Data-driven Framework to Characterize Crater Dynamics During Plume-Surface Interactions

During propulsive landings on the Moon, Mars, or other terrestrial bodies, high-speed thruster plumes erode the regolith surface, generating ejecta that lift off and pose hazards to the vehicle and surroundings. The volume and shape of craters formed during plume-surface interactions (PSI) depend on thruster characteristics, ambient conditions, and regolith properties. We review existing analytical, empirical, and data-driven models of plume-induced granular erosion, highlighting the need for models that accurately capture PSI across a broader range of conditions. In this work, low-pressure, high-speed data from NASA’s Physics-Focused Ground Test (PFGT) campaign were used to characterize crater evolution and morphology across varying nozzle mass flow rates, nozzle heights, background pressures, and regolith simulants. Half-space experiments employed a splitter plate aligned with the nozzle axis, enabling high-speed recordings of crater formation. Crater profiles were extracted using an in-house image processing tool, augmented with an open-source machine learning algorithm. Sensitivity analysis of crater dynamics was conducted to classify cratering behavior based on nozzle, ambient, and particle properties. An intuitive regime map for crater shapes is presented, using nozzle pressure ratio, nozzle height, and a non-dimensional erosion parameter, revealing five distinct cratering regimes. Finally, volumetric erosion rates are compared with existing physics-based models, and a correction is proposed that accounts for nozzle height and reduced ambient pressure.