Artificial intelligence-driven airfoil aerodynamic design via deep surrogate modeling and evolutionary algorithm
Abstract
Airfoil aerodynamic design relies on computationally expensive flow solvers, while aerodynamic performance optimization and target pressure distribution inverse design are treated separately. This study develops a framework combining low-dimensional geometric representation, deep surrogate modeling, and evolutionary search for aerodynamic prediction, constrained optimization, and inverse design. A density-guided principal component analysis strategy constructs the airfoil design space, while a fully connected autoencoder provides a compact representation of the surface pressure distribution. A multi-branch residual surrogate network jointly predicts lift coefficient, drag coefficient, and surface pressure coefficient distribution from airfoil geometry, Mach number, and angle of attack; mean absolute errors are 3.765 × 10−3, 1.178 × 10−4, and 1.399 × 10−2, respectively. The surrogate maintains high accuracy for combinations of unseen airfoil geometries and interpolated inflow conditions, demonstrating predictive coverage within the training parameter range. Each inference requires 0.024 ms, and 6 × 104 aerodynamic evaluations are completed in 20.5 s. Coupled with the covariance matrix adaptation evolution strategy, the surrogate supports two constrained design tasks. Aerodynamic-efficiency optimization improves the lift-to-drag ratio in all 20 cases without reducing lift or increasing drag. Target pressure distribution inverse design reconstructs the prescribed suction peak, low-pressure region, and pressure-recovery process. Independent high-fidelity computational fluid dynamics recalculations of 40 optimized airfoils show close agreement with surrogate predictions. Flow-field analyses reveal localized velocity and pressure changes for efficiency-oriented optimization, whereas inverse design produces broader upper-surface acceleration, increased suction, and modified pressure recovery. These results demonstrate an efficient framework linking airfoil geometry, aerodynamic performance, and distributed flow responses.