3D large-scale SPH modeling of vehicle wading with GPU acceleration

Vehicle wading is a complex fluid-structure interaction (FSI) problem and has attracted great attention recently from the automotive industry, especially for electric vehicles. As a meshless Lagrangian particle method, smoothed particle hydrodynamics (SPH) is one of the most suitable candidates for simulations of vehicle wading due to its inherent advantages in modeling free surface flows, splash, and moving interfaces. Nevertheless, the inevitable neighbor query for the nearest adjacent particles among the support domain leads to considerable computational cost and thus limits its application in 3D large-scale simulations. In this work, a GPU-based SPH method is developed with an adaptive spatial sort technology for simulations of vehicle wading. In addition, a fast, easy-to-implement particle generator is presented for isotropic initialization of the complex vehicle geometry with optimal interpolation properties. A comparative study of vehicle wading on a puddle between the GPU-based SPH with two pieces of commercial software is used to verify the capability of the GPU-based SPH method in terms of convergence analysis, kinematic characteristics, and computing performance. Finally, different conditions of vehicle speeds, water depths, and puddle widths are tested to investigate the vehicle wading numerically. The results demonstrate that the adaptive spatial sort technology can significantly improve the computing performance of the GPU-based SPH method and meanwhile promotes the GPU-based SPH method to be a competitive tool for the study of 3D large-scale FSI problems including vehicle wading. Some helpful findings of the critical vehicle speed, water depth as well as boundary wall effect are also reported in this work.