Search NASA⌕ Search

SEARCH · Search NASA

Results for “ARC JET”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Influence of Catalysis and Oxidation on Slug Calorimeter Measurements in Arc Jets

Arc jet tests play a critical role in the characterization and certification of thermal protection materials and systems (TPS). The results from these arc jet tests feed directly into computational models of material response and aerothermodynamics to predict the performance of the TPS in flight. Thus the precise knowledge of the plasma environment to which the test material is subjected, is invaluable. As one of the environmental parameters, the heat flux is commonly measured. The measured heat flux is used to determine the plasma enthalpy through analytical or computational models. At NASA Ames Research Center (ARC), slug calorimeters of a geometrically similar body to the test article are routinely used to determine the heat flux. A slug calorimeter is a thermal capacitance-type calorimeter that uses the temperature rise in a thermally insulated slug to determine the heat transfer rate, see Figure 1(left). Current best practices for measuring the heat flux with a slug calorimeter are described in ASTM E457 - 96. Both the calorimeter body and slug are made of Oxygen Free High Conductivity Copper, and are cleaned before each run.

Nawaz, Anuscheh↗
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 NTRS2018

The Laser Enhanced Arc-Jet Facility (LEAF-Lite): Simulating Convective and Radiative Heating with Arc-Jets and Multiple 50-KW CW Lasers

LEAF-Lite (Laser Enhanced Arc-Jet Facility) is a radiative laser heating facility that has been added to the 60 MW Interaction Heating Facility (IHF) convective plasma arc-jet located at NASA Ames Research Center. Together, these two systems can simulate both convective and radiative heating at heat fluxes reaching 551 W/cm2 by simultaneously combining a highest measured heat flux of 160 W/cm2 convective and 391 W/cm2 radiative heating on a 152-mm x 152-mm wedge model configuration. Adding radiant heating to an existing convective facility better simulates Earth atmospheric entry from hyperbolic lunarreturn speeds. The radiative heat is provided by multiple 50-kW CW IR lasers, which is nearly uniform across the illuminated surface with a total variation less than 6%, while the convective heat is provided by a high enthalpy plasma arc-jet. In a later phase, the facility will expand to test panel test articles of 432-mm x 432-mm and provide 100 W/cm2 of radiative heating in a plasma convective flow environment. The paper describes this new combined heating capability, its current testing conditions, and the unique application of the laser system with respect to the Orion test flight lunar orbits.

Cushman, Geoff↗
NASA NTRS1996

Emission Spectral Measurements in the Plenum of an Arc Jet Wind Tunnel

Arc jet wind tunnel facilities are used to evaluate thermal protection system materials for re-entry vehicles. The high speed, high temperature flowfield generated by the arc jet can simulate the extreme aerodynamic heating environment experienced during re-entry so that the survivability of heat shield materials and performance of various designs options can be tested. Although the re-entry heating environment can be approximated in the arc jet facility, the flowfield only partially simulates the actual re-entry flight conditions. Reynolds numbers are not matched so that surface shear stress distributions and mass transfer rates due to ablation or other mechanisms are not modeled correctly. Unlike flight conditions the arc freestream air is in non-equilibrium because of the rapid expansion that occurs in the supersonic nozzle. To properly study the actual re-entry flow environment, computational fluid dynamics, computational chemistry and radiation models must be used. Arc jet tunnel tests serve to validate these models. To perform accurate simulations inlet and boundary-conditions are needed, which come from measurements of the flowfield. The present study is concerned with measurements in the plenum region of an arc heater. In the past, conditions in the arc heater flowfield have been predicted using simulations since conventional measurement techniques could not be used in the harsh extremely high temperature environment. The present study is part of a recent push to utilize optical techniques to help better characterize the arc jet flowfields. Emission measurements have been made in the shock layer and the constrictor section of the arc heater to determine temperatures and species number densities. LIF measurements have been made in the free stream to determine temperature and velocity.

Donohue, Jim↗
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: 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 NTRS2011

Bulk Enthalpy Calculations in the Arc Jet Facility at NASA ARC

The Arc Jet Facilities at NASA Ames Research Center generate test streams with enthalpies ranging from 5 MJ/kg to 25 MJ/kg. The present work describes a rigorous method, based on equilibrium thermodynamics, for calculating the bulk enthalpy of the flow produced in two of these facilities. The motivation for this work is to determine a dimensionally-correct formula for calculating the bulk enthalpy that is at least as accurate as the conventional formulas that are currently used. Unlike previous methods, the new method accounts for the amount of argon that is present in the flow. Comparisons are made with bulk enthalpies computed from an energy balance method. An analysis of primary facility operating parameters and their associated uncertainties is presented in order to further validate the enthalpy calculations reported herein.

Thompson, Corinna S.↗
NASA NTRS1986

Simulating AOTV heating environment in an arc jet

The application of arc-jet facilities to the simulation of the combined radiative and convective heating environments calculated for AOTV return missions is presented. The AOTV mission requirement and design configuration determine the magnitude of the radiative and convective heating rates. This paper considers methods for obtaining radiation from the high temperature plasma in arc-jet flows. A computer code that predicts arc-heater performance was used to determine the effect of gas mixtures on arc-heater performance, with the intent of increasing the gas temperature and resultant radiative emission. Numerical radiation solutions were compared with direct measurements of the radiative intensity from the gas cap. Convective heating measurements were made as well. The arc-jet was operated at AOTV enthalpy and pressure levels for these measurements to assess the simulation capabilities for AOTV material development/testing. A parametric study is presented that defines the capability of the Interaction Heating Facility arc jet at Ames to simulate both the radiative and the convective heating for current AOTV configurations.

