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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Analysis of Wallops Flight Test Data Through an Automated COTS System

During the summer of 2004 NASA Langley Research Center flight tested a Synthetic Vision System (SVS) at the Reno/Tahoe International Airport (RNO) and the Wallops Flight Facility (WAL). The SVS included a Runway Incursion Prevention System (RIPS) to improve pilot situational awareness while operating near and on the airport surface. The flight tests consisted of air and ground operations to evaluate and validate the performance of the system. This paper describes the flight test and emphasizes how positioning data was collected, post processed and analyzed through the use of a COTS-derived software system. The system that was developed to analyze the data was constructed within the MATLAB(TM) environment. The software was modified to read the data, perform several if-then scenarios and produce the relevant graphs, figures and tables.

Blackstock, Dexter Lee↗
NASA NTRSDate not supplied

ArcjetCV: a new machine learning application for extracting time-resolved recession measurements from arc jet test videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗
NASA NTRSDate not supplied

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗
NASA NTRSDate not supplied

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗
NASA NTRSDate not supplied

arcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗
NASA NTRS1992

Comparison of central axis and jet ring coolant supply for turbine disk cooling on a SSME-HPOTP model

The test facility, test methods and results are presented for an experimental study modeling the cooling of turbine disks in the blade attachment regions with multiple impinging jets, in a configuration simulating the disk cooling method employed on the Space Shuttle Main Engine oxygen turbopump. The study's objective was to provide a comparison of detailed local convection heat transfer rates obtained for a single center-supply of disk coolant with those obtained with the present flight configuration where disk coolant is supplied through an array of 19 jets located near the disk outer radius. Specially constructed disk models were used in a program designed to evaluate possible benefits and identify any possible detrimental effects involved in employing an alternate disk cooling scheme. The study involved the design, construction and testing of two full scale rotating model disks, one plane and smooth for baseline testing and the second contoured to the present flight configuration, together with the corresponding plane and contoured stator disks. Local heat transfer rates are determined from the color display of encapsulated liquid crystals coated on the disk in conjunction with use of a computer vision system. The test program was composed of a wide variety of disk speeds, flowrates, and geometrical configurations, including testing for the effects of disk boltheads and gas ingestion from the gas path region radially outboard of the disk-cavity.

Kim, Y. W.↗
NASA NTRS2017

Development of a High-Fidelity Simulation Environment for Shadow-Mode Assessments of Air Traffic Concepts

This paper describes the Shadow-Mode Assessment Using Realistic Technologies for the National Airspace System (SMART-NAS) Test Bed. The SMART-NAS Test Bed is an air traffic simulation platform being developed by the National Aeronautics and Space Administration (NASA). The SMART-NAS Test Bed's core purpose is to conduct high-fidelity, real-time, human-in-the-loop and automation-in-the-loop simulations of current and proposed future air traffic concepts for the United States' Next Generation Air Transportation System called NextGen. The setup, configuration, coordination, and execution of realtime, human-in-the-loop air traffic management simulations are complex, tedious, time intensive, and expensive. The SMART-NAS Test Bed framework is an alternative to the current approach and will provide services throughout the simulation workflow pipeline to help alleviate these shortcomings. The principle concepts to be simulated include advanced gate-to-gate, trajectory-based operations, widespread integration of novel aircraft such as unmanned vehicles, and real-time safety assurance technologies to enable autonomous operations. To make this possible, SNTB will utilize Web-based technologies, cloud resources, and real-time, scalable, communication middleware. This paper describes the SMART-NAS Test Bed's vision, purpose, its concept of use, and the potential benefits, key capabilities, high-level requirements, architecture, software design, and usage.

human-in-the-loop↗
NASA NTRS2007

Stroboscopic Vision as a Treatment for Retinal Slip Induced Motion Sickness

Motion sickness in the general population is a significant problem driven by the increasingly more sophisticated modes of transportation, visual displays, and virtual reality environments. It is important to investigate non-pharmacological alternatives for the prevention of motion sickness for individuals who cannot tolerate the available anti-motion sickness drugs, or who are precluded from medication because of different operational environments. Based on the initial work of Melvill Jones, in which post hoc results indicated that motion sickness symptoms were prevented during visual reversal testing when stroboscopic vision was used to prevent retinal slip, we have evaluated stroboscopic vision as a method of preventing motion sickness in a number of different environments. Specifically, we have undertaken a five part study that was designed to investigate the effect of stroboscopic vision (either with a strobe light or LCD shutter glasses) on motion sickness while: (1) using visual field reversal, (2) reading while riding in a car (with or without external vision present), (3) making large pitch head movements during parabolic flight, (4) during exposure to rough seas in a small boat, and (5) seated and reading in the cabin area of a UH60 Black Hawk Helicopter during 20 min of provocative flight patterns.

