A novel optimization method for maintaining aerodynamic performances in noise reduction design

Noise reduction has now become an indispensable part of civil aircraft design, however, it is difficult to reduce noise and maintain aerodynamic performances simultaneously. In this case, a novel method based on artificial neural network is introduced. An established database lays foundation for and serves as the training sample of a back propagation (BP) artificial neural network, which uses confidence coefficient reasoning method for optimization later on. Then the trained BP network is employed to select the most satisfactory configuration for validating computations. In order to verify the design method, a process of slat cove filler (SCF) design for EET HLD is presented. Unsteady simulations based on DES method are carried out to validate aerodynamic performances of both the baseline and design configurations, while noise reduction effect is verified by a hybrid method which combines unsteady DES method with acoustic analogy theory. The numerical results indicate a significant noise reduction at the given observation point while the aerodynamic performances are still maintained.