The discrete element method (DEM) is one of the most popular methods for simulating lunar soil simulants due to the lack of real lunar soil. To reduce the computational consumption and difficulty because of complex particle models, simplified particle models, in which a single particle consists of two, four, or six elements, are discussed in this paper. Three steps, including random generation, particle replacement, and sedimentation, can generate the proposed simulant. The relationship between the mechanical properties of the simulant and microscopic parameters defined in DEM was analyzed by the orthogonal array testing (OATS) technique. Then, the prediction functions, which can calculate mechanical properties from inputting the microscopic parameters without carrying out the DEM, are also established by a back-propagation artificial neural network (BP-ANN). The widely used physical simulants JSC-1 from the USA and FJS-1 from Japan are simulated in DEM from the prediction function with high accuracy.
