponding airfoil performance coefficients are embedded in the Appendix of this document for public distribution. This provides high-order-accurate simulation data covering a wide range of aerospace design applications, which enables users to develop OVERFLOW-quality airfoil performance look-up tables without additional high-performance computing. In addition to engineering design and analysis of aerospace vehicles, PALMO is well suited to be a benchmark dataset for the development and testing of machine learning methods in aerospace engineering. Downstream surrogate models enable OVERFLOW- quality airfoil performance predictions for any arbitrary combination of camber, thickness, Mach number, Reynolds number, and angle-of-attack within the bounds of the database.

Database↗

NASA NTRS1968

Improved coefficients of the thirteenth order harmonics of the geopotential derived from satellite Doppler data at three different orbital inclinations

Improved coefficients of thirteenth order harmonics of geopotential derived from satellite Doppler data at three orbital inclinations

Yionoulis, S. M.↗

DOE OSTI2020

Data-driven materials research enabled by natural language processing and information extraction

Given the emergence of data science and machine learning throughout all aspects of society, but particularly in the scientific domain, there is increased importance placed on obtaining data. Data in materials science are particularly heterogeneous, based on the significant range in materials classes that are explored and the variety of materials properties that are of interest. This leads to data that range many orders of magnitude, and these data may manifest as numerical text or image-based information, which requires quantitative interpretation. The ability to automatically consume and codify the scientific literature across domains - enabled by techniques adapted from the field of natural language processing - therefore has immense potential to unlock and generate the rich datasets necessary for data science and machine learning. This review focuses on the progress and practices of natural language processing and text mining of materials science literature and highlights opportunities for extracting additional information beyond text contained in figures and tables in articles. Here, we discuss and provide examples for several reasons for the pursuit of natural language processing for materials, including data compilation, hypothesis development, and understanding the trends within and across fields. Current and emerging natural language processing methods along with their applications to materials science are detailed. We, then, discuss natural language processing and data challenges within the materials science domain where future directions may prove valuable.

36 MATERIALS SCIENCE↗

NASA NTRS1982

Evaluating near-surface soil moisture using Heat Capacity Mapping Mission data

Four dates of Heat Capacity Mapping Mission (HCMM) data were analyzed in order to evaluate HCMM thermal data use in estimating near-surface soil moisture in a complex agricultural landscape. Because of large spatial and temporal ground cover variations, HCMM radiometric temperatures alone did not correlate with soil water content. The radiometric temperatures consisted of radiance contributions from different canopies and their respective soil backgrounds. However, when surface soil temperatures were empirically estimated from HCMM temperatures and percent cover of each pixel, a highly significant correlation was obtained between the estimated soil temperatures and near-surface soil water content.

Heilman, J. L.↗

NASA NTRS1984

A hardware/software simulation for the video tracking system of the Kuiper Airborne Observatory telescope

This simulator was created so that C-141 Kuiper Airborne Observatory investigators could test their Airborne Data Acquisition and Management System software on a system which is generally more accessible than the ADAMS on the plane. An investigator can currently test most of his data acquisition program using the data computer simulator in the Cave. (The Cave refers to the ground-based computer facilities for the KAO and the associated support personnel.) The main Cave computer is interfaced to the data computer simulator in order to simulate the data-Exec computer communications. However until now, there has been no way to test the data computer interface to the tracker. The simulator described here simulates both the KAO Exec and tracker computers with software which runs on the same Hewlett-Packard (HP) computer as the investigator's data acquisition program. A simulator control box is hardwired to the computer to provide monitoring of tracker functions, to provide an operator panel similar to the real tracker, and to simulate the 180 deg phase shifting of the chopper squre-wave reference with beam switching. If run in the Cave, one can use their Exec simulator and this tracker simulator.

