AI-Assisted Fluid-Structure Modeling and Optimization of Pump-Jet Propulsor

The traditional design of pump-jet propulsor (PJP) usually relies on costly and time-consuming experiments and simulations. Recently, the digital revolutions have driven the integration of artificial intelligence (AI) and the fast modeling of PJP. To this end, this paper proposes an AI-assisted fluid-structure modeling and optimization method for the PJP. Firstly, the geometric parametric modeling of the PJP is developed, and the related performance characteristics are obtained via simulation methods. Secondly, based on the generated data, the Gaussian process regression (GPR) is used to model the hydrodynamic performance, the proper orthogonal decomposition (POD) is employed to predict the deformed blade coordinates, and a novel graph neural network (GNN) is proposed to handle the nonlinear stress field of blade. Finally, the multi-objective evolutionary algorithms (EAs) are performed for the optimization of PJP with the POD modal coefficients serving as design variables. Leveraging the AI technology, this study constructs a fluid-structure digital twin of the PJP, facilitating rapid performance analysis and optimization.