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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 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Deep spectral CNN for laser induced breakdown spectroscopy

This work proposes a spectral convolutional neural network (CNN) operating on laser induced breakdown spectroscopy (LIBS) signals to learn to (1) disentangle spectral signals from the sources of sensor uncertainty (i.e., pre-process) and (2) get qualitative and quantitative measures of chemical content of a sample given a spectral signal (i.e., calibrate). Once the spectral CNN is trained, it can accomplish either task through a single feed-forward pass, with real-time benefits and without any additional side information requirements including dark current, system response, temperature and detector-to-target range. Our experiments demonstrate that the proposed method outperforms the existing approaches used by the Mars Science Lab for pre-processing and calibration for remote sensing observations from the Mars rover, ‘Curiosity’.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1986

The VEGA Venus balloon experiment

In June 1985, two instrumented balloons were placed in the atmosphere of Venus as part of the VEGA mission. Each balloon traveled about 30 percent of the way around the planet at a float altitude near 54 kilometers. In situ sensors measured pressure, temperature, vertical wind velocity, cloud particle backscatter, ambient light level, and frequency of lightning. A ground-based network of 20 radio antennas tracked the balloons by very long baseline interferometry (VLBI) techniques to monitor the Venus winds. The history, organization, and principal characteristics of this international balloon experiment are described.

Sagdeev, R. Z.↗
NASA NTRS1987

Development of a graphical display on the DMS test bed

The DMS test bed is a model of a data network aboard space station. Users of the network share data relevant to the functional status of various systems aboard the station. Users may inquire the status of myriad sensors, obtaining reading of station subsystem status in real time via the Data Acquisition and Distribution Service. A graphical display of the status of a simulation of the Environmental Control and Life Support System was developed. Two broad issues were addressed: (1) flexible, extensible software design; and (2) the impact of utilizing standard processors, languages, and graphics packages implementing the software design concept. The experience gained with DEC hardware, the DEC implementation of the GKS graphics standard, and with Ada is summarized.

Donnelly, Robert A.↗
NASA NTRS1989

Neural networks in support of manned space

Many lobbyists in Washington have argued that artificial intelligence (AI) is an alternative to manned space activity. In actuality, this is the opposite of the truth, especially as regards artificial neural networks (ANNs), that form of AI which has the greatest hope of mimicking human abilities in learning, ability to interface with sensors and actuators, flexibility and balanced judgement. ANNs and their relation to expert systems (the more traditional form of AI), and the limitations of both technologies are briefly reviewed. A Few highlights of recent work on ANNs, including an NSF-sponsored workshop on ANNs for control applications are given. Current thinking on ANNs for use in certain key areas (the National Aerospace Plane, teleoperation, the control of large structures, fault diagnostics, and docking) which may be crucial to the long term future of man in space is discussed.

Werbos, Paul J.↗
NASA NTRS1994

Rainfall variability over the tropical Pacific from July 1987 through December 1991 as inferred via monthly estimates from SSM/I

Estimates of monthly rainfall have been computed over the tropical Pacific using passive microwave satellite observations from the Special Sensor Microwave/Imager (SSM/I) for the preiod from July 1987 through December 1991. The monthly estimates were calibrated using measurements from a network of Pacific atoll rain gauges and compared to other satellite-based rainfall estimation techniques. Based on these monthly estimates, an analysis of the variability of large-scale features over intraseasonal to interannual timescales has been performed. While the major precipitation features as well as the seasonal variability distributions show good agreement with expected values, the presence of a moderately intense El Nino during 1986-87 and an intense La Nina during 1988-89 highlights this time period.

Berg, Wesley↗
NASA NTRS1999

The 1997-98 El Nino Event and Related Wintertime Lightning Variations in the Southeastern United States

The El Nino Southern Oscillation (ENSO) is a climate anomaly responsible for world-wide weather impacts ranging from droughts to floods. In the United States, warm episode years are known to produce above normal rainfall along the Southeast US Gulf Coast and into the Gulf of Mexico, with the greatest response observed in the October-March period of the latest warm-episode year. The 1997-98 warm episode, notable for being the strongest event since 1982-83, presents our first opportunity to examine the response to a major ENSO event and determine the variation of wintertime thunderstorm activity in this part of the world. Due to the recent launch of a lightning sensor on NASA's Tropical Rainfall Measuring Mission (TRMM) in November 1997 and the expanded coverage of the National Lightning Detection Network (NLDN), we are able to examine such year-to-year changes in lightning activity with far greater detail than ever before.