Reschke, M. F.↗
NASA NTRS1993

Research and test facilities

A description is given of each of the following Langley research and test facilities: 0.3-Meter Transonic Cryogenic Tunnel, 7-by 10-Foot High Speed Tunnel, 8-Foot Transonic Pressure Tunnel, 13-Inch Magnetic Suspension & Balance System, 14-by 22-Foot Subsonic Tunnel, 16-Foot Transonic Tunnel, 16-by 24-Inch Water Tunnel, 20-Foot Vertical Spin Tunnel, 30-by 60-Foot Wind Tunnel, Advanced Civil Transport Simulator (ACTS), Advanced Technology Research Laboratory, Aerospace Controls Research Laboratory (ACRL), Aerothermal Loads Complex, Aircraft Landing Dynamics Facility (ALDF), Avionics Integration Research Laboratory, Basic Aerodynamics Research Tunnel (BART), Compact Range Test Facility, Differential Maneuvering Simulator (DMS), Enhanced/Synthetic Vision & Spatial Displays Laboratory, Experimental Test Range (ETR) Flight Research Facility, General Aviation Simulator (GAS), High Intensity Radiated Fields Facility, Human Engineering Methods Laboratory, Hypersonic Facilities Complex, Impact Dynamics Research Facility, Jet Noise Laboratory & Anechoic Jet Facility, Light Alloy Laboratory, Low Frequency Antenna Test Facility, Low Turbulence Pressure Tunnel, Mechanics of Metals Laboratory, National Transonic Facility (NTF), NDE Research Laboratory, Polymers & Composites Laboratory, Pyrotechnic Test Facility, Quiet Flow Facility, Robotics Facilities, Scientific Visualization System, Scramjet Test Complex, Space Materials Research Laboratory, Space Simulation & Environmental Test Complex, Structural Dynamics Research Laboratory, Structural Dynamics Test Beds, Structures & Materials Research Laboratory, Supersonic Low Disturbance Pilot Tunnel, Thermal Acoustic Fatigue Apparatus (TAFA), Transonic Dynamics Tunnel (TDT), Transport Systems Research Vehicle, Unitary Plan Wind Tunnel, and the Visual Motion Simulator (VMS).

Source record↗
NASA NTRS2006

Real-time Enhancement, Registration, and Fusion for a Multi-Sensor Enhanced Vision System

Over the last few years NASA Langley Research Center (LaRC) has been developing an Enhanced Vision System (EVS) to aid pilots while flying in poor visibility conditions. The EVS captures imagery using two infrared video cameras. The cameras are placed in an enclosure that is mounted and flown forward-looking underneath the NASA LaRC ARIES 757 aircraft. The data streams from the cameras are processed in real-time and displayed on monitors on-board the aircraft. With proper processing the camera system can provide better-than- human-observed imagery particularly during poor visibility conditions. However, to obtain this goal requires several different stages of processing including enhancement, registration, and fusion, and specialized processing hardware for real-time performance. We are using a real-time implementation of the Retinex algorithm for image enhancement, affine transformations for registration, and weighted sums to perform fusion. All of the algorithms are executed on a single TI DM642 digital signal processor (DSP) clocked at 720 MHz. The image processing components were added to the EVS system, tested, and demonstrated during flight tests in August and September of 2005. In this paper we briefly discuss the EVS image processing hardware and algorithms. We then discuss implementation issues and show examples of the results obtained during flight tests. Keywords: enhanced vision system, image enhancement, retinex, digital signal processing, sensor fusion

Hines, Glenn D.↗
NASA NTRS2014

Vision Algorithm for the Solar Aspect System of the HEROES Mission

This work covers the design and test of a machine vision algorithm for generating high-accuracy pitch and yaw pointing solutions relative to the sun for the High Energy Replicated Optics to Explore the Sun (HEROES) mission. It describes how images were constructed by focusing an image of the sun onto a plate printed with a pattern of small fiducial markers. Images of this plate were processed in real time to determine relative position of the balloon payload to the sun. The algorithm is broken into four problems: circle detection, fiducial detection, fiducial identification, and image registration. Circle detection is handled by an "Average Intersection" method, fiducial detection by a matched filter approach, identification with an ad-hoc method based on the spacing between fiducials, and image registration with a simple least squares fit. Performance is verified on a combination of artificially generated images, test data recorded on the ground, and images from the 2013 flight

Solar Aspect↗
NASA NTRS2014

Vision Algorithm for the Solar Aspect System of the HEROES Mission

This work covers the design and test of a machine vision algorithm for generating high-accuracy pitch and yaw pointing solutions relative to the sun for the High Energy Replicated Optics to Explore the Sun (HEROES) mission. It describes how images were constructed by focusing an image of the sun onto a plate printed with a pattern of small fiducial markers. Images of this plate were processed in real time to determine relative position of the balloon payload to the sun. The algorithm is broken into four problems: circle detection, fiducial detection, fiducial identification, and image registration. Circle detection is handled by an Average Intersection method, fiducial detection by a matched filter approach, identification with an ad-hoc method based on the spacing between fiducials, and image registration with a simple least squares fit. Performance is verified on a combination of artificially generated images, test data recorded on the ground, and images from the 2013 flight.

