An efficient surrogate-based global optimization for low sonic boom design

It is of great significance to develop efficient numerical optimization methods for low boom design of future supersonic transport aircrafts. To this end, researchers have developed the methods of combining sonic boom prediction with Genetic Algorithm (GA), gradient-based optimization using an Adjoint approach, etc. However, GA has suffered from the prohibitive computational cost for high-dimensional design optimization, and gradient-based optimization can become trapped into a local optimum. This paper proposes to use efficient surrogate-based global optimization towards more effective low sonic boom design. First, the fundamentals of the Whitham theory are introduced, and a comparison of the predicted pressure signals with experimental data shows that the theory is efficient and reasonably accurate for preliminary design of a supersonic transport aircraft. Second, the framework of Surrogate-Based Optimization (SBO) is introduced, including the key elements such as design of experiments, surrogate modeling, infill-sampling criteria and convergence criteria, etc. Third, the proposed methodology of low sonic boom design optimization using SBO is verified by a benchmark sonic boom model of the NASA stepped cone. The comparative study shows that the efficiency of the proposed method is two-orders higher than that of GA, and the optimization results are apparently better than that obtained by the gradient-based method. Finally, a wing-body configuration (69° sweepback delta wing body) taken from the first sonic boom prediction workshop of AIAA is optimized by using the proposed method, and 27.4% reduction of overpressure of the far-field N-wave is achieved. This test demonstrates the great potential of applying the surrogate-based optimization to low boom design of more complex configurations. © 2018, Press of Chinese Journal of Aeronautics. All right reserved.