Flow field reconstruction of underwater vehicle based on physics-informed neural network

Aiming at the technical challenge of reconstructing high-resolution flow fields from sparse flow field data, research on flow field reconstruction based on the physics-informed neural network (PINN) for underwater vehicles has been carried out. To begin with, the Lattice Boltzmann Method (LBM) was used to construct the benchmark flow field data (Timesequenced velocity and pressure fields in three-dimensional space) of the SUBOFF model, and the sparse flow field dataset is generated by spatial down-sampling of the benchmark flow field data. Subsequently, the fully data-driven algorithm and the PINN deep learning algorithm for SUBOFF flow reconstruction (with spatio-temporal coordinate points as inputs and velocity and pressure fields as outputs) were developed, the fully data-driven model and the PINN were obtained by training with the sparse flow data set. Eventually, only the spatiotemporal coordinate points from the benchmark flow field data were input into the two models, and the complete flow field was reconstructed using model inference inversion, and the results were compared and analyzed with the benchmark flow field data generated by the LBM, to validate the correctness of the reconstructed flow field for the two models. The results demonstrate that the PINN model is significantly more accurate than the fully data-driven model in reconstructing the flow field from sparse flow field data. The study provides a new flow field reconstruction solution that could contribute to the optimal design of the hydrodynamic performance of underwater vehicles and similar structures.