Immersed boundary-physics informed machine learning approach for fluid–solid coupling. (1st November 2022) - Fang, Dehong; Tan, Jifu
Abstract: Fluid–solid coupling is commonly used but sometimes expensive in large-scale simulations for fluid dynamics. Conventional numerical methods rely on high performance computers and parallel computing techniques to accelerate simulations. In this work, a lightweight immersed boundary-physics informed machine learning model is proposed for the fluid–solid coupling based on the physical framework of multi-direct forcing of the immersed boundary method. Two dimensional flows past a static cylinder are adopted as case studies for the drag. It shows close agreements of drag coefficient among simulations conducted by the immersed boundary-lattice Boltzmann method (IB-LBM), immersed boundary-physics informed neural network model (IB-PINN), and data from references. No-slip boundary conditions around the cylinder boundaries are closely satisfied and the time consumed by the machine learning model is reduced by 38.5% compared with IB-LBM, which demonstrates that the machine learning approach is robust, fast, and accurate. Highlights: Use physics-informed machine learning model to conduct fluid–solid coupling. The machine learning model is shown to be fast, reliable and accurate. Complex physical process can be learned and replaced by machine learning model.