HEROES↗
NASA NTRS2003

Initial SVS Integrated Technology Evaluation Flight Test Requirements and Hardware Architecture

This document presents the flight test requirements for the Initial Synthetic Vision Systems Integrated Technology Evaluation flight Test to be flown aboard NASA Langley's ARIES aircraft and the final hardware architecture implemented to meet these requirements. Part I of this document contains the hardware, software, simulator, and flight operations requirements for this light test as they were defined in August 2002. The contents of this section are the actual requirements document that was signed for this flight test. Part II of this document contains information pertaining to the hardware architecture that was realized to meet these requirements as presented to and approved by a Critical Design Review Panel prior to installation on the B-757 Airborne Research Integrated Experiments Systems (ARIES) airplane. This information includes a description of the equipment, block diagrams of the architecture, layouts of the workstations, and pictures of the actual installations.

Harrison, Stella V.↗
NASA NTRS2000

Visual detection of spatial contrast patterns: evaluation of five simple models

The ModelFest Phase One dataset is a collection of luminance contrast thresholds for 43 two-dimensional monochromatic spatial patterns confined to an area of approximately two by two degrees. These data were collected by a collaboration among twelve laboratories, and were designed to provide a common database for calibration and testing of spatial vision models. Here I report fits of the ModelFest data with five models: Peak Contrast, Contrast Energy, Generalized Energy, a Gabor Channels model, and a Discrete Cosine Transform model. The Gabor Channels model provides the best fit, though the other, simpler models, with the exception of Peak Contrast, provide remarkably good fits as well. Though there are clear individual differences, regularities in the data suggest the possibility of constructing a standard observer for spatial vision. c2000 Optical Society of America.

NASA Discipline Space Human Factors↗
NASA NTRS2016

Comparison of Structural and Functional Ocular Outcomes Between 14- and 70 Day Bed Rest

Purpose: To compare structural and functional ocular outcomes in healthy human subjects undergoing 14- and/or 70-day head-down-tilt bed rest (HDTBR). We hypothesized the amount of HDTBR-induced ocular changes be affected by the HDTBR duration. Methods: The studies were conducted at the NASA Flight Analogs Research Unit, The University of Texas Medical Branch at Galveston, Galveston, TX. Participants were selected using NASA standard screening procedures. Standardized NASA screening procedures and bed rest conditions (e.g., strict sleep-wake cycle, standardized diet, continuous video monitoring) were implemented in both studies. Participants maintained a 6deg HDTBR position for 14 and/or 70 consecutive days and did not engage in exercise. Weekly ophthalmological examinations were conducted in the sitting (pre/post-bed rest only) and HDT positions. Ocular outcomes of interest included: near best-corrected visual acuity (BCVA); spherical equivalent, as determined by cycloplegic autorefraction; Goldmann applanation tonometry and iCare (Icare Finland Oy, Espoo, Finland) intraocular pressure (IOP) measurement; color vision; red dot test; modified Amsler grid test; confrontational visual field; stereoscopic color fundus photography; Spectralis OCT (Heidelberg Engineering, GmbH, Heidelberg, Germany) retinal nerve fiber layer thickness (RNFLT), peripapillary and macular retinal thicknesses. Mixed-effects linear models were used to compare pre- and post-HDTBR observations between 14- and 70-day HDTBR for our continuously scaled outcomes.

Cromwell, R. L.↗
NASA NTRS2014

Development and Testing of the Smart SPHERES Telerobotic Free-Flyer

Smart SPHERES is a free-flying robot that can be remotely operated by astronauts in space, or by mission controllers on the ground. We are developing Smart SPHERES to perform a variety of intravehicular activities (IVA) inside the International Space Station (ISS). These IVA tasks include interior environmental surveys, inventory, and mobile camera work. During 2013, we tested crew-controlled remote operations and wireless network-based localization with Smart SPHERES on ISS. In 2014, we plan to test 3D vision-based navigation.

Fong, Terrence↗
NASA NTRS1986

Seam tracking performance of a Coaxial Weld Vision System and pulsed welding

This report describes a continuation of a series of tests on the Coaxial Weld Vision System at MSFC. The ability of the system to compensate for transients associated with pulsed current welding is analyzed. Using the standard image processing approach for root pass seam tracking, the system is also tested for the ability to track the toe of a previous weld bead, for tracking multiple pass weld joints. This Coaxial Weld Vision System was developed by the Ohio State University (OSU) Center for Welding Research and is a part of the Space Shuttle Main Engine Robotic Welding Development System at MSFC.

Gangl, K. J.↗