Fast aerodynamics prediction of wedge tail airfoils using multi-head perceptron network

Abstract

The study aimed to predict the flow fields and aerodynamic coefficients of wedge tail airfoils using a multi-head perceptron (MHP) network and classical machine learning (ML) algorithms, including k-nearest neighbors (KNN), decision tree (DT), and random forest (RF). These predictions were based on airfoil sections, x-y grid coordinates, Mach number, and angle of attack, eliminating the need to solve the Navier-Stokes (NS) equations. The database required for training the MHP network and the ML models were generated using the Ansys solver, which solved the NS equations. The results of the models were compared, and it was observed that the MHP network consistently outperformed the others in terms of prediction accuracy. The R2 score of these models on the test data was found to be close to 1. Additionally, the prediction process demonstrated a significant speed improvement of 125 times compared to conventional computational fluid dynamics (CFD) techniques. Once the training was completed, the flow fields and aerodynamic coefficients for a wedge tail airfoil could be obtained within seconds.