Hybrid DSMC and PIC-MCC Method with Spatially Varying Pressure for Electric Propulsion Ground Testing

Ground testing of electric propulsion (EP) thrusters is influenced by elevated chamber pressures that increase charge–exchange (CEX) and momentum–exchange (MEX) collisions, altering plume divergence and neutralization behavior. Fully kinetic Particle-in-Cell with Direct Simulation Monte Carlo (PIC–DSMC) simulations capture these effects with high fidelity but require substantial computational resources. More efficient Particle-in-Cell with Monte Carlo Collisions (PIC–MCC) methods require a prescribed neutral background, but assuming a spatially uniform value based on facility pressure gauges is problematic because such measurements do not resolve the localized pressure gradients near the thruster exit, limiting the accuracy of the model. This work evaluates a hybrid DSMC–PIC–MCC approach in which spatially varying neutral density and temperature fields from a neutral-only DSMC simulation are incorporated into a PIC–MCC solver. Three models are compared: (A) fully kinetic PIC–DSMC, (B) PIC–MCC with a uniform neutral background, and (C) PIC–MCC with DSMC-informed spatially varying neutrals. Under low-pressure conditions, the neutral-only DSMC field does not match the PIC–DSMC neutral distribution because it lacks wall-accommodated neutralized ions, but all three methods generate similar ion-beam properties due to minimal CEX activity. Under high-pressure conditions, DSMC accurately captures the spatial variation of neutral number density, as neutrals dominate over ions, but yields a nearly uniform temperature, unlike the elevated temperatures produced in PIC–DSMC by fast CEX neutrals. Despite this temperature discrepancy, Method~C reflects the major plume features governed by neutral-density gradients and provides better agreement with PIC–DSMC than the uniform-background model, especially in predicting CEX production and beam-wing broadening. Moreover, the hybrid method reduces computation time by roughly 90\% relative to PIC–DSMC and requires far less memory. These results indicate that DSMC-informed PIC–MCC offers a practical and computationally efficient alternative for modeling EP plumes in high-pressure ground-test environments where neutral-density variations strongly influence plume behavior.