Machine Learning Analysis of Hall Thruster Facility Effects Data – Team Ion
INSTITUTION
Arizona State University (Tempe Campus)
CLASS
Cobalt Class (2019 – 2020)
STUDENT TEAM
Anthony Benites, Computer Science (Cybersecurity)
Seth Brunwasser, Computer Science
Artem Kerutis, Computer Science
John Macken, 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
Dr. Helen Chavez, Lecturer, 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.