Multifidelity reduced-order modeling of particle-in-cell simulations for anomalous transport closure models

Hall thrusters are a prominent electric propulsion device which achieve much higher efficiency and specific impulse than conventional rockets by ejecting ions from a stationary plasma at high velocities to produce thrust. NASA has long recognized the advantage of Hall thrusters as a candidate for long duration in-space missions with advancements in magnetic shielding, the development of fluid simulation codes, the long history of work on fluid closure models, and the development of the propulsion systems for the forthcoming Artemis missions. To date, modeling and simulation of Hall thrusters has been limited by complex physics in the discharge plasma, namely the "anomalous" transport of electrons across magnetic field lines. Anomalous transport arises primarily from kinetic instabilities in the plasma, yet while computationally expensive kinetic simulations are able to resolve these dynamics, the integration of these kinetic effects into a practical fluid model have been with limited success. To this end, the key objective of this proposal is to reduce the intractable computational requirements of fully kinetic particle-in-cell (PIC) plasma simulations by orders of magnitude to enable a new generation of anomalous transport closure for fluid models. This work will use novel methods in the field of reduced- order models (ROMs) to approximate the solution space of PIC simulations with significantly lower computational requirements and tight error bounds on the quantities of interest. The construction of a ROM is enabled by recent advances in high performance computing and deep learning, which will be used to generate a large dataset of PIC simulations at multiple modeling fidelities. This work will then combine kinetic information from the ROM with experimental measurements into an existing Hall thruster code. Finally, the work will use a feedback cycle enabled by optimal experimental design for model improvement.

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