A multi-task learning framework for aerodynamic computation of two-dimensional airfoils

Abstract

Accurate and efficient prediction of airfoil aerodynamic coefficients is essential for improving aircraft performance. However, current research often encounters significant challenges in balancing accuracy with computational efficiency when predicting complex aerodynamic coefficients. In this paper, a Multi-Task Learning framework for Aerodynamic parameters Computation (MTL4AC) of two-dimensional (2D) airfoils is proposed. The MTL4AC processes two key subtasks: flow field prediction and pressure coefficient prediction. These two subtasks complement each other to reveal both global and local aerodynamic changes around the airfoil. The flow field prediction provides a coarse-grained global perspective, which focuses on the pressure and velocity variations on and around the airfoil surface. The pressure coefficient prediction offers a fine-grained local perspective, which concentrates on the pressure distribution on the airfoil surface to accurately calculate lift and drag coefficients. The MTL4AC demonstrated substantial improvements in the experiments conducted on the public dataset, achieving significant enhancements in accuracy and stability. This research contributes an accurate and efficient framework for aerodynamic computation, integrating geometric features and advanced multi-task learning techniques to achieve superior performance in predicting aerodynamic coefficients.