Boozer, G. A.↗

NASA NTRS1993

Hot-gas-side heat transfer characteristics of subscale, plug-nozzle rocket calorimeter chamber

An experimental investigation was conducted to determine the hot-gas-side heat transfer characteristics for a liquid-hydrogen-cooled, subscale, plug-nozzle rocket test apparatus. This apparatus has been used since 1975 to evaluate rocket engine advanced cooling concepts and fabrication techniques, to screen candidate combustion chamber liner materials, and to provide data for model development. In order to obtain the data, a water-cooled calorimeter chamber having the same geometric configuration as the plug-nozzle test apparatus was tested. It also used the same two showerhead injector types that were used on the test apparatus: one having a Rigimesh faceplate and the other having a platelet faceplate. The tests were conducted using liquid oxygen and gaseous hydrogen as the propellants over a mixture ratio range of 5.8 to 6.3 at a nominal chamber pressure of 4.14 MPa abs (600 psia). The two injectors showed similar performance characteristics with the Rigimesh faceplate having a slightly higher average characteristic-exhaust-velocity efficiency of 96 percent versus 94.4 percent for the platelet faceplate. The throat heat flux was 54 MW/m(sup 2) (33 Btu/in.(sup 2)-sec) at the nominal operating condition, which was a chamber pressure of 4.14 MPa abs (600 psia), a hot-gas-side wall temperature of 730 K (1314 R), and a mixture ratio of 6.0. The chamber throat region correlation coefficient C(sub g) for a Nusselt number correlation of the form Nu =C(sub g)Re(sup 0.8)Pr(sup 0.3) averaged 0.023 for the Rigimesh faceplate and 0.026 for the platelet faceplate.

Quentmeyer, Richard J.↗

NASA NTRS1994

Basic principles of flight test instrumentation engineering, volume 1, issue 2

Volume 1 of the AG 300 series on ´Flight Test Instrumentation´ gives a general introduction to the basic principles of flight test instrumentation. The other volumes in the series provide more detailed treatments of selected topics on flight test instrumentation. Volume 1, first published in 1974, has been used extensively as an introduction for instrumentation courses and symposia, as well as being a reference work on the desk of most flight test and instrumentation engineers. It is hoped that this second edition, fully revised, will be used with as much enthusiasm as the first edition. In this edition a flight test system is considered to include both the data collection and data processing systems. In order to obtain an optimal data flow, the overall design of these two subsystems must be carefully matched; the detail development and the operation may have to be done by separate groups of specialists. The main emphasis is on the large automated instrumentation systems used for the initial flight testing of modern military and civil aircraft. This is done because there, many of the problems, which are discussed here, are more critical. It does not imply, however, that smaller systems with manual data processing are no longer used. In general, the systems should be designed to provide the required results at the lowest possible cost. For many tests which require only a few parameters, relatively simple systems are justified, especially if no complex equipment is available to the user. Although many of the aspects discussed in this volume apply to both small and large systems, aspects of the smaller systems are mentioned only when they are of special interest. The volume has been divided into three main parts. Part 1 defines the main starting points for the design of a flight test instrumentation system, as seen from the points of view of the flight test engineer and the instrumentation engineer. In Part 2 the discussion is concentrated on those aspects which apply to each individual measuring channel, and in Part 3 the main emphasis is on the integration of the individual data channels into one data collection system and on those aspects of the data processing which apply to the complete system.

channels (data transmission)↗

NASA NTRS1950

Tests of the Northrop XSSM-A-3 Missile in the Ames 40- by 80-Foot Wind Tunnel: Wing Modifications

Wind-tunnel tests were conducted to determine the longitudinal stability characteristics of a full-scale Northrop XSSM-A-3 missile. Various wing modifications were investigated in an effort to provide a configuration that would maintain longitudinal stability to lift coefficients necessary for landing the missile during flight tests. The results of the tests led to the choice of a wing with an increased leading-edge radius. A short discussion of the results is presented, but no analysis of the data has been made in order to make the data available as soon as possible.

