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Machine Learning Analysis of Hall Thruster Facility Effects Data – Penn State Behrend

INSTITUTION

Penn State Behrend

CLASS

Cobalt Class (2019 – 2020)

STUDENT TEAM

Alec Dady
Daniel Carter Donley
James (Jimmy) Patrick Fennelly Jr.

SCIENTIFIC & TECHNICAL GUIDANCE

Dr. Jason Frieman, NASA Glenn Research Center

ACADEMIC GUIDANCE

Dr. Dean Lewis, Assistant Teaching Professor of Mechanical Engineering, Penn State Behrend

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.