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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRS1974

Method and apparatus for checking fire detectors

A fire detector checking method and device are disclosed for nondestructively verifying the operation of installed fire detectors of the type which operate on the principle of detecting the rate of temperature rise of the ambient air to sound an alarm and/or which sound an alarm when the temperature of the ambient air reaches a preset level. The fire alarm checker uses the principle of effecting a controlled simulated alarm condition to ascertain wheather or not the detector will respond. The checker comprises a hand-held instrument employing a controlled heat source, e.g., an electric lamp having a variable input, for heating at a controlled rate an enclosed mass of air in a first compartment, which air mass is then disposed about the fire detector to be checked. A second compartment of the device houses an electronic circuit to sense and adjust the temperature level and heating rate of the heat source.

Clawson, G. T.↗
NASA NTRS2023

Measurement Report :Emission Factors of NH3 and NHx for Wildfires and Agricultural Fires in the United States

During the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) study, the NASA DC-8 carried out in situ chemical measurements in smoke plumes emitted from wildfires and agricultural fires in the contiguous United States. The DC-8 payload included a modified proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS) for the fast measurement of gaseous ammonia (NH 3 ) and a high-resolution time-of-flight aerosol mass spectrometer (AMS) for the fast measurement of submicron particulate ammonium (NH 4 + ). We herein report data collected in smoke plumes emitted from 6 wildfires in the Western United States, 2 prescribed grassland fires in the Central United States, 1 prescribed forest fire in the Southern United States, and 66 small agricultural fires in the Southeastern United States. Smoke plumes contained double to triple digit ppb levels of NH 3 . In the wildfire plumes, a significant fraction of NH 3 had already been converted to NH 4 + at the time of sampling (≥2 h after emission). Substantial amounts of NH 4 + were also detected in freshly emitted smoke from corn and rice field fires. We herein present a comprehensive set of emission factors of NH 3 and NH x , with NH x = NH 3 + NH 4 + . Average NH 3 and NH x emission factors for wildfires in the Western United States were 1.86±0.75 g kg −1 and 2.47±0.80 g kg −1 of fuel burned, respectively. Average NH 3 and NH x emission factors for agricultural fires in the Southeastern United States were 0.89±0.58 and 1.74±0.92 g kg −1 , respectively. Our data show no clear inverse correlation between modified combustion efficiency (MCE) and NH 3 emissions. The observed NH 3 emissions were significantly higher than measured in previous laboratory experiments in the FIREX FireLab 2016 study.

Laura Tomsche↗
NASA NTRS1980

Space Borne Event Timer

The Space Borne Event Timer is a part of the NASA laser ranging system that is intended to operate aboard the Space Shuttle orbiting over California. The object is to measure, by laser ranging, the earth movement along the San Andreas fault and possibly forecast future earthquakes. A number of cube reflector targets will be placed along both sides of the fault. The ranging system aboard the Space Shuttle will fire a burst of laser pulses at each target and detect the reflected light. Time differences between pulses from the two sides of the fault will indicate earth displacements. The Space Borne Event Timer is a CAMAC compatible system that provides extremely accurate timing data and controls the operation of the ranging system. For each event the time is given in 19.53 increments from the instant of firing the laser to the instant the reflected light is received back, within a range of 130 days.

Turko, B.↗
NASA NTRS2006

Real-Time Radiographic In-Situ Characterization Of Ply Lift In Composite Aerospace Materials

The problem of ply lifting in composite materials is a significant issue for various aerospace and military applications. A fundamental element in the prevention or mitigation of ply lift is determination of the timing of the ply lifting event during exposure of the composite material to flight conditions. The Marshall Space Flight Center s Nondestructive Evaluation Team developed a real-time radiographic technique for the detection of ply lift in carbon phenolic ablative materials in situ during live firings of subscale test motors in support of NASA s Reusable Solid Rocket Motor program, using amorphous silicon detector panels. The radiographic method has successfully detected ply lifting in seven consecutive carbon phenolic converging cones attached to solid fuel torches, providing the time of ply lift initiation in each test. Post-processing of the radiographic images improved the accuracy of timing measurements and allowed measurement of the ply lifting height as a function of time. Radiographic data correlated well with independent pressure and temperature measurements that indicate the onset of ply lift in the nozzle material.

Beshears, Ronald D.↗
NASA NTRS1985

History, design and performance of the space shuttle hazardous gas detection system

The Hazardous Gas Detection System (HGDS) is designed to detect leaks hich could result in pre-launch or in-flight fires or explosions resulting from the presence of cryogenic rocket propellants. The historical development, design, and performance of the HGDS is discussed. Data for response time, detection limits, accuracy, and drift are presented. Finally, present and future applications are discussed, and some general conclusions are drawn.