Graham, David↗

NASA NTRS2007

The Operation and Evolution of the Swift X-ray Telescope

The Swift X-ray Telescope (XRT) is a CCD based X-ray telescope designed for localization, spectroscopy and long term light curve monitoring of Gamma-Ray Bursts and their X-ray afterglows. Since the launch of Swift in November 2004, the XRT has undergone significant evolution in the way it is operated. Shortly after launch there was a failure of the thermo-electric cooler on the XRT CCD, which led to the XRT team being required to devise a method of keeping the XRT CCD temperature below 50C utilizing only passive cooling by minimizing the exposure of the XRT radiator to the Earth. We present in this paper an update on how the modeling of this passive cooling method has improved in first -1000 days since the method was devised, and the success rate of this method in day-to-day planning. We also discuss the changes to the operational modes and onboard software of the XRT. These changes include improved rapid data product generation in order to improve speed of rapid Gamma-Ray Burst response and localization to the community; changes to the way XRT observation modes are chosen in order to better fine tune data aquisition to a particular science goal; reduction of "mode switching" caused by the contamination of the CCD by Earth light or high temperature effects.

Kennea, Jamie↗

NASA NTRS2018

Exploring Synergistic Tools from NASA Satellite Data for Air Quality

Space-borne earth observation has been important to monitor the earth condition and played a critical role in validating other instruments or modeling's outputs. However, the data from satellite earth observation are usually very complex in terms of science contents, formats, and spatiotemporal granularities, making them difficult to use from many aspects. NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), one of the 12 official NASA data centers, archives and distributes rich collections of data from multiple satellite missions and model results. The GES DISC is also the official archive center for data from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura mission since 2004. Recently, the GES DISC has been evolving and improving its data management and services in order to promote NASA data to be easily discovered and accessed, as well as to facilitate interoperability. We'll show in this presentation how to explore and analyze NASA earth observation data for air quality through a suite of user-friendly tools - from Worldview to Giovanni, demonstrating in using this set of tools prepares us to serve the Sentinel 5P TROPOMI to the community.

air quality↗

NASA NTRS2019

Explore Air Quality from NASA Satellite Data

Space-borne earth observation has been important to monitor the earth condition and played acritical role in validating other instruments or modeling's outputs. However, the data from satellite earth observation are usually very complex in terms of science contents, formats, and spatiotemporal granularities, making them difficult to use from many aspects. NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), one of the 12 official NASA data centers, archives and distributes rich collections of data from multiple satellite missions and model results. The GES DISC is also the official archive center for data from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura mission since 2004. Recently, the GES DISChas been evolving and improving its data management and services in order to promote NASA data to be easily discovered and accessed, as well as to facilitate interoperability. We'll show in this presentation how to explore and analyze NASA earth observation data for air quality through a suite of user-friendly tools - from Giovanni to ArcGIS, demonstrating in using this set of tools prepares us to serve the Sentinel 5P TROPOMI to the community.

Wei, J.↗

NASA NTRS2020

Event Selection and Background Rejection in Time Projection Chambers Using Convolutional Neural Networks and a Specific Application to the AdEPT Gamma-ray Polarimeter Mission

The Advanced Energetic Pair Telescope gamma-ray polarimeter uses a time projection chamber for measuring pair production events and is expected to generate a raw instrument data rate four orders of magnitude greater than is transmittable with typical satellite data communications. GammaNet, a convolutional neural network, proposes to solve this problem by performing event classification on-board for pair production and background events, reducing the data rate to a level that can be accommodated by typical satellite communication systems. In order to train GammaNet, a set of 1.1 × 10(exp 6) pair production events and 10(exp 6) background events were simulated for the Advanced Energetic Pair Telescope using the Geant4 Monte Carlo code. An additional set of 10(exp 6) pair production and 10 background events were simulated to test GammaNet’s capability for background discrimination. With optimization, GammaNet has achieved the proposed background rejection requirements for Galactic Cosmic Ray proton events. Given the best case assumption for downlink speeds, signal sensitivity for pair production ranged between 1.1 ± 0.5% to 69 ± 2% for 5 and 250 MeV incident gamma rays. This range became 0.1 ± 0.1% to 17 ± 2% for the worst case scenario of downlink speeds. The application of a feature visualization algorithm to GammaNet demonstrated decreased response to electronic noise and events exiting or entering the frame and increased response to parallel tracks that are close in proximity. GammaNet has been successfully implemented and shows promising results.

