Research on a Fast Resistance Performance Prediction Method for SUBOFF Model Based on Physics-Informed Neural Networks (PINNs)

Physics-Informed Neural Networks (PINNs) represent a novel intelligent algorithm for solving partial differential equations (PDEs), which has been partially validated in solving Navier-Stokes equations. However, numerous challenges remain in the application of PINNs to the calculation of hydrodynamic performance of ships. This paper utilizes discrete solutions of the Navier-Stokes (N-S) equations obtained via the Finite Volume Method (FVM) and Reynolds Averaged Navier-Stokes (RANS) approach of the CFD method for training PINNs to directly solve flow field information. This approach achieves a solution with approximate accuracy to that based on the CFD method, but with significantly reduced time consumption. The development of this algorithm could offer a novel perspective for calculating the hydrodynamic performance of ships. The study focuses on the DARPA SUBOFF-B model, using field information obtained at specific speeds through the CFD method, including pressure and velocity fields, as the training set to train the neural parameters within PINNs. This enables the PINNs model to predict the flow field information around the hull and calculate the hull resistance through integration. The results predicted by the PINNs method are compared and analyzed against those calculated via the CFD method, Shows the comparison of the predicted results of the hull surface pressure field, as well as the relative error of the predicted total resistance., thereby proving the feasibility of the method.