Helms, W. R.↗
NASA NTRSDate not supplied

A Comparison of Stratospheric Smoke Events Seen by SAGE II (1984-2005) and SAGE III (2017-2021)

The Stratospheric Aerosol and Gas Experiment (SAGE II) operated for over 20 years (1984-2005) and collected near global observations of aerosol extinction coefficient from the upper troposphere to about40 km. SAGE III has flown aboard the International Space Station (ISS) since 2017 and has collected comparable aerosol measurements during this period. The SAGE II record is dominated by recovery from El Chichón (1982) and the Pinatubo eruption of 1991 with a few smaller volcanic events that are detectable in the data set. In addition, a number of pyrocumulus events injected smoke into the lower stratosphere with magnitudes that are comparable to the smaller volcanic events. The shorter SAGE III/ISS record is qualitatively different than SAGE II’s with a number of small to moderate volcanic eruptions in both low and high latitudes (e.g., Ambae in 2018 and Raikoke in 2020) as well several smokeevents including the two largest events seen by SAGE-like instruments: the BC pyrocumulus event in 2017 and the Australian brush fires of 2020/2021. In this paper, I will review the detection and quantification of the magnitude of these smoke events with a particular focus on the differences between the two data periods.

SAGE↗
NASA NTRSDate not supplied

Quantifying SAGE II (1984-2005) and SAGE III/ISS (2017-2022) Observations of Smoke in the Stratosphere

The Stratospheric Aerosol and Gas Experiment (SAGE II) operated for over 20 years (1984-2005) and collected near global observations of aerosol extinction coefficient from the upper troposphere to about 40 km. SAGE III has flown aboard the International Space Station (ISS) since 2017 and has collected comparable aerosol measurements during this period. The SAGE II record is dominated by recovery from El Chichón (1982) and the Pinatubo eruption of 1991 with a few smaller volcanic events that are detectable in the data set. In addition, a number of pyrocumulus events injected smoke into the lower stratosphere with magnitudes that are comparable to the smaller volcanic events. The shorter SAGE III/ISS record is qualitatively different than SAGE II’s with a number of small to moderate volcanic eruptions in both low and high latitudes (e.g., Ambae in 2018 and Raikoke in 2020) as well several smoke events including the two largest events seen by SAGE-like instruments: the BC pyrocumulus event in 2017 and the Australian brush fires of 2020/2021. In this paper, I will review the detection and quantification of the magnitude of these smoke events with a particular focus on the differences between the two data periods.

Smoke↗
NASA NTRSDate not supplied

Quantifying SAGE II (1984-2005) and SAGE III/ISS (2017-2022) Observations of Smoke in the Stratosphere

The Stratospheric Aerosol and Gas Experiment (SAGE II) operated for over 20 years (1984-2005) and collected near global observations of aerosol extinction coefficient from the upper troposphere to about 40 km. SAGE III has flown aboard the International Space Station (ISS) since 2017 and has collected comparable aerosol measurements during this period. The SAGE II record is dominated by recovery from El Chichón (1982) and the Pinatubo eruption of 1991 with a few smaller volcanic events that are detectable in the data set. In addition, a number of pyrocumulus events injected smoke into the lower stratosphere with magnitudes that are comparable to the smaller volcanic events. The shorter SAGE III/ISS record is qualitatively different than SAGE II’s with a number of small to moderate volcanic eruptions in both low and high latitudes (e.g., Ambae in 2018 and Raikoke in 2020) as well several smoke events including the two largest events seen by SAGE-like instruments: the BC pyrocumulus event in 2017 and the Australian brush fires of 2020/2021. In this talk, I will review the detection and quantification of the magnitude of these smoke events with a particular focus on the differences between the two data periods.

Smoke Stratosphere SAGE↗
NASA NTRSDate not supplied

Quantifying SAGE II (1984-2005) and SAGE III/ISS (2017-2022) Observations of Smoke in the Stratosphere

The Stratospheric Aerosol and Gas Experiment (SAGE II) operated for over 20 years (1984-2005) and collected near global observations of aerosol extinction coefficient from the upper troposphere to about 40 km. SAGE III has flown aboard the International Space Station (ISS) since 2017 and has collected comparable aerosol measurements during this period. The SAGE II record is dominated by recovery from El Chichón (1982) and the Pinatubo eruption of 1991 with a few smaller volcanic events that are detectable in the data set. In addition, a number of pyrocumulus events injected smoke into the lower stratosphere with magnitudes that are comparable to the smaller volcanic events. The shorter SAGE III/ISS record is qualitatively different than SAGE II’s with a number of small to moderate volcanic eruptions in both low and high latitudes (e.g., Ambae in 2018 and Raikoke in 2020) as well several smoke events including the two largest events seen by SAGE-like instruments: the BC pyrocumulus event in 2017 and the Australian brush fires of 2020/2021. In this talk, I will review the detection and quantification of the magnitude of these smoke events with a particular focus on the differences between the two data periods.

