Reconstruction of hydrofoil cavitation flow based on the chain-style physics-informed neural network

Cavitation flow is a typical complex flow phenomenon, which involves many flow mechanisms. At present, the main approach to reconstruct the cavitation flow field based on the experimental results is numerical simulation, which has the defects of low computational efficiency and is difficult to effectively use the experimental data. In this paper, a chain-style physics-informed neural network (chain-style PINN) is developed to solve the reconstruction problem of cavitation flow field. On the basis of decoupling the governing equations, our method solves the physical quantities of interest serially by introducing multiple serial PINNs. A physics-informed loss function is defined to realize the assimilation of experimental data and physical mechanism. The prediction for a 3D NACA66 hydrofoil case is validated by comparing with Direct Numerical Simulation (DNS), which demonstrates that the calculation time is reduced by about 70% while the relative L2 errors of pressure and liquid volume fraction fields are only 0.0030 and 0.0035. While comparing with the existing method Hidden Fluid Mechanics (i.e., baseline PINN), the results show the validity of our method. To the best of our knowledge, this is the first theoretical work that applies PINN to cavitation flow.