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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 NTRS2013

Coordinated Analyses of Antarctic Sediments as Mars Analog Materials Using Reflectance Spectroscopy and Current Flight-Like Instruments for CheMin, SAM and MOMA

Coordinated analyses of mineralogy and chemistry of sediments from the Antarctic Dry Valleys illustrate how data obtained using flight-ready technology of current NASA and ESA missions can be combined for greater understanding of the samples. Mineralogy was measured by X-ray diffraction (XRD) and visible/ near-infrared (VNIR) reflectance spectroscopy. Chemical analyses utilized a quadrupole mass spectrometer (QMS) to perform pyrolysis-evolved gas analysis (EGA) and gas chromatography-mass spectrometry (GC/MS) both with and without derivatization, as well as laser desorption-mass spectrometry (LD/MS) techniques. These analyses are designed to demonstrate some of the capabilities of near-term landed Mars missions, to provide ground truthing of VNIR reflectance data acquired from orbit by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) on MRO and to provide detection limits for surface- operated instruments: the Chemistry and Mineralogy (CheMin) and Sample Analysis at Mars (SAM) instrument suites onboard Mars Science Laboratory (MSL) and the Mars Organic Molecule Analyzer (MOMA) onboard ExoMars-2018. The new data from this study are compared with previous analyses of the sediments performed with other techniques. Tremolite was found in the oxic region samples for the first time using the CheMin-like XRD instrument. The NIR spectral features of tremolite are consistent with those observed in these samples. Although the tremolite bands are weak in spectra of these samples, spectral features near 2.32 and 2.39 micrometers could be detected by CRISM if tremolite is present on the martian surface. Allophane was found to be a good match to weak NIR features at 1.37-1.41, 1.92, and 2.19 micrometers in spectra of the oxic region sediments and is a common component of immature volcanic soils. Biogenic methane was found to be associated with calcite in the oxic region samples by the SAM/EGA instrument and a phosphoric acid derivative was found in the anoxic region sample using the SAM/MTBSTFA technique.

Mars↗
NASA NTRS2013

NASA Tech Briefs, September 2013

Topics include: ISS Ammonia Leak Detection Through X-Ray Fluorescence; A System for Measuring the Sway of the Vehicle Assembly Building; Fast, High-Precision Readout Circuit for Detector Arrays; Victim Simulator for Victim Detection Radar; Hydrometeor Size Distribution Measurements by Imaging the Attenuation of a Laser Spot; Quasi-Linear Circuit; High-Speed, High-Resolution Time-to-Digital Conversion; Li-Ion Battery and Supercapacitor Hybrid Design for Long Extravehicular Activities; Ultrasonic Low-Friction Containment Plate for Thermal and Ultrasonic Stir Weld Processes; High-Powered, Ultrasonically Assisted Thermal Stir Welding; Next-Generation MKIII Lightweight HUT/Hatch Assembly; Centrifugal Sieve for Gravity-Level-Independent Size; Segregation of Granular Materials; Ion Exchange Technology Development in Support of the Urine Processor Assembly; Nickel-Graphite Composite Compliant Interface and/or Hot Shoe Material; UltraSail CubeSat Solar Sail Flight Experiment; Mechanism for Deploying a Long, Thin-Film Antenna From a Rover; Counterflow Regolith Heat Exchanger; Acquisition and Retaining Granular Samples via a Rotating Coring Bit; Very-Low-Cost, Rugged Vacuum System; Medicine Delivery Device With Integrated Sterilization and Detection; FRET-Aptamer Assays for Bone Marker Assessment, C-Telopeptide, Creatinine, and Vitamin D; Multimode Directional Coupler for Utilization of Harmonic Frequencies from TWTAs; Dual-Polarization, Multi-Frequency Antenna Array for use with Hurricane Imaging Radiometer; Complementary Barrier Infrared Detector (CBIRD) Contact Methods; Autonomous Control of Space Nuclear Reactors; High-Power, High-Speed Electro-Optic Pockels Cell Modulator; Covariance Analysis Tool (G-CAT) for Computing Ascent, Descent, and Landing Errors; Enigma Version 12; Micrometeoroid and Orbital Debris (MMOD) Shield Ballistic Limit Analysis Program; Spitzer Telemetry Processing System; Planetary Protection Bioburden Analysis Program; Wing Leading Edge RCC Rapid Response Damage Prediction Tool (IMPACT2); ISSM: Ice Sheet System Model; Automated Loads Analysis System (ATLAS); Integrated Main Propulsion System Performance Reconstruction Process/Models. Phoenix Telemetry Processor; Contact Graph Routing Enhancements Developed in ION for DTN; GFEChutes Lo-Fi; Advanced Strategic and Tactical Relay Request Management for the Mars Relay Operations Service; Software for Generating Troposphere Corrections for InSAR Using GPS and Weather Model Data; Ionospheric Specifications for SAR Interferometry (ISSI); Implementation of a Wavefront-Sensing Algorithm; Sally Ride EarthKAM - Automated Image Geo-Referencing Using Google Earth Web Plug-In; Trade Space Specification Tool (TSST) for Rapid Mission Architecture (Version 1.2); Acoustic Emission Analysis Applet (AEAA) Software; Memory-Efficient Onboard Rock Segmentation; Advanced Multimission Operations System (ATMO); Robot Sequencing and Visualization Program (RSVP); Automating Hyperspectral Data for Rapid Response in Volcanic Emergencies; Raster-Based Approach to Solar Pressure Modeling; Space Images for NASA JPL Android Version; Kinect Engineering with Learning (KEWL); Spacecraft 3D Augmented Reality Mobile App; MPST Software: grl_pef_check; Real-Time Multimission Event Notification System for Mars Relay; SIM_EXPLORE: Software for Directed Exploration of Complex Systems; Mobile Timekeeping Application Built on Reverse-Engineered JPL Infrastructure; Advanced Query and Data Mining Capabilities for MaROS; Jettison Engineering Trajectory Tool; MPST Software: grl_suppdoc; PredGuid+A: Orion Entry Guidance Modified for Aerocapture; Planning Coverage Campaigns for Mission Design and Analysis: CLASP for DESDynl; and Space Place Prime.

