Data-Driven Predictive Modeling

Enhanced electron mobility in a Hall thruster

project personnel
Matthew Byrne, Joshua Woods, Pete Dahl, Thomas Marks, Benjamin Jorns

Principal Investigator
Benjamin Jorns

project sponsors

Current computational models of many electric propulsion (EP) devices are hampered by insufficient understanding of the underlying physics. Most prominently, current models are unable to capture the well-known problem of enhanced anomalous transport of electrons across magnetic field lines in Hall thrusters​. Recent work suggests that kinetic effects are likely to play a large role in cross-field electron transport. However, fully-kinetic models are computationally expensive, so there is an incentive to make use of comparably inexpensive fluid models, despite their inherently reduced fidelity. If sufficiently-predictive fluid models for electric propulsion devices were available, they would enable rapid design iteration and reduce the cost of developing and qualifying EP systems by decreasing the number of expensive, long-duration ground tests. They would enable institutions to experiment with novel electric propulsion concepts with increased confidence in their on-orbit performance. If ground-to-flight performance changes became reliably predictable, even to a first order, thruster lifetime, thrust, and efficiency could be better optimized for in-space performance, enabling new mission architectures. There is thus a clear need to develop predictive fluid models of electric propulsion systems.

One promising approach is to make use of the vast amounts of available data, both from ground testing and on-orbit operation, to work backwards and infer some of the critical parameters of interest. This is known as a ​data-driven approach, and it allows one to circumvent some of the restrictions of the fluid approach by folding many of the inaccessible kinetic effects into a data-driven closure term. Despite being of lower fidelity than a full kinetic simulation, it nevertheless has the potential to increase the accuracy capability of fluid simulations to the point that they may be called predictive. Data-driven models make use of empirical data to develop expressions for unknown parameters. They have been employed in turbulence modeling​ and partial differential equation solutions​, to name just a few, and they have already been promisingly applied to Hall thrusters​. This research proposes to build upon the methodology and develop a robust toolkit for predicting electric propulsion performance for a wide variety of devices in an array of operation conditions and scenarios.

Selected Publications

  • Predictive, data-driven model for the anomalous electron collision frequency in a Hall effect thruster

    Jorns, B.

    Plasma Sources Science and Technology, Vol. 27, No. 10, 10.1088/1361-6595, 2018

  • Data-Driven Scaling Laws for Electrospray Plume Divergence from a Capillary Tube

    Dahl, P.N., Kimber, A.M., and Jorns, B.

    AIAA Propulsion and Energy 2019 Forum, Indianapolis, IN, AIAA-2019-3901, August 19-22, 2019

  • Two Equation Closure Model for Plasma Turbulence in a Hall Effect Thruster

    Jorns, B.A.

    36th International Electric Propulsion Conference, Vienna, Austria, IEPC-2019-129, 2019

  • Data-driven Models for the Effects of Background Pressure on the Operation of Hall Thrusters

    Byrne, M.P., and Jorns, B.A.

    36th International Electric Propulsion Conference, Vienna, Austria, IEPC-2019-630, 2019

  • Data-Driven Approach to Modeling and Development of a 30 kW Field-reversed Configuration Thruster

    Woods, J.M., Sercel, C.L., Gill, T.M., Viges, E., and Jorns, B.A.

    36th International Electric Propulsion Conference, Vienna, Austria, IEPC-2019-717, 2019

  • Future Directions for Electric Propulsion Research

    Dale, Ethan; Jorns, Benjamin; Gallimore, Alec

    ,, August 17, 2020

  • A Predictive Hall Thruster Model Enabled by Data-Driven Closure

    Benjamin A. Jorns, Thomas A. Marks and Ethan T. Dale

    VIRTUAL,, August 17, 2020

  • Hall2De Simulations of a Magnetic Nozzle

    Thomas A. Marks , Ioannis G. Mikellides , Alejandro Lopez Ortega and Benjamin Jorns

    VIRTUAL,, August 17, 2020

  • Model for the dependence of cathode voltage in a Hall thruster on facility pressure

    Benjamin Jorns and Matthew Byrne

    University of Michigan,, 2020

  • Robust Design of Electrospray Emitters

    Alex A. Gorodetsky, Collin B. Whittaker, Audelia Szulman and Benjamin Jorns

    AIAA Propulsion and Energy 2021 Forum,, August 2021

  • Self-consistent implementation of a zero-equation transport model into a predictive model for a Hall effect thruster

    Thomas A. Marks, Alejandro Lopez Ortega, Ioannis G. Mikellides, and Benjamin A. Jorns

    AIAA Propulsion and Energy 2021 Forum,, August 2021

  • Quantifying Uncertainty in Predictions of Spacecraft Erosion Induced by a Hall Thruster

    Mackenzie E. Meyer, Matthew P. Byrne, Iain D. Boyd, and Benjamin A. Jorns

    Journal of Spacecraft and Rockets,, December 22, 2021

  • Model Inference from Electrospray Thruster Array Tests

    Collin B. Whittaker, Alex A. Gorodetsky and Benjamin A. Jorns

    AIAA SciTech 2022,, January 2022

  • Modeling anomalous electron transport in Hall thrusters using surrogate methods

    Thomas A. Marks, and Benjamin A. Jorns

    IEPC 2022,, June 2022

  • Evaluation of several first-principles closure models for Hall thruster anomalous transport

    Thomas A. Marks, and Benjamin A. Jorns

    SciTech 2023,, January 2023

  • Challenges with the self-consistent implementation of closure models for anomalous electron transport in fluid simulations of Hall thrusters

    Marks, Thomas A. Jorns, Benjamin A

    Plasma Sources Science and Technology,, April 2023

  • HallThruster.jl: a Julia package for 1D Hall thruster discharge simulation

    Marks, Thomas A. Schedler, P. Jorns, Benjamin A

    Journal of Open Source Software,, July 2023

  • Laser Measurement of Anomalous Electron Diffusion in a Crossed-Field Plasma

    Roberts, P.J. and Jorns, B.A.

    PHYSICAL REVIEW LETTERS 132, 135301,, March 2024