Construction of Sampling Disturbance Model of Lunar Surface

This study establishes a dynamic evolution model of the physical and mechanical properties of lunar simulant as a function of sampling-induced disturbance on the lunar surface, aiming to eliminate design errors in sampling missions caused by neglecting the disturbance of lunar soil. A standard probe was inserted into the lunar soil simulant both before and after disturbance, and the variation in penetration resistance at the exact location was proposed as an indicator of the regolith’s disturbance state. Compression tests and disturbance tests were conducted on CUG-1A lunar soil simulant, with the experimental results subjected to regression analysis and neural network prediction. Based on the compression tests, a regression equation was derived relating the slope of the probe penetration resistance to the internal friction angle and density of the lunar soil simulant, showing a strong correlation between predicted and actual values. The disturbance tests provided penetration resistance curves under various disturbance conditions. By integrating these two components, a correspondence was established between the disturbance conditions and the internal friction angle and density of the lunar soil simulant. The predictive performance of three typical neural network algorithms—LM, BR, and SCG—with varying numbers of neurons was compared. The LM algorithm with 10 neurons was selected for its superior performance. Ultimately, a sampling disturbance model was developed to predict the internal friction angle and density of the lunar soil simulant based on disturbance conditions, demonstrating an extremely high correlation between predicted and actual values.