Source record↗
NASA NTRSDate not supplied

Development of Computational Environmental Microbiome Workflows for the Laboratory and the International Space Station

Identification of microorganisms in the spaceflight environment is critical for crew health risk assessment on the International Space Station (ISS). Since 2017, nanopore sequencing technology has been used to support thein situ identification of microbial species during spaceflight. Beginning in 2018, a culture-independent, swab-to-sequencer method was implemented onboard the ISS to provide a more thorough insight of the ISS microbiome. Eliminating microbial culture enables identification of difficult-to-culture organisms, reduces risks associated with potentially pathogenic cultures, and could significantly reduce the time from sample-to-answer. However, this molecular-based approach generates large metagenomic datasets that require substantial computational resources for analysis. To process nanopore-generated sequencing data, the JSC Microbiology Laboratory established a bioinformatics workflow on Amazon EC2 under the security guidance of the NASA Science Managed Cloud Environment (SMCE).This resource allows for the development, testing, and accessing of computational tools for processing large and complex datasets. The work described here will address the downlinking of data from the ISS, the automated pipeline developed to identify targeted bacterial and fungal organisms, and the time from sampling onboard to microbial identification. The pipelines have been enhanced to address high and low biomass samples using optimization based on sample source (air, water, or surface) and type of collection (filter, colony, or swab).The resulting microbiome data can be assessed beyond microbial identifications to gain understanding toward population changes over time, potential selective environmental pressures, and evaluating correlations with a wide range of additional data sets. Metagenome analysis pipelines in development could allow for simultaneous identification of microbial species, gene function, and gene pathways present in the environment. Beyond the ground processing, the developed analysis pipeline is currently deployed onboard the ISS to allow for near real-time assessments of the ISS microbiome. This study serves as a critical foundation for exploration missions, where rapid microbiome analyses will be required.

