Time-Resolved Data-Driven Surrogates of Hall-effect Thrusters

TL;DR

This paper explores a Reservoir Computing framework to develop data-driven surrogate models for Hall-effect thrusters, enabling accurate prediction and inference of their complex, high-frequency oscillatory behavior from experimental time-series data.

Contribution

It introduces a novel application of Reservoir Computing to model and predict Hall-effect thruster dynamics using high-speed experimental data.

Findings

Surrogate models accurately predict thruster behavior.

Models can infer missing measurements from available data.

Framework captures high-frequency oscillations effectively.

Abstract

The treatment of Hall-effect thrusters as nonlinear, dynamical systems has emerged as a new perspective to understand and analyze data acquired from the thrusters. The acquisition of high-speed data that can resolve the characteristic high-frequency oscillations of these thruster enables additional levels of classification in these thrusters. Notably, these signals may serve as unique indicators for the full state of the system that can aid digital representations of thrusters and predictions of thruster dynamics. In this work, a Reservoir Computing framework is explored to build surrogate models from experimental time-series measurements of a Hall-effect thruster. Such a framework has shown immense promise for predicting the behavior of low-dimensional yet chaotic dynamical systems. In particular, the surrogates created by the Reservoir Computing framework are capable of both predicting the observed behavior of the thruster and estimating the values of certain measurements from others, known as inference.