Richard L. Garnett↗

NASA NTRS2021

A High Dynamic-Range Photon-Counting Receiver for Deep Space Optical Communication

The Deep Space Optical Communication (DSOC) project will demonstrate free-space optical communication at almost 3 AU, or 3 orders of magnitude further than any previous attempt. DSOC will utilize the 5m Palomar Hale Telescope to receive the downlink signal, which will couple the downlink light onto an optical table and into a superconducting nanowire single photon detector (SNSPD). The output of the SNSPD is digitized by the Ground Laser Receiver Signal Processing Assembly (GSPA) using a high throughput streaming time to digital converter (TDC). The GSPA is a scalable FPGA-based receiver which demodulates and decodes the DSOC downlink signal through novel signal processing algorithms implemented on Xilinx UltraScale+ FPGAs, as well as Python-based software monitor and control routines. Exploiting the unique TDC-based architecture, the GSPA supports over four orders of magnitude of downlink data rates across multiple orders of magnitude of signal and background powers. In this paper we present an overview of the hardware, firmware and software architectures to implement this system, as well as performance analysis for links ranging from near-Earth to 2.8 AU.

Srinivasan, Meera↗

NASA NTRSDate not supplied

Metadata Entry Optimization for NASA's Biological Institutional Scientific Collection (NBISC)

The NASA Biological Institutional Sample Collection (NBISC) at NASA’s Ames Research Center is a critical resource housing non-human samples collected from spaceflight missions and ground analog studies, primarily consisting of specimens from rats, mice, and select microbes. The primary objective of NBISC is to systematically receive, document, preserve, and facilitate access to these samples for the global scientific community. NBISC promotes international collaboration and maximizes the return on investment for precious tissues from spaceflight and analog experiments. Researchers can request physical samples through an online request form and subsequent written proposal review process. This study addresses two core research objectives: streamlining the NBISC sample lifecycle processes and strategizing for managing an influx of 50,000 tissue samples from a series of cosmic radiation analog experiments carried out at the NASA Space Radiation Laboratory (NSRL) by Drs. Eleanor Chang (Lawrence Berkeley Laboratory) and Polly Blakely (SRI). The Chang/Blakely studies investigated Harderian gland (HG) tumorigenesis in mice exposed to low dose and LET radiation comprising 8 different exposure protocols in over 4000 mice. NBISC sample metadata is stored in a Laboratory Information Management System (SLIMS). To streamline sample data entry, we customize python scripts using information extracted from the individual experimental protocols. The scripts automate entry into multiple SLIMS data fields including protocol name, unique sample barcode, tissue and sub-tissue information, freezer location, sample preservation method, etc. The semi-automated procedure significantly decreases the time spent on data entry by several orders of magnitude. Automation and data organization are essential, as they free up time for curation and promotion of the collection which, in turn, increase the accessibility of samples to the broader research community. NBISC benefits from streamlined data ingestion, and the methodologies developed here are applicable to other projects which use SLIMS including the NASA Biospecimen Sharing Program and GeneLab. As of Fall 2023, plans include transferring sample data from SLIMS to public facing repositories (OSDR and NLSP), expanding the reach of the Chang/Blakely sample collection. The Human Research Program Space Radiation Element plans to transfer non-human tissues from many more investigations to NBISC in the coming year.

Sample Repository↗

NASA NTRSDate not supplied

Metadata Entry Optimization For NASA's Biological Institutional Scientific Collection (NBISC)

Biospecimen↗