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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 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 NTRSDate not supplied

arcjetCV: automating recession extraction from video

Arc jet Computer Vision (arcjetCV)[1][2] is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV 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.

Recession tracking↗
NASA NTRSDate not supplied

ArcjetCV: Automating Recession Tracking

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV 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.

Recession tracking↗
NASA NTRSDate not supplied

ArcjetCV: Automating Arc Jet Analysis

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV 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.

Recession tracking↗
NASA NTRS1963

Effects of Mass Addition on the Stability of Slender Cones at Hypersonic Speeds

The effect of mass addition on the flow over bodies moving at hypersonic speeds has been studied by several investigators (e.g., Cresci and Libby). In most of this work, primary attention logically has been directed toward the effects of foreign-gas injection on heat transfer and pressure distributions, and, principally for this reason, most of the work ha been done at zero angle of attack. The foreign gas can be provided either by some active injection system or by the action of an ablation heat shield. With increasing rates of injection, the basic flow about the body can be affected significantly. One such effect was observed in the paper by Cresci and Libby, where it was shown that the shockwave standoff distance can be increased by gas injection at the nose of a body.

SLENDER BODY↗
NASA NTRS1964

Experimental Data on Stagnation-Point Gas Injection Cooling on a Hemisphere-Cone in a Hypersonic Arc Tunnel

Exploratory tests were made to determine the reduction of heat transfer resulting from ejection of gases at the stagnation point on a hemisphere-cone at a nominal Mach number of about 9 and at stagnation enthalpies up to 1600 BTU per pound. Helium, nitrogen, and argon gases were used as coolants. The ratio of mass flow of coolant to the mass flow of air swept out by the model projected area was less than 0.20 in all cases. The experimental heat-transfer data were normalized by the calculated stagnation-point values based on test conditions in the tunnel and are presented as a function of the surface distance from the stagnation point. The variation of shock standoff distances with mass flow and volumetric flow of the gaseous ejectants is presented along with photographs of the luminous flow field of the model during tests.

STAGNATION FLOW↗
NASA NTRS1966

Bypass rod transfers heat developed in thermionic diode

In a thermionic diode, a cesium tube joining the emitter-collector area and the cesium reservoir is fitted with a copper bypass rod held in place by two standoff brackets. The rod transfers heat from the emitter-collector to the reservoir without going through the ceramic seal structure which surrounds the cesium tube and cannot sustain large temperature gradients.

Lazaridis, L. J.↗
NASA NTRS1967

Magnetogasdynamic re-entry phenomena.

MGD reentry flow regimes analyzed, noting increased bow shock standoff distance and drag, comparing in-flight and laboratory simulation

MAGNETOGASDYNAMICS↗
NASA NTRS1968

Vacuum-jacketed transfer line installation technique

Rolling-type spacers in the form of steel balls retained in appropriate sleeves affixed at intervals to the exterior of the transfer line facilitate the installation of a vacuum-jacketed line. They act as standoffs to position the transfer line concentrically within the vacuum jacket line.

Bowers, W. M.↗
NASA NTRS1964

Injection and distribution of liquids in the flow fields of blunt shapes at hypersonic speeds

A possible method for reducing the attenuation of radio transmission from hypersonic vehicles is by injection of a liquid into the flow field. The hot plasma layer surrounding the vehicle could presumably be quenched if the latent heat of the liquid and the heat capacity of the vapor could be utilized efficiently. In the case of stagnation-point injection, the momentum of the liquid could be utilized to modify the bow shock and possibly reduce the initial ionization. To attain these objectives of efficient coolant utilization or modification of the bow shock a knowledge of the liquid injection, atomization, spray penetration, and evaporation processes is required. This report presents the results of a study of liquid injection into hypersonic flow fields. A simplified theory for evaporation distances is derived and is shown to be in qualitative agreement with experimental data. Correlation of experimental data for maximum cross-current penetration of the liquid spray for injection near the shoulder pf a hemisphere cylinder, a sphere 9° cone, and a Mercury model are given. These data were obtained in the Langley Mach 8 variable density tunnel with both liquid nitrogen and water injection to provide a large range of liquid parameters. Flow-field disturbances, as indicated by schlieren photographs and surface pressures, increased with the ratio of coolant mass flux to airstream mass flux. For stagnation-point injection, the maximum shock standoff distance was correlated by the ratio of the liquid jet momentum flux to the airstream momentum flux.

Ivan E. Beckwith↗