SAGE↗
NASA NTRS1993

LVQ and backpropagation neural networks applied to NASA SSME data

Feedfoward neural networks with backpropagation learning have been used as function approximators for modeling the space shuttle main engine (SSME) sensor signals. The modeling of these sensor signals is aimed at the development of a sensor fault detection system that can be used during ground test firings. The generalization capability of a neural network based function approximator depends on the training vectors which in this application may be derived from a number of SSME ground test-firings. This yields a large number of training vectors. Large training sets can cause the time required to train the network to be very large. Also, the network may not be able to generalize for large training sets. To reduce the size of the training sets, the SSME test-firing data is reduced using the learning vector quantization (LVQ) based technique. Different compression ratios were used to obtain compressed data in training the neural network model. The performance of the neural model trained using reduced sets of training patterns is presented and compared with the performance of the model trained using complete data. The LVQ can also be used as a function approximator. The performance of the LVQ as a function approximator using reduced training sets is presented and compared with the performance of the backpropagation network.

Doniere, Timothy F.↗
NASA NTRSDate not supplied

Improving Satellite-Based Hotspot Detection Through Deep Learning-Enabled Smoke Recognition

While geostationary satellites, such as the GOES-R series, provide wildland fire hotspot readings at a high temporal resolution, they are prone to false negative readings and decreased confidence. One cause of decreased hotspot confidence is cloud contamination. Smoke produced from wildfire is often misinterpreted as cloud contamination, resulting in inaccurate and unsure sensor readings. To this end, we built a deep learning image segmentation model to identify smoke and cloud in true color satellite images. The model is pre-trained using self-supervised learning on over 10,000 GOES-R images to learn the underlying structure of satellite imagery. Then, the model is fine-tuned on a set of 130 labeled documents using supervised learning. The resulting model performs multi-class image segmentation with 85% accuracy and runs in under a minute on a standard personal computer. When paired alongside hotspot data, the model’s outputs can help increase confidence in wildfire location by identifying cases of cloud contamination that are due to smoke. The resulting model can be deployed in a stand-alone application or bundled in an Open Data Integration for wildland fire management (ODIN) application.

Earth observation↗
NASA NTRS2008

Lab-on-a-Chip: From Astrobiology to the International Space Station

The continual and long-term habitation of enclosed environments, such as Antarctic stations, nuclear submarines and space stations, raises unique engineering, medical and operational challenges. There is no easy way out and no easy way to get supplies in. This situation elevates the importance of monitoring technology that can rapidly detect events within the habitat that affect crew safety such as fire, release of toxic chemicals and hazardous microorganisms. Traditional methods to monitor microorganisms on the International Space Station (ISS) have consisted of culturing samples for 3-5 days and eventual sample return to Earth. To augment these culture methods with new, rapid molecular techniques, we developed the Lab-on-a-Chip Application Development - Portable Test System (LOCAD-PTS). The system consists of a hand-held spectrophotometer, a series of interchangeable cartridges and a surface sampling/dilution kit that enables crew to collect samples and detect a range of biological molecules, all within 15 minutes. LOCAD-PTS was launched to the ISS aboard Space Shuttle Discovery in December 2006, where it was operated for the first time during March-May 2007. The surfaces of five separate sites in the US Lab and Node 1 of ISS were analyzed for endotoxin, using cartridges that employ the Limulus Amebocyte Lysate (LAL) assay; results of these tests will be presented. LOCAD-PTS will remain permanently onboard ISS with new cartridges scheduled for launch in February and October of 2008 for the detection of fungi (Beta-glucan) and Gram-positive bacteria (lipoteichoic acid), respectively.

Maule, Jake↗
NASA NTRS2005

Pre-Launch GOES-R Risk Reduction Activities for the Geostationary Lightning Mapper

