Data-driven sparse modeling of oscillations in plasma space propulsion

An algorithm to obtain data-driven models of oscillatory phenomena in plasma space propulsion systems is presented, based on sparse regression (SINDy) and Pareto front analysis. The algorithm can incorporate physical constraints, use data bootstrapping for additional robustness, and fine-tuning to different metrics. Standard, weak and integral SINDy formulations are discussed and compared. The scheme is benchmarked for the case of breathing-mode oscillations in Hall effect thrusters, using particle-in-cell/fluid simulation data. Models of varying complexity are obtained for the average plasma properties, and shown to have a clear physical interpretability and agreement with existing 0D models in the literature. Lastly, the algorithm applied is also shown to enable the identification of physical subdomains with qualitatively different plasma dynamics, providing valuable information for more advanced modeling approaches.

hall effect thrusters; sparse regression; pareto front analysis; equation discovery; dominant balance physics; constraints; data-driven modeling

Data-driven sparse modeling of oscillations in plasma space propulsion Articles