Abstract
Variable camber is an effective method for improving the flight efficiency of large aircraft, and has attracted the attention of researchers. This work focused on the optimization of a variable camber airfoil. First, the influences of the variable camber of the leading and trailing edges on the airfoil aerodynamic performance were investigated using a computational fluid dynamics numerical simulation. An initial database was established for a deep neural network. Second, an iterative algorithm was constructed to optimize the variable camber airfoil in terms of the rotation angle of the leading edge, deflection position of the leading edge, rotation angle of the trailing edge, and deflection position of the trailing edge. A genetic algorithm was used in each iteration to maximize the lift coefficient and lift-to-drag ratio, as predicted using a deep neural network (DNN). The optimal results were validated using Fluent. If the DNN result approximated the Fluent results, the iterative process was stopped. Otherwise, the Fluent results were inserted into the database to update the DNN prediction model. The optimization results showed that the lift-to-drag ratio of the 2D airfoil could be increased by more than 14 when the angle of attack was less than 8° relative to the original airfoil. Furthermore, to validate the 2D optimal results, the optimized 2D airfoil was stretched into 3D, and it was discovered that the aerodynamic performance trend of the 3D airfoil with respect to the angle of attack was basically the same as that of the 2D airfoil. In addition, the corresponding 3D airfoil improved the aerodynamic performance and reduced the noise at a high frequency (by approximately 16 dB). In contrast, the noise in the low and medium frequencies remained unchanged. Therefore, the optimization method and results can provide a reference for the aerodynamic design and acoustic design of large civil aircraft wings.
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