Goodman, S. J.↗
NASA NTRS1999

The 1997-98 El-Nino Event and Related Lightning Variations in the Southeastern United States

The El Nino Southern Oscillation (ENSO) is a climate anomaly responsible for world-wide weather impacts ranging from droughts to floods. In the United States, warm episode years are known to produce above normal rainfall along the Southeast US Gulf Coast and into the Gulf of Mexico, with the greatest response observed in the October-March period of the current warm-episode year. The 1997-98 warm episode, notable for being the strongest event since 1982-83, presents our first opportunity to examine the response to a major ENSO event and determine the variation of wintertime thunderstorm activity in this part of the world. Due to the recent launch of a lightning sensor on NASA's Tropical Rainfall Measuring Mission (TRMM) in November 1997 and the expanded coverage of the National Lightning Detection Network (NLDN), we are able to examine such year-to-year changes in lightning activity with far greater detail than ever before.

Buechler, D. E.↗
NASA NTRS1999

The 1997-98 El Nino Event and Related Wintertime Lightning Variations in the Southeastern United States

The El Nino Southern Oscillation (ENSO) is a climate anomaly responsible for world-wide weather impacts ranging from droughts to floods. In the United States, warm episode years are known to produce above normal rainfall along the Southeast US Gulf Coast and into the Gulf of Mexico, with the greatest response observed in the October-March period of the latest warm-episode year. The 1997-98 warm episode is notable for being the strongest event since 1982-83. With the recent launch of a lightning sensor on NASA's Tropical Rainfall Measuring Mission (TRMM) in November 1997 and the expanded coverage of the National Lightning Detection Network (NLDN), such year-to-year changes in lightning activity can be examined with far greater detail than ever before. For the recent ENSO event the greatest year-to-year changes in lightning frequency occur within a broad swath across the northern Gulf of Mexico basin where there is nearly a 200% increase in lightning days year-to-year (35 days in 1997-98 vs. 13 days in 1996-97) and a 200% increase in lightning hours (150 hours vs. 50). These changes occur in association with a 100% increase in the number of synoptic scale cyclones and an anomalously strong jet stream encompassing much of the Gulf basin.

Goodman, S. J.↗
NASA NTRS1997

Use of the Aromascan(TM) Instrument for Nonsubjective Evaluation of Rodent Spaceflight Hardware

This report describes the verification and utilization of the AromaScan(TM) (Hollis, NH) instrument for the ground-based evaluation of odor containment by various spaceflight habitats developed at NASA's Ames Research Center (ARC). The AromaScan(TM) instrument is an electronic odor detection system consisting of 32 polymer sensors that respond differentially to 10 different chemical groups present in an air sample. The AromaScan(TM) system also includes neural network software for constructing a database of known odors, against which an unknown odor can be compared. At present, the standard method for characterizing rodent odor containment during the development and testing of spaceflight hardware is the use of a human odor assessment panel. However, this can be a very time consuming and costly process, and the results are inherently subjective. The AromaScan(TM) system should produce more consistent and objective results, as well as a cost savings in the long term. To test and verify the AromaScan(TM) instrument, daily air samples will be collected from the exhaust port of rodent habitats, during experiment development tests, then injected into the instrument and used to create a database of recognizable odors. Human sniff tests will be performed in conjunction with the AromaScan(TM) analysis, and the results will be correlated. We will then teach the neural network to differentiate between an acceptable and an unacceptable odor profile, as defined by the human sniff test, and to be able to accurately identify an odor that would not pass a sniff panel. The results of our efforts will be to verify that the AromaScan(TM) system is a valuable alternative to human sniff panel assessments for the early iterative process of designing and testing rodent waste filters for spaceflight. Acceptance by a human panel will remain one of the final criteria for successful rodent habitat development.

