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Machine Learning Analysis of Hall Thruster Facility Effects Data – Team Xenon

INSTITUTION

Arizona State University (Tempe Campus)

CLASS

Cobalt Class (2019 – 2020)

STUDENT TEAM

David Gibb, Engineering Management
Trent Hall, Computer Science (Cybersecurity)
Luis Montano, Computer Science
Kevin Patterson, Computer Science
Jude Abishek, Computer Science
Garrett Tang, Computer Science

SCIENTIFIC & TECHNICAL GUIDANCE

Dr. Jason Frieman, NASA Glenn Research Center

ACADEMIC GUIDANCE

Dr. Ming Zhao, Associate Professor, School of Computing, Informatics, and Decision Systems Engineering, ASU

PROJECT DESCRIPTION

Terrestrial space simulation facilities cannot perfectly replicate the pressures observed in space. Since Hall thrusters are sensitive to the ambient pressure at which they are operated, this can lead to differences in performance between ground test and orbital operation. Despite extensive research, current physics-based models have not yet uncovered the cause of this sensitivity. Student teams are applying machine learning and/or data mining techniques to uncover previously undiscovered correlations in published data sets and devising methods to predict and/or correct for this sensitivity. The end result will include a report detailing the results of project including, but not limited to, training data, any models/analyses generated by the machine learner, and an evaluation of the model accuracy and predictive capability.