Hierarchical optimization method of supersonic low sonic boom configuration

Sonic boom suppression is a key technology that must be broken through in the development of a new generation of supersonic civil aircraft. The reasonable design of configuration parameters can make the aircraft have good sonic boom characteristics. In order to break through the bottleneck of evolutionary algorithm in optimizing large-scale design variables, a hierarchical optimization method based on data mining is proposed, and the decision tree (DT) algorithm based data mining is used to extract the design knowledge, obtain the hierarchical information of design variables, and guide the configuration optimization of low sonic boom aircraft. For a low boom supersonic aircraft, five configuration parameters, including sweep angle, aspect ratio, taper ratio, dihedral angle and fuselage slenderness ratio, are selected as design variables to carry out the numerical experiments of hierarchical optimization, and the method is performed with comparison verification with the integrated optimization method. The results show that the hierarchical optimization method can obtain the optimal solution consistent with the integrated optimization method, and the convergence speed of hierarchical optimization method is significantly faster than that of integrated optimization method, and the performance of different optimization processes is more robust.