Slat cove filler design for noise reduction based on artificial neural network

Airframe noise becomes one of the dominant noise sources during landing and taking off phases of a civil aircraft because that jet engine noise is decreased obviously in recent years. High lift device noise is the main source of airframe noise, decreasing high lift device noise makes a significant contribution to the overall noise reduction for a civil aircraft. Slat cove filler (SCF), as a type of slat noise reduction method, may decrease the aerodynamic performances, such as the maximum lift coefficient and the stall angle. To this situation, a database of SCF is built for a multi-element airfoil, and one of the SCFs in the database is selected as the reference configuration, 20 optimized configurations are generated through confidence coefficient reasoning. Aerodynamic performance of each optimized configuration is predicted by a back propagation(BP) artificial neural network, and the one with the best aerodynamic performance is selected as the design configuration for verifying computation, steady Navier-Stokes simulations are executed to compare aerodynamic performances of the design configuration with that of the baseline configuration. A hybrid method, combining CFD with acoustic analogy, is applied to compare the acoustic performances of the design configuration with that of the baseline configuration. The results indicate that the noises are reduced significantly at the given observation points by adding the designed SCF, while the aerodynamic performances have been maintained.