Multi-fidelity neural network-based aerodynamic optimization framework for propeller design in electric aircraft

The energy efficiency of the electric propulsion system in electric aircraft is significantly influenced by the propeller's efficiency, making the design of a high-efficiency propeller critical. Currently, the Blade Element Momentum Theory (BEMT) is widely used for propeller design. The BEMT method exhibits high efficiency but is limited by its low accuracy due to its inability to capture three-dimensional effects of rotating flow. On the other hand, the CFD simulation can enhance the accuracy of simulating complex rotating flows, but it is computationally expensive. To alleviate the contradiction between accuracy and design efficiency in propeller optimization design, this paper describes a Multi-Fidelity Neural Network (MFNN)-based optimization framework for the optimization design of electric aircraft propellers. This method is based on high-fidelity CFD numerical simulations for the optimization design of propellers. In order to enhance design efficiency and accuracy, it fuses low-fidelity BEMT knowledge, aiming to achieve higher model accuracy with fewer CFD simulations. Based on the MFNN, an adaptive sample-infilling approach is employed to update the model, and a teaching–learning-based optimization is used to build the surrogate-based optimization framework. The proposed method improves the cruise efficiency of the propeller from 82.3% (designed by BEMT) to 87.1% and shows advantages in optimization effectiveness and efficiency compared with single-fidelity optimization.