Uncertainty quantification of a multi-component Hall thruster model at varying facility pressures

Bayesian inference is applied to calibrate and quantify prediction uncertainty in a coupled multi-component Hall thruster model. The model consists of cathode, discharge, and plume submodels and outputs thruster performance metrics, one-dimensional plasma properties, and the angular distribution of the current density in the plume. The simulated thrusters include a magnetically shielded thruster operating on krypton, the H9, and an unshielded thruster operating on xenon, the SPT-100, at pressures between 4.3−43μTorr-Kr and 1.7−80μTorr-Xe, respectively. After calibration, the model captures key pressure-related trends, including changes in thrust and upstream shifts in the ion acceleration region. Furthermore, the model exhibits predictive accuracy to within 10% when evaluated on flow rates and pressures not included in the training data and can predict some performance characteristics across test facilities to within the same range. Compared to a previous model calibrated on some of the same data [Eckels et al., J. Electric Propul. 3, 19 (2024)], the model reduced predictive errors in thrust and discharge current by greater than 50%. An extrapolation to on-orbit performance is performed with an error of 9%, capturing trends in discharge current but not thrust. These findings are discussed in the context of using data for predictive Hall thruster modeling in the presence of facility effects.