The GOES-R Geostationary Lightning Mapper (GLM) is a new instrument planned for GOES-R that will greatly improve storm hazard nowcasting and increase warning lead time day and night. Daytime detection of lightning is a particularly significant technological advance given the fact that the solar illuminated cloud-top signal can exceed the intensity of the lightning signal by a factor of one hundred. Our approach is detailed across three broad themes which include: Data Processing Algorithm Readiness, Forecast Applications, and Radiance Data Mining. These themes address how the data will be processed and distributed, and the algorithms and models for developing, producing, and using the data products. These pre-launch risk reduction activities will accelerate the operational and research use of the GLM data once GOES-R begins on-orbit operations. The GLM will provide unprecedented capabilities for tracking thunderstorms and earlier warning of impending severe and hazardous weather threats. By providing direct information on lightning initiation, propagation, extent, and rate, the GLM will also capture the updraft dynamics and life cycle of convective storms, as well as internal ice precipitation processes. The GLM provides information directly from the heart of the thunderstorm as opposed to cloud-top only. Nowcasting applications enabled by the GLM data will expedite the warning and response time of emergency management systems, improve the dispatch of electric power utility repair crews, and improve airline routing around thunderstorms thereby improving safety and efficiency, saving fuel and reducing delays. The use of GLM data will assist the Bureau of Land Management (BLM) and the Forest Service in quickly detecting lightning ground strikes that have a high probability of causing fires. Finally, GLM data will help assess the role of thunderstorms and deep convection in global climate, and will improve regional air quality and global chemistry/climate modeling. The GLM has a robust design that benefits and improves upon its strong heritage of NASA-developed LEO predecessors, the Optical Transient Detector (OTD) and the Lightning Imaging Sensor (LIS). GLM will have a substantially larger number of pixels within the focal plane, two lens systems, and multiple Real-Time Event Processors REPS for on-board event detection and data compression to provide continuous observations of the Americas and adjacent oceans.

Goodman, S. J.↗
NASA NTRS1991

Development of life prediction capabilities for liquid propellant rocket engines. Post-fire diagnostic system for the SSME system architecture study

This system architecture task (1) analyzed the current process used to make an assessment of engine and component health after each test or flight firing of an SSME, (2) developed an approach and a specific set of objectives and requirements for automated diagnostics during post fire health assessment, and (3) listed and described the software applications required to implement this system. The diagnostic system described is a distributed system with a database management system to store diagnostic information and test data, a CAE package for visual data analysis and preparation of plots of hot-fire data, a set of procedural applications for routine anomaly detection, and an expert system for the advanced anomaly detection and evaluation.

Gage, Mark↗
NASA NTRS1992

Airborne Remote Sensing

NASA imaging technology has provided the basis for a commercial agricultural reconnaissance service. AG-RECON furnishes information from airborne sensors, aerial photographs and satellite and ground databases to farmers, foresters, geologists, etc. This service produces color "maps" of Earth conditions, which enable clients to detect crop color changes or temperature changes that may indicate fire damage or pest stress problems.

Source record↗
NASA NTRS2013

Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology

Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolution, and the applied statistical method. Here we compare the performance of a wide range of trend estimation methods and demonstrate that performance decreases with increasing inter-annual variability in the NDVI time series. Trend slope estimates based on annual aggregated time series or based on a seasonal-trend model show better performances than methods that remove the seasonal cycle of the time series. A breakpoint detection analysis reveals that an overestimation of breakpoints in NDVI trends can result in wrong or even opposite trend estimates. Based on our results, we give practical recommendations for the application of trend methods on long-term NDVI time series. Particularly, we apply and compare different methods on NDVI time series in Alaska, where both greening and browning trends have been previously observed. Here, the multi-method uncertainty of NDVI trends is quantified through the application of the different trend estimation methods. Our results indicate that greening NDVI trends in Alaska are more spatially and temporally prevalent than browning trends. We also show that detected breakpoints in NDVI trends tend to coincide with large fires. Overall, our analyses demonstrate that seasonal trend methods need to be improved against inter-annual variability to quantify changing trends in ecosystem productivity with higher accuracy.

Inter-Annual↗
NASA NTRS1988

Development of an advanced failure detection algorithm for the SSME

Since ground testing of the Space Shuttle Main Engine began in 1975, a multifaceted detection/shutdown system has been used to detect engine anomalies and initiate shutdown. In spite of this system, 27 major incidents have occurred since testing began. Although a small percentage of over 1200 successful hot fire tests, such incidents can result in significant cost and schedule impacts. Therefore, an advanced failure detection algorithm has been developed which consists of three detection approaches designed for a failure characteristic that has been previously documented. Simulations using sensor data from five incident tests show the algorithm detecting an anomally and signalling shutdown prior to the redline cutoff time. The foundation elements of the algorithm and its functioning are described.

Norman, A.↗
NASA NTRS1985

Reliability of void detection in structural ceramics using scanning laser acoustic microscopy

The reliability of scanning laser acoustic microscopy (SLAM) for detecting surface voids in structural ceramic test specimens was statistically evaluated. Specimens of sintered silicon nitride and sintered silicon carbide, seeded with surface voids, were examined by SLAM at an ultrasonic frequency of 100 MHz in the as fired condition and after surface polishing. It was observed that polishing substantially increased void detectability. Voids as small as 100 micrometers in diameter were detected in polished specimens with 0.90 probability at a 0.95 confidence level. In addition, inspection times were reduced up to a factor of 10 after polishing. The applicability of the SLAM technique for detection of naturally occurring flaws of similar dimensions to the seeded voids is discussed. A FORTRAN program listing is given for calculating and plotting flaw detection statistics.

Roth, D. J.↗