Prognostics of Hall Thruster Erosion Using Multiphysics-Based Modeling and Machine Learning

Hall thrusters, a type of propulsion device with high specific impulses, have gathered substantial interest within the aerospace community. However, the challenge of thruster wall erosion induced by sputtering impedes their stable and reliable function. This paper proposes a novel prognostic framework for estimating Hall thruster channel wall erosion based on multiphysics simulation and machine learning. First, a one-dimensional (1D) plasma discharge code is introduced to simulate plasma dynamics within discharge channel. Then, the erosion rate is quantified based on a semi-empirical sputter yield model. In addition, an erosion profile estimation loop is proposed to accommodate the 1D simulation while leveraging 2D erosion profiles. Finally, a machine-learning polynomial regression model serves as a surrogate model, facilitating efficient erosion rate estimations without extensive computations. Results and comparisons demonstrate that the proposed low-fidelity prognostic framework reliably reflects the erosion trends observed in high-fidelity models and experimental data, while reducing both simulation and testing requirements.