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ArcjetCV: Automating Arc Jet AnalysisArc 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.
Document ID
20230016068
Acquisition Source
Ames Research Center
Document Type
Presentation
Authors
Alexandre Quintart
(Analytical Mechanics Associates (United States) Hampton, Virginia, United States)
Magnus Haw
(Ames Research Center Mountain View, California, United States)
Date Acquired
November 6, 2023
Subject Category
Computer Programming and Software
Meeting Information
Meeting: 13th Ablation Workshop
Location: Mountain View, CA
Country: US
Start Date: November 7, 2023
End Date: November 9, 2023
Sponsors: University of Kentucky
Funding Number(s)
CONTRACT_GRANT: NNA15BB15C
Distribution Limits
Public
Copyright
Public Use Permitted.
Technical Review
NASA Technical Management
Keywords
Recession tracking
machine learning
arc jet
porous ablator
open source
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