G. Marie Sharp↗
NASA NTRS2011

Analysis of Noise Exposure Measurements Made Onboard the International Space Station

The International Space Station (ISS) is a unique workplace environment for U.S. astronauts and Russian cosmonauts to conduct research and live for a period of six months or more. Noise has been an enduring environmental physical hazard that has been a challenge for the U.S. space program since before the Apollo era. Noise exposure in ISS poses significant risks to the crewmembers, such as; hearing loss (temporary or permanent), possible disruptions of crew sleep, interference with speech intelligibility and communication, possible interference with crew task performance, and possible reduction in alarm audibility. Acoustic measurements are made aboard ISS and compared to requirements in order to assess the acoustic environment to which the crewmembers are exposed. The purpose of this paper is to describe in detail the noise exposure monitoring program as well as an assessment of the acoustic dosimeter data collected to date. The hardware currently being used for monitoring the noise exposure onboard ISS will be discussed. Acoustic data onboard ISS has been collected since the beginning of ISS (Increment 1, November 2000). Noise exposure data analysis will include acoustic dosimetry logged data from crew-worn during work and sleep periods and also fixed-location measurements from Increment 1 to present day. Noise exposure levels (8-, 16- and 24-hr), LEQ, will also be provided and discussed in this paper. Discussions related to hearing protection will also be included. Future directions and recommendations for the noise exposure monitoring program will be highlighted. This acoustic data is used to ensure a safe and healthy working and living environment for the crewmembers aboard the ISS.

Limardo, Jose G.↗
NASA NTRS2018

NOAA-20 VIIRS Thermal Emissive Bands On-Orbit Performance

The VIIRS (Visible Infrared Imaging Radiometer Suite) instrument onboard the NOAA-20 satellite (launched on November 18, 2017) started to collect Earth-view data after its nadir door opened on December 13, 2017. Seven of the VIIRS bands, I4-5 and M12-16 are thermal emissive bands (TEBs), covering a spectral range from 3.6 to 12.5 meters. They began collecting valid data after the cold focal plane assembly (CFPA) cooled down to its nominal operating temperature on January 6, 2018. This paper will present the performance of each TEB, including calibration coefficients, noise equivalent differential temperature (NEdT), on-orbit calibration coefficient estimates from scheduled onboard blackbody warm-up and cool-down (WUCD) data, as well as related telemetry temperatures. Several methods are tested and compared in the WUCD data analysis for estimating the calibration coefficients. Based on the preliminary results, the NEdT of each band is well below the design specification and very close to that of the VIIRS onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. The detector gains appear stable for bands on the short- and mid-wave infrared CFPA, whereas the detector gains have larger than expected degradation for bands on the long-wave infrared CFPA during the early mission. All TEB-related telemetry temperatures are stable. The on-orbit performance of NOAA-20 VIIRS TEB is compared with VIIRS onboard the SNPP.

Thermal Emissive Bands (TEB)↗
NASA NTRS1991

ATMOS data processing and science analysis methods

The atmospheric trace molecule spectroscopy (ATMOS) instrument, a high-speed Fourier transform spectrometer operating in the middle IR (2.2-16 microns), recorded more than 1500 solar spectra at about 0.0105/cm resolution during its first mission onboard the shuttle Challenger in the spring of 1985. These spectra were acquired during high-sun conditions for studies of the solar atmosphere and during low-sun conditions for studies of the earth's upper atmosphere. This paper describes the steps by which the telemetry data were converted into spectra suitable for analysis, the analysis software and methods developed for the atmospheric and solar studies, and the ATMOS data analysis facility.

Norton, Robert H.↗
NASA NTRS1984

Analysis of LANDSAT-4 TM Data for Lithologic and Image Mapping Purpose

Lithologic mapping techniques using the near infrared bands of the Thematic Mapper onboard the LANDSAT 4 satellite are investigated. These methods are coupled with digital masking to test the capability of mapping geologic materials. Data are examined under medium to low Sun angle illumination conditions to determine the detection limits of materials with absorption features. Several detection anomalies are observed and explained.

Podwysocki, M. H.↗
NASA NTRS1992

Onboard System Processes SAR Data

Aircraft Flight Correlator (AFC) computing system, mounted with AIRSAR synthetic-aperture-radar (SAR) system, supplements recording and postflight analysis, processing portion of data in real or nearly real time to provide imagery for rapid evaluation. Facilitates diagnosis of SAR equipment or adjustment of parameters of experiment. Operates in two modes: "quick-look" mode, data processed in 10 minutes into high-resolution image; and "real-time" mode, data processed in real time to reduced resolution. Also used as aid to navigation.

