Surrogate-based multi-objective optimization for inlet flow separation control using self-sustaining synthetic jet

Abstract

The phenomenon of flow separation induced by shock wave/boundary layer interaction (SWBLI) is commonly observed in the inlets of high-speed aircraft, exerting a significant influence on inlet performance. In this paper, the self-sustaining synthetic jet is employed to mitigate the phenomenon of inlet flow separation through numerical simulation. The optimization parameters include the gap width, the location of the jet gap, and the location of the suction gap. The surrogate-based multi-objective optimization approach is utilized to minimize the separation zone and maximize the flow coefficient. The orthogonal experimental design is employed to acquire sample points and establish a Kriging model. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized for optimizing the design configuration of the self-sustaining synthetic jet, and a comparative analysis of the flow structure under uncontrolled and optimized conditions is conducted. The results indicate that airflow from the high-pressure inlet mixes with that from the low-pressure zone via the self-sustaining synthetic jet channel. This reduces the adverse pressure gradient of the boundary layer and significantly inhibits flow separation. And the optimized configuration outperforms both the uncontrolled and experimental cases. Within the parameter settings considered in this study, a maximum flow coefficient of 0.3647 and a minimum separation zone of 5930 mm² have been achieved, resulting in an improvement of 84.3 % and a reduction of 59.0 %.