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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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Solar cells used in radiation sensors can be efficiently matched by individual trimming. Strip of aluminized Mylar is used to adjust cell output to within required tolerances. Method is faster than individual selection of matched cells.
An approach for augmenting vibration damping characteristics in space structures with large panels is presented. It is based on generation of bending moments rather than forces. The moments are generated using bimetallic strips, suitably mounted at selected stations on both sides of the large panels, under the influence of differential solar heating, giving rise to thermal gradients and stresses. The collocated angular velocity sensors are utilized in conjunction with mini-servos to regulate the control moments by flipping the bimetallic strips. A simple computation of the rate of dissipation of vibrational energy is undertaken to assess the effectiveness of the proposed approach.
Adaptive Tactical Navigation (ATN) is a laboratory prototype of a knowledge based system to provide navigation system management and decision aiding in the next generation of tactical aircraft. ATN's purpose is to manage a set of multimode navigation equipment, dynamically selecting the best equipment to use in accordance with mission goals and phase, threat environment, equipment malfunction status, and battle damage. ATN encompasses functions as diverse as sensor data interpretation, diagnosis, and planning. Real time issues that were identified in ATN and the approaches used to address them are addressed. Functional requirements and a global architecture for the ATN system are described. Decision making with time constraints are discussed. Two subproblems are identified; making decisions with incomplete information and with limited resources. Approaches used in ATN to address real time performance are described and simulation results are discussed.
The concept, fundamental design principles, and capabilities of the FTS, a multipurpose telerobotic system for use on the Space Station and Space Shuttle, are discussed. The FTS is intended to assist the crew in the performance of extravehicular tasks; the telerobot will also be used on the Orbital Maneuvering Vehicle to service free-flyer spacecraft. The FTS will be capable of both teleoperation and autonomous operation; eventually it may also utilize ground control. By careful selection of the functional architecture and a modular approach to the hardware and software design, the FTS can accept developments in artificial intelligence and newer, more advanced sensors, such as machine vision and collision avoidance.
This paper describes a design for fiber-optic sensing and control in advanced aircraft Electronic Engine Control (EEC). The recommended architecture is an on-engine EEC which contains electro-optic interface circuits for fiber-optic sensors. Size and weight are reduced by multiplexing arrays of functionally similar sensors on a pairs of optical fibers to common electro-optical interfaces. The architecture contains interfaces to seven sensor groups. Nine distinct fiber-optic sensor types were found to provide the sensing functions. Analysis revealed no strong discriminator (except reliability of laser diodes and remote electronics) on which to base a selection of preferred common interface type. A hardware test program is recommended to assess the relative maturity of the technologies and to determine real performance in the engine environment.
Sensors that are sensitive in the infrared spectral region have been under continuous development since the WW2 era. A quest for the military advantage of 'seeing in the dark' has pushed thermal imaging technology toward high spatial and temporal resolution for night vision equipment, fire control, search track, and seeker 'homing' guidance sensing devices. Similarly, scientific applications have pushed spectral resolution for chemical analysis, remote sensing of earth resources, and astronomical exploration applications. As a result of these developments, focal plane arrays (FPA) are now available with sufficient sensitivity for both high spatial and narrow bandwidth spectral resolution imaging over large fields of view. Such devices combined with emerging opto-electronic developments in integrated FPA data processing techniques can yield miniature sensors capable of imaging reflected sunlight in the near IR and emitted thermal energy in the Mid-wave (MWIR) and longwave (LWIR) IR spectral regions. Robotic space sensors equipped with advanced versions of these FPA's will provide high resolution 'pictures' of their surroundings, perform remote analysis of solid, liquid, and gas matter, or selectively look for 'signatures' of specific objects. Evolutionary trends and projections of future low power micro detector FPA developments for day/night operation or use in adverse viewing conditions are presented in the following test.
