Development of convolutional neural network-based surrogate model for three-dimensional vacuum plume prediction via direct simulation Monte Carlo method

The vacuum plume phenomenon encountered during lunar exploration missions poses significant challenges, such as impingement forces, heat fluxes, and spacecraft contamination. Numerical simulation represents the predominant method for evaluating the impacts of vacuum plumes. However, the conventional direct simulation Monte Carlo (DSMC) method, despite being the standard, is notably time-consuming and impractical for real-time analysis. Addressing this limitation, our research explores deep learning, specifically convolutional neural networks (CNN), for the efficient prediction of vacuum plume dynamics. We introduce a novel CNN-based DSMC method (CNN-DSMC-3D), leveraging a dataset obtained from three-dimensional DSMC simulations. This approach translates the spacecraft's shape and boundary conditions into a signed distance function and an identifier matrix. The CNN-DSMC-3D method effectively predicts the vacuum plume field, aligning closely with DSMC results across various lunar surface conditions. Crucially, the CNN-DSMC-3D method achieves a speed increase in four to six orders of magnitude over the conventional DSMC method, demonstrating substantial potential for real-time aerospace applications and offering a paradigm shift in the simulation of lunar landing scenarios.