Carande, Richard E.↗
NASA NTRS2005

The application of support vector machines to analysis of global satellite data sets from MlSR

The Multi-angle Imaging Spectro Radiometer (MISR) is one of a suite of five instruments onboard NASA's Terra EOS satellite, launched in December 1999. Typical satellite imagers view the earth from a single direction, but MISR's cameras image the earth simultaneously from nine different directions in four spectral bands. In this way, MISR provides unique multiangle information about solar radiation scattered from clouds, aerosols and other terrestrial surfaces. One of the primary goals of the MISR mission is to improve our understanding of how clouds and aerosols affect the earth's global energy balance.

support vector machines↗
NASA NTRS1984

LANDSAT-4 horizon scanner performance evaluation

Representative data spans covering a little more than a year since the LANDSAT-4 launch were analyzed to evaluate the flight performance of the satellite's horizon scanner. High frequency noise was filtered out by 128-point averaging. The effects of Earth oblateness and spacecraft altitude variations are modeled, and residual systematic errors are analyzed. A model for the predicted radiance effects is compared with the flight data and deficiencies in the radiance effects modeling are noted. Correction coefficients are provided for a finite Fourier series representation of the systematic errors in the data. Analysis of the seasonal dependence of the coefficients indicates the effects of some early mission problems with the reference attitudes which were computed by the onboard computer using star trackers and gyro data. The effects of sun and moon interference, unexplained anomalies in the data, and sensor noise characteristics and their power spectrum are described. The variability of full orbit data averages is shown. Plots of the sensor data for all the available data spans are included.

Bilanow, S.↗
NASA NTRS2008

Extraction and Analysis of Display Data

The Display Audit Suite is an integrated package of software tools that partly automates the detection of Portable Computer System (PCS) Display errors. [PCS is a lap top computer used onboard the International Space Station (ISS).] The need for automation stems from the large quantity of PCS displays (6,000+, with 1,000,000+ lines of command and telemetry data). The Display Audit Suite includes data-extraction tools, automatic error detection tools, and database tools for generating analysis spread sheets. These spread sheets allow engineers to more easily identify many different kinds of possible errors. The Suite supports over 40 independent analyses, 16 NASA Tech Briefs, November 2008 and complements formal testing by being comprehensive (all displays can be checked) and by revealing errors that are difficult to detect via test. In addition, the Suite can be run early in the development cycle to find and correct errors in advance of testing.

Land, Chris↗
NASA NTRS1997

Technology

Session WA3 includes short reports concerning: (1) Physiolab A Cardio Vascular Laboratory; (2) MEDEX: A Flexible Modular Physiological Laboratory; (3) A Sensate Liner for Personnel Monitoring Applications; (4) Secure Remote Access to Physiological Data; (5) DARA Vestibular Equipment Onboard MIR; (6) The Kinelite Project: A New powerful Motion Analysis System for Spacelab Mission; (7) The Technical Evolution of the French Neurosciences Multipurpose Instruments Onboard the MIR Station; (8) Extended Ground-Based Research in Preparation for Life Sciences Experiments; and (9) MEDES Clinical Research Facility as a Tool to Prepare ISSA Space Flights.

Source record↗
NASA NTRS2020

Study of Antarctic Blowing Snow Storms Using MODIS and CALIOP Observations With a Machine Learning Model

As a common phenomenon over Antarctica, blowing snow (BLSN), especially the large BLSN storms, play an important role in the Antarctic surface mass balance, radiation budget, and planetary boundary layer processes. This study presents the work on BLSN storm identification and analysis with observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Spectral analysis shows that BLSN identification is feasible with MODIS daytime data. A random forest machine learning model is developed and observations from the Cloud‐Aerosol Lidar with Orthogonal Polarization are used for training. Model performance results show that machine‐learning based classification can achieve over 90% overall accuracy when classifying MODIS pixels into cloud, clear, and BLSN categories. The machine learning model is applied to MODIS observations during the month of October 2009 for BLSN storm analysis. Results show that the size of BLSN storms has a large spectrum and can reach hundreds of thousands km2. The MODIS based BLSN storm frequency map extends the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations coverage limit from 82°S to the South Pole. A BLSN storm belt, which extends from the South Pole region to the coastal area between 130°E and 160°E along the Transantarctic Mountains, provides a potential pathway of snow transport. These results are important in improving the understanding of BLSN impact on Antarctic surface mass balance and boundary layer processes.