A high temperature thin film strain gage sensor capable of functioning at temperatures above 1400.degree. C. The sensor contains a substrate, a nanocomposite film comprised of an indium tin oxide alloy, zinc oxide doped with alumina or other oxide semiconductor and a refractory metal selected from the group consisting of Pt, Pd, Rh, Ni, W, Ir, NiCrAlY and NiCoCrAlY deposited onto the substrate to form an active strain element. The strain element being responsive to an applied force.
The NASA Short-term Prediction Research and Transition (SPoRT) Center in Huntsville, AL is running a real-time configuration of the Noah land surface model (LSM) within the NASA Land Information System (LIS) framework (hereafter referred to as the "SPoRT-LIS"). Output from the real-time SPoRT-LIS is used for (1) initializing land surface variables for local modeling applications, and (2) displaying in decision support systems for situational awareness and drought monitoring at select NOAA/National Weather Service (NWS) partner offices. The experimental CONUS run incorporates hourly quantitative precipitation estimation (QPE) from the National Severe Storms Laboratory Multi- Radar Multi-Sensor (MRMS) which will be transitioned into operations at the National Centers for Environmental Prediction (NCEP) in Fall 2014.This paper describes the current and experimental SPoRT-LIS configurations, and documents some of the limitations still remaining through the advent of MRMS precipitation analyses in the SPoRT-LIS land surface model (LSM) simulations.
Labsphere, Inc in conjunction with NASA GSFC and Genesis Engineering Solutions inc. has developed an innovative, fully T-VAC compatible, calibration spectro-radiometric illumination source with a highly uniform large-area rectangular flat panel active area. This source leverages a new shaped integration geometry to achieve high uniformity over a large active output area while maintaining a smaller and more robust overall form-factor when compared to previous designs or integrating spheres. This new illuminator also leverages a set of selectable LED arrays as well as broadband tungsten-halogen sources to provide multi-level, wide spectrum reference illumination to the VIIRS instrument over all optical sensor bands during pre-launch testing. The first unit has been demonstrated to meet its requirements using ambient technology and a fully-vacuum-compatible second unit is planned to allow testing for T-VAC campaigns. This technology is both flexible and capable of meeting the needs of many other instruments/missions requiring this type of test/calibration capability.
For the Orion Multi-Purpose Crew Vehicle (MPCV) project, NASA is minimizing the use of onboard diagnostics, especially external sensors that penetrate the structure. Nonetheless, there is a desire to measure the Thermal Protection System (TPS) recession during reentry. One noninvasive technique currently under investigation is the insertion of indicator metals into the heatshield at varied depths and spatial locations. A remote (airborne) spectrometer detects the emissions from the ionized metal to reveal the time (thus depth) of the metal release. Innovative processing enables the emission features from trace amounts of the selected metals to be reliably detected against the complex and structured spectral background of the shock layer and ablated TPS material. The con-cept has been proven viable through ground testing at NASA HyMETS and AHF arc jet facilities using the Orion TPS material (Avcoat). This presentation highlights the parametric testing that was conducted to select the optimal indicator metals and to assess the accuracy of remote recession measurements using this technique. The CONOPS for integrating the technique into the Orion flight tests is also presented. This includes the onboard indicator metal "seeded plugs" and the offboard airborne sensor platform that would be deployed.
A-train sensors such as MODIS and MISR provide column aerosol properties, and in the process a means of estimating aerosol type (e.g. smoke vs. dust). Correct classification of aerosol type is important because retrievals are often dependent upon selection of the right aerosol model. In addition, aerosol scene classification helps place the retrieved products in context for comparisons and analysis with aerosol transport models. The recent addition of CALIPSO to the A-train now provides a means of classifying aerosol distribution with altitude. CALIPSO level 1 products include profiles of attenuated backscatter at 532 and 1064 nm, and depolarization at 532 nm. Backscatter intensity, wavelength ratio, and depolarization provide information on the vertical profile of aerosol concentration, size, and shape. Thus similar estimates of aerosol type using MODIS or MISR are possible with CALIPSO, and the combination of data from all sensors provides a means of 3D aerosol scene classification. The NASA Goddard Earth Observing System general circulation model and data assimilation system (GEOS-4) provides global 3D aerosol mass for sulfate, sea salt, dust, and black and organic carbon. A GEOS-4 aerosol scene classification algorithm has been developed to provide estimates of aerosol mixtures along the flight track for NASA's Geoscience Laser Altimeter System (GLAS) satellite lidar. GLAS launched in 2003 and did not have the benefit of depolarization measurements or other sensors from the A-train. Aerosol typing from GLAS data alone was not possible, and the GEOS-4 aerosol classifier has been used to identify aerosol type and improve the retrieval of GLAS products. Here we compare 3D aerosol scene classification using CALIPSO and MODIS with the GEOS-4 aerosol classifier. Dust, smoke, and pollution examples will be discussed in the context of providing an initial verification of the 3D GEOS-4 aerosol products. Prior model verification has only been attempted with surface mass comparisons and column optical depth from AERONET and MODIS.