Scribner, K. A.↗
NASA NTRS2004

Examples of Current and Future Uses of Neural-Net Image Processing for Aerospace Applications

Feed forward artificial neural networks are very convenient for performing correlated interpolation of pairs of complex noisy data sets as well as detecting small changes in image data. Image-to-image, image-to-variable and image-to-index applications have been tested at Glenn. Early demonstration applications are summarized including image-directed alignment of optics, tomography, flow-visualization control of wind-tunnel operations and structural-model-trained neural networks. A practical application is reviewed that employs neural-net detection of structural damage from interference fringe patterns. Both sensor-based and optics-only calibration procedures are available for this technique. These accomplishments have generated the knowledge necessary to suggest some other applications for NASA and Government programs. A tomography application is discussed to support Glenn's Icing Research tomography effort. The self-regularizing capability of a neural net is shown to predict the expected performance of the tomography geometry and to augment fast data processing. Other potential applications involve the quantum technologies. It may be possible to use a neural net as an image-to-image controller of an optical tweezers being used for diagnostics of isolated nano structures. The image-to-image transformation properties also offer the potential for simulating quantum computing. Computer resources are detailed for implementing the black box calibration features of the neural nets.

Decker, Arthur J.↗
NASA NTRS2019

Rapid Development of the Seeker Free-Flying Inspector Guidance, Navigation, and Control System

Seeker is an automated extravehicular free-flying inspector CubeSat designed and built in-house at the Johnson Space Center (JSC). As a Class 1E project funded by the International Space Station (ISS) Program, Seeker had a streamlined process to flight certification, but the vehicle had to be designed, developed, tested, and delivered within approximately one year after authority to pro-ceed (ATP) and within a $1.8 million budget. These constraints necessitated an expedited Guidance, Navigation, and Control (GNC) development schedule, development began with a navigation sensor trade study using Linear Covariance (LinCov) analysis and a rapid sensor downselection process, resulting in the use of commercial off-the-shelf (COTS) sensors which could be procured quickly and subjected to in-house environmental testing to qualify them for flight. A neural network was used to enable a COTS camera to provide bearing measurements for visual navigation. The GNC flight software (FSW) algorithms utilized lean development practices and leveraged the Core Flight Software (CFS) architecture to rapidly develop the GNC system, tune the system parameters, and verify performance in simulation. This pace was anchored by several Hardware-Software Integration (HSI) milestones, which forced the Seeker GNC team to develop the interfaces both between hardware and software and between the GNC domains early in the project and to enable a timely delivery.

Sullivan, Jacob↗
NASA NTRS2019

Rapid Development of the Seeker Free-Flying Inspector Guidance, Navigation, and Control System

Seeker is an automated extravehicular free-flying inspector CubeSat designed and built in-house at the Johnson Space Center (JSC). As a Class 1E project funded by the International Space Station (ISS) Program, Seeker had a stream-lined process to flight certification, but the vehicle had to be designed, developed, tested, and delivered within approximately one year after authority to proceed (ATP) and within a $1.8 million budget. These constraints necessitated an expedited Guidance, Navigation, and Control (GNC) development schedule. Development began with a navigation sensor trade study using Linear Covariance (LinCov) analysis and a rapid sensor down-selection process, resulting in the use of commercial off-the-shelf (COTS) sensors which could be procured quickly and subjected to in-house environmental testing to qualify them for flight. A neural network was used to enable a COTS camera to provide bearing measure-ments for visual navigation. The GNC flight software (FSW) algorithms utilized lean development practices and leveraged the Core Flight Software (CFS) architecture to rapidly develop the GNC system, tune the system parameters, and verify performance in simulation. This pace was anchored by several Hardware-Software Integration (HSI) milestones, which forced the Seeker GNC team to develop the interfaces both between hardware and software and between the GNC domains early in the project and to enable a timely delivery.

Sullivan, Jake↗
DOE OSTI2019

Real-time metocean sensor arrays

A real-time metocean sensor array system may include a one or more floating instruments each including geolocation capabilities and connected to a satellite communication network. In some examples, the floating instruments may further include an omnidirectional hydrophone. Motion and acoustical data gathered by the instruments may be converted by onboard processing logic into wave, current, and/or wind-related observations that may be communicated in real time and analyzed via a cloud-based system.

