Optimization of unsteady cavitation performance of airfoil based on machine learning

In order to improve the anti-cavitation performance of the airfoil under unsteady conditions, a modified leading-edge type function transformation method is employed to characterize the airfoil geometry. The optimal Latin hypercube sampling method was used to sample in the optimized space, and the cavitation performance parameters of each airfoil under unsteady state were calculated by CFD. The mapping relationship between airfoil geometry and cavitation performance parameters was constructed by BP neural network, and the time-averaged non-dimensionless cavitation area was used as the optimization objective. The cavitation performance of two-dimensional NACA66 (MOD) under the condition of σ = 0.83 was optimized by using Genetic Algorithm (GA). The unsteady cavitation flow field of the original airfoil and the two airfoils optimized by different neural network