The status of a global-scale model of background aerosol backscatter cross-sections at CO2 wavelengths is described. The model needs, strategy, concept, parameters, and capabilities are addressed, and the data base is discussed, concluding data selection, CO2 backscatter measurements, aerosol optical measurements, aerosol microphysical measurements, water vapor measurements, and data analysis. Strong evidence is reported for a 'universal' background tropospheric aerosol population. Typical background backscatter values at CO2 wavelengths appear to be about 3 x 10 to the -11th to 8 x 10 to the -11th/m/sr. Background signatures are evident in most aerosol data sets which have global-scale coverage in space or time.
This dataset supports a broader study examining wildfire impacts on hydrologic connectivity across 5 sites within the Oak Creek watershed and the resulting biogeochemical impacts. Stream sites were selected using the Advanced Terrestrial Simulator (ATS) hydrologic model to identify locations with varying groundwater contributions and hydrologic responses across different burn severity scenarios. The Retreat Fire burned from July 23 to August 2 in 2024, affecting the five study sites at varying burn severities. Each site is equipped with YSI EXO2 sondes logging sub-hourly throughout the year, and grab samples are collected approximately every six weeks. YSI sondes are used to measure temporally resolved proxies for groundwater inputs (specific conductivity) and organic matter (fluorescent dissolved organic matter; fDOM) along with basic water quality and depth. Grab samples of surface water, groundwater, and precipitation are analyzed for water stable isotopes and conductivity to understand endmembers for hydrologic mixing Grab samples of surface water, groundwater, soil water, and litter/vegetation/soil leachates are analyzed for organic matter composition measured by Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS) to understand organic matter dynamics. Game camera photos are provided in a separate data package available at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3018598. Future versions of this dataset will include time series data from YSI EXO2 sondes (fDOM, dissolved oxygen, temperature, depth, specific conductance, turbidity, pH), BaroTROLL sensors (air temperature and barometric pressure), rain gauges (precipitation), and data from the soil and vegetation samples. Because this study is ongoing, this data package will be updated regularly to include newly collected data and the additional data types. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) a folder of field photos; (2) a folder of surface water sample data; (3) a folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data; (4) a data checks report; (5) file-level metadata; (6) data dictionary; (7) field metadata; (8) readme; (9) international generic sample number (IGSN) mapping file; and (10) field protocols. The sample data subfolder contains (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) total dissolved nitrogen data and averages; (3) stable water isotopes and averages; (4) methods codes; (5) FTICR-MS methods; and (15) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains the processed data and three subfolders, one containing the .xml files, one containing the water CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .xml, .d, .html, .Rmd, .py, .cal, .json, .jpg, .jpeg, .png, .mov, or .mp4.
The Geostationary Lightning Mapper (GLM) is a single channel, near-IR optical detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational. The mission objectives for the GLM are to 1) provide continuous, full-disk lightning measurements for storm warning and Nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 11 year data record of global lightning activity. Instrument formulation studies begun in January 2006 will be completed in March 2007, with implementation expected to begin in September 2007. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite, airborne science missions (e.g., African Monsoon Multi-disciplinary Analysis, AMMA), and regional test beds (e.g, Lightning Mapping Arrays) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data now being provided to selected forecast offices will lead to improved understanding of the application of these data in the severe storm warning process and accelerate the development of the pre-launch algorithms and Nowcasting applications. Proxy data combined with MODIS and Meteosat Second Generation SEVERI observations will also lead to new applications (e.g., multi-sensor precipitation algorithms blending the GLM with the Advanced Baseline Imager, convective cloud initiation and identification, early warnings of lightning threat, storm tracking, and data assimilation).
Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This research exploits recent advancements in bio-optical modeling, cloud computing, and machine learning to enhance our capacity to leverage present and heritage satellite data. Recent research suggests that sensors with low spectral resolution, such as Sentinel 2 and Landsat missions, contain enough hidden spectral variation which can be exploited using data-driven approaches. The availability of three decades of archival imagery will open doors to discover global trends of eutrophication and increased cyanobacteria dominance and provide valuable insight to the development of predictive methodologies. Preliminary efforts in synthetic emulation of global natural inland waters will be discussed and contextualized against satellite radiometric measurement uncertainty, satellite data product uncertainties and causal signal ambiguity over the visible wavelength range, supported by high quality field and image data for selected inland aquatic sites. Insights on water quality estimation via data-driven machine learning models versus matrix inversions will be discussed, and how we can exploit spectral-spatial relationships in high spatial resolution data. A cross-sensor synergistic approach with detailed uncertainty analysis based on optical water types, will allow for unprecedented global snapshots of fine scale ecological dynamics of inland waters.
Fluorophores covalently bound to azacrown ether ionophores can be assembled into sensitive turn-on chemosensors. The size specificity and electron-rich nature of the ionophore's binding domain contribute to both selectivity and strong turn-on fluorescence sensing by various mechanisms when properly constructed. Aza-18-crown-6 ethers are quite selective for binding to K + and Ba 2+ , yet the more electron-withdrawing dicationic nature of barium imposes a larger electronic effect on turn-on fluorescent sensors. Barium chemosensors can be important for measuring soluble Ba 2+ in drinking water and have gained recent attention for their potential to enhance the detection of rare events in xenon decay. Here we quantify the capability of three chemosensors, marketed for biologically useful K + sensing, as effective probes for Ba 2+ ions. Here, we present measurements from bulk spectrofluorometry to characterize the system in aqueous solutions and demonstrate the usefulness of these species for low-background single-ion fluorescence microscopy, revealing new candidates for Ba 2+ sensing.
This article presents Clover, a compiler-directed soft error detection and recovery scheme for lightweight soft error resilience. The compiler carefully generates soft-error-tolerant code based on idempotent processing without explicit checkpoints. During program execution, Clover relies on a small number of acoustic wave detectors deployed in the processor to identify soft errors by sensing the wave made by a particle strike. To cope with DUEs (detected unrecoverable errors) caused by the sensing latency of error detection, Clover leverages a novel selective instruction duplication technique called tail-DMR (dual modular redundancy) that provides a region-level error containment. Once a soft error is detected by either the sensors or the tail-DMR, Clover takes care of the error as in the case of exception handling. To recover from the error, Clover simply redirects program control to the beginning of the code region where the error is detected. The experimental results demonstrate that the average runtime overhead is only 26%, which is a 75% reduction compared to that of the state-of-the-art soft error resilience technique. In addition, this article evaluates an alternative technique called tail-wait, comparing it to Clover. According to the evaluation with the different processor configurations and the various error detection latencies, Clover turns out to be a superior technique, achieving 1.06 to 3.49× speedup over the tail-wait.
The Technology Application Center reviewed abstracted literature sources, and selected document data and data gathering techniques which were performed or obtained remotely from space, aircraft or groundbased stations. All of the documentation was related to remote sensing sensors or the remote sensing of the natural resources. Sensors were primarily those operating within the 10 to the minus 8 power to 1 meter wavelength band. Included are NASA Tech Briefs, ARAC Industrial Applications Reports, U.S. Navy Technical Reports, U.S. Patent reports, and other technical articles and reports.