58 GEOSCIENCES↗
NASA NTRS1972

Evaluation of thermal network correction program using test temperature data

An evaluation process to determine the accuracy of a computer program for thermal network correction is discussed. The evaluation is required since factors such as inaccuracies of temperatures, insufficient number of temperature points over a specified time period, lack of one-to-one correlation between temperature sensor and nodal locations, and incomplete temperature measurements are not present in the computer-generated information. The mathematical models used in the evaluation are those that describe a physical system composed of both a conventional and a heat pipe platform. A description of the models used, the results of the evaluation of the thermal network correction, and input instructions for the thermal network correction program are presented.

Ishimoto, T.↗
NASA NTRS2019

NASA GEOS Aerosol Modeling and Assimilation Activities

The current assimilation of Aerosol Optical Depth (AOD) in GEOS involves very careful cloud screening and homogenization of the observing system by means of a neural network that translates satellite reflectances from MODIS into AERONET calibrated AOD. In this talk we will present an update of the GEOS aerosol assimilation system, with emphasis on the improved treatment of MODIS observations. We will then proceed to assess the impact of geostationary aerosol observations from the ABI and AHI sensors on the GOES-16 and Himawari-8 satellites. The GEOS assimilated aerosol fields will be validated by comparison to independent in-situ and remotely-sensed measurements (PM2.5 concentrations, surface dust concentrations, Maritime Aerosol Network, airborne and ground based lidars, UV based measurements, etc.).

Castellanos, Patricia↗
NASA NTRS2010

An Overview of the Micro Pulse Lidar Network (MPLNET)

The NASA Micro Pulse Lidar Network (MPLNET) is a federated network of Micro Pulse Lidar (MPL) systems designed to measure aerosol and cloud vertical structure continuously, day and night, over long time periods required to contribute to climate change studies and provide ground validation for models and satellite sensors in the NASA Earth Observing System (FOS). At present, there are eighteen active sites worldwide, and several more in the planning stage. Numerous temporary sites are deployed in support of various field campaigns. Most MPLNET sites are co-located with sites in the NASA Aerosol Robotic Network (AERONET) to provide both column and vertically resolved aerosol and cloud data. MPLNET data and more information on the project are available at http://mpinet.gsfc.nasa.gov . Here we present a summary of the first ten years of MPLNET, along with an overview of our current status, specifically our version two data products and applications. Future network plans will be presented, with a focus on our activities in South East Asia.

Welton, Ellsworth↗
DOE OSTI2021

Characterization of mechanical discontinuities based on data-driven classification of compressional-wave travel times

Wave propagation and diffusive transport phenomena are influenced by the mechanical discontinuities in material. Here this study shows that certain bulk properties of the network of low-velocity mechanical discontinuities (e.g. air-filled cracks) in a material can be characterized by processing compressional-wave travel times using traditional data-driven classification techniques. To that end, we perform three tasks in chronological order: (1) use the discrete fracture network (DFN) method to create two-dimensional (2D) numerical models of crack-bearing material embedded with various types of low-velocity mechanical discontinuities, (2) use the fast marching method (FMM) to simulate the propagation of the wave/diffusion front from a single source through the 2D crack-bearing material to multiple receivers placed on the boundary of the material, and (3) train 9 data-driven classifiers to characterize the crack-bearing materials (i.e. bulk properties of the network of mechanical discontinuities in the crack-bearing material) by learning from the simulations of travel times detected by multiple receivers placed around the crack-bearing material. The classifiers identified the orientation, spatial distribution, and dispersion of the low-velocity mechanical discontinuities. Voting classifier performs the best among the 9 classifiers. For the characterization of bulk dispersion and distribution of discontinuities, the sensors located on the adjacent boundaries are more important; whereas for the characterization of bulk orientation of discontinuities, the sensors located on the opposite side are more important.

42 ENGINEERING↗
DOE OSTI2022

Using artificial intelligence to detect human errors in nuclear power plants: A case in operation and maintenance

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