A Novel Aerodynamic Modeling Method Based on Data for Tiltrotor evtol

Abstract

A data-driven aerodynamic modeling method is proposed to address the problem that traditional modeling methods based on physical mechanisms cannot fully represent the special aerodynamic characteristics of tiltrotor evtol aircraft. By analyzing the uniquely complex aerodynamic characteristics of electric vertical take-off and landing (evtol) aircraft, an MLP neural network model has been constructed that reflects the coupling characteristics between influencing factors. Using the XV15 wind tunnel test data, a dataset was constructed, and the neural network model was trained and validated. Simulation results show that the selected data-driven method can accurately predict the aerodynamic characteristics of the longitudinal transition phase of the tiltrotor evtol.