Airfoil Shape Optimization in Ultralow Reynolds Flows Applying a Deep Learning–Genetic Algorithm Framework on a Shear‐Stress‐Based Inverse Design Method

ABSTRACT Pressure‐based inverse design (ID) cannot converge in flow regimes with ultralow Reynolds numbers (Res). This study proposes a shear‐stress‐based ID method for airfoil design at Re = 1000 at the optimal angle of attack (AOA) in the presence of a laminar separation bubble. The proposed method applies the difference between the existing and target shear stress distributions (SSDs) to a deformable surface. The Navier–Stokes equations are solved to calculate the wall SSD during each iteration of the ID process. This process modifies the airfoil geometry until the abovementioned difference becomes negligible, achieving convergence to the target geometry. Achieving the maximum lift‐to‐drag ratio by manually correcting the wall SSD involves extensive trial and error, making it almost impossible. Thus, in the second part of this research, we trained Gaussian process regression and an ensemble of trees deep learning (DL) models using data generated during ID at the optimal AOA to predict lift and drag coefficients, respectively. The SSD was optimized throughout the ID process by coupling the DL models with a genetic algorithm (GA). Optimization was performed in several consecutive cycles, with the DL models becoming more accurate and updated as more data were gathered, helping the GA obtain the optimal SSD and geometry precisely. Finally, the performance curves of different geometries obtained through the optimization cycles were evaluated and compared using the Fluent solver. The results demonstrated a 22.42% increase in the lift‐to‐drag ratio relative to the initial population at the optimal AOA.