Balboni, J.↗
NASA NTRS2021

NASA Ames Arc Jet Complex Measurements Handbook

The Arc Jet Measurement System Handbook will serve as a reference of the calculations and formulas used by the Arc Jet Complex Data Acquisition System. It is the objective that Code ASF personnel and experimenters will be able to manually reproduce data calculations performed by the Arc Jet Data Acquisition System using these formulas as a guide.

Jerry Cheng↗
NASA NTRS2009

Boundary Layer Transition Protuberance Tests at NASA JSC Arc-Jet Facility

A series of arc-jet tests in support of the Shuttle Orbiter Boundary Layer Transition flight experiment was conducted in the Channel Nozzle of the NASA Johnson Space Center Atmospheric Reentry Materials and Structures Facility. The boundary layer trip was a protrusion of a certain height and geometry fabricated as part of a 6"x6" tile insert, a special test article made of the Boeing Rigid Insulation tile material and coated with the Reaction Cured Glass used for the bottom fuselage tiles of the Space Shuttle Orbiter. A total of five such tile inserts were manufactured: four with the 0.25-in. trip height, and one with the 0.35-in. trip height. The tile inserts were interchangeably installed in the center of the 24"x24" variable configuration tile array mounted in the 24"x24" test section of the channel nozzle. The objectives of the test series were to demonstrate that the boundary layer trip can safely withstand the Space Shuttle Orbiter flight-like re-entry environments and provide temperature data on the protrusion surface, surfaces of the nearby tiles upstream and downstream of the trip, as well as the bond line between the tiles and the structure. The targeted test environments were defined for the tip of the protrusion, away from the nominal surface of the tile array. The arc jet test conditions were approximated in order to produce the levels of the free stream total enthalpy at the protrusion height similar to those expected in flight. The test articles were instrumented with surface, sidewall and bond line thermocouples. Additionally, Tempilaq temperature-indicating paint was applied to the nominal tiles of the tile array in locations not interfering with the protrusion trip. Five different grades of paint were used that disintegrate at different temperatures between 1500 and 2000 deg F. The intent of using the paint was to gauge the RCG-coated tile surface temperature, as well as determine its usefulness for a flight experiment. This paper provides an overview of the channel nozzle arc jet, test articles and test conditions, as well as the results of the arc-jet tests including the measured temperature response of the test articles, their pre- and post-test surface scans, condition of the thermal paint, and continents on the protrusion tip heating achieved in tests compared to the computational fluid dynamics predictions.

Larin, M. E.↗
NASA NTRS2021

Emission Spectroscopy Characterization of Electrodes Species in the Freestream Flow at the NASA Ames Miniature Arc Jet II Facility

The second generation miniature Arc Jet (mARC II) is a subscale arc jet at NASA Ames Research Center that is undergoing initial characterization. During its operation, the electrodes used to produce continuous plasma flows deteriorate and the flow purity decreases. The present work reports the use of optical emission spectroscopy as a characterization tool for impurities in the plasma flow. The discussion is focused on the interpretation of spectroscopic measurements and their evolution under different testing conditions. Special emphasis is given to copper and silver, the main atomic elements of the electrodes. The highest rate of anode deterioration is found to occur during the arc jet ignition, while cathode deterioration is constant throughout the entire run. Based on the experimental spectra, a preliminary numerical characterization of the plasma temperature, enthalpy and number density is conducted.

facility↗
NASA NTRS2014

Time-varying Entry Heating Profile Replication with a Rotating Arc Jet Test Article

A new approach for arc jet testing of thermal protection materials at conditions approximating the time-varying conditions of atmospheric entry was developed and demonstrated. The approach relies upon the spatial variation of heat flux and pressure over a cylindrical test model. By slowly rotating a cylindrical arc jet test model during exposure to an arc jet stream, each point on the test model will experience constantly changing applied heat flux. The predicted temporal profile of heat flux at a point on a vehicle can be replicated by rotating the cylinder at a prescribed speed and direction. An electromechanical test model mechanism was designed, built, and operated during an arc jet test to demonstrate the technique.

Thermal protection materials↗
NASA NTRS2015

Arc Jet Facility Test Condition Predictions Using the ADSI Code

The Aerothermal Design Space Interpolation (ADSI) tool is used to interpolate databases of previously computed computational fluid dynamic solutions for test articles in a NASA Ames arc jet facility. The arc jet databases are generated using an Navier-Stokes flow solver using previously determined best practices. The arc jet mass flow rates and arc currents used to discretize the database are chosen to span the operating conditions possible in the arc jet, and are based on previous arc jet experimental conditions where possible. The ADSI code is a database interpolation, manipulation, and examination tool that can be used to estimate the stagnation point pressure and heating rate for user-specified values of arc jet mass flow rate and arc current. The interpolation is performed in the other direction (predicting mass flow and current to achieve a desired stagnation point pressure and heating rate). ADSI is also used to generate 2-D response surfaces of stagnation point pressure and heating rate as a function of mass flow rate and arc current (or vice versa). Arc jet test data is used to assess the predictive capability of the ADSI code.

fluid dynamics↗