Antarctic↗
NASA NTRS1983

Solar irradiance variations. II - Analysis of the extreme ultraviolet measurements onboard the Atmosphere Explorer E satellite

Five of the extreme ultraviolet channels (L-alpha, L-beta, He I, He II, Fe XV) measuring irradiance fluctuations on board the AE-E satellite between 1977 and 1980 have been studied in detail. It is shown that the daily variations correspond very closely to the daily variations in solar radio emission (F10.7), but that the UV data are afflicted with serious and to date unrecognized calibration changes during the period of operation of the instruments. In order to correct for these changes, a statistical analysis is carried out, and a set of corrections to the raw data is suggested. The resulting, now uniform, data are then compared with rocket measurements (L-alpha) and data acquired onboard the AE-C satellite (L-beta). Finally the remaining discrepancies are discussed. After concluding that they are below the overall level of uncertainties, a first-order 10-year run of EUV irradiances derived from F10.7 data is proposed. This estimate includes the ratio of irradiance levels between the maxima of solar cycles 20 and 21 and the intervening minimum.

Oster, L.↗
NASA NTRSDate not supplied

Applicability of Loads Estimation Techniques Using Sparse Acceleration Sensor Data to Spacecraft Structural Health Monitoring

The use of structural health monitoring systems on spacecraft structures can play a crucial role in ensuring the safety, reliability, and longevity of the structure by gathering and analyzing onboard sensor data. Of specific importance is monitoring for excessive loading at critical interfaces as any off-nominal structural excitations experienced by spacecraft structures can cause early unpredicted high structural life consumption or damage. The availability and cost of flight-certified sensors along with the size of spacecraft structures and allowable payload mass drives the need for a method to estimate loads using sparsely-located sensors. Numerous approaches such as physics-based, statistical learning, and physics-enhanced statistical learning algorithms have gained popularity among structural prognostics applications. However, developing noise-robust prediction models to assess loads and structural life predictions from a sparse multi-sensor data acquisition system can be a challenging task. This paper discusses the evaluation of physics-based versus machine-learning algorithms for predicting loads and structural life at mission critical locations on the spacecraft structure using a finite element loads analysis with the application of simulated noise and noise reduction techniques. To estimate the loads from accelerations, the physics-based algorithm leverages a loads transformation matrix from a Craig-Bampton reduced finite element model. A System Equivalent Reduction Expansion Process (SEREP) and a pseudo-inverse approach are considered to expand from the onboard sensor degrees of freedom to the Craig-Bampton model degrees of freedom. The machine learning algorithm provides a data driven solution/mapping of the sensor accelerations to the loads at the mission critical locations using a high dimensionality analysis. Although these strategies produce comparable loads prediction without noise, the limitations of these strategies with incorporating simulated noise and noise reduction techniques with low signal to noise ratio signals are evaluated. The study demonstrates the immense potential of statistical learning algorithms for sparse structural prognostic models and enhancing signal denoising techniques. These findings also highlight the need for noise-resilient prognostic models and low-noise data acquisition systems onboard spacecraft structures.

Spacecraft Structural Health Monitoring↗
NASA NTRS1978

ALT space shuttle barometric altimeter altitude analysis

The accuracy was analyzed of the barometric altimeters onboard the space shuttle orbiter. Altitude estimates from the air data systems including the operational instrumentation and the developmental flight instrumentation were obtained for each of the approach and landing test flights. By comparing the barometric altitude estimates to altitudes derived from radar tracking data filtered through a Kalman filter and fully corrected for atmospheric refraction, the errors in the barometric altitudes were shown to be 4 to 5 percent of the Kalman altitudes. By comparing the altitude determined from the true atmosphere derived from weather balloon data to the altitude determined from the U.S. Standard Atmosphere of 1962, it was determined that the assumption of the Standard Atmosphere equations contributes roughly 75 percent of the total error in the baro estimates. After correcting the barometric altitude estimates using an average summer model atmosphere computed for the average latitude of the space shuttle landing sites, the residual error in the altitude estimates was reduced to less than 373 feet. This corresponds to an error of less than 1.5 percent for altitudes above 4000 feet for all flights.

Killen, R.↗