Multiobjective Design Optimization of a Cantilevered Ramp Injector Using the Surrogate-Assisted Evolutionary Algorithm

Abstract

The mixing efficiency of the fuel and the supersonic freestream has a great impact on the operation of the scramjet engine because of the very short residence time. In the current study, the cantilevered ramp injector was used to promote mixing of the fuel and the supersonic flow, and the injector's configuration was optimized by the surrogate-assisted evolutionary algorithm—namely, the nondominated sorting genetic algorithm coupled with the Kriging surrogate model. In the optimization process, the swept angle, the ramp angle, the compression angle, the length of the step, and the diameter of the injector were considered as the design variables, and the mixing efficiency, the total pressure recovery efficiency, and the drag force were taken as the objective functions. Finally, the Pareto front for the multiobjective design optimization results was obtained, and the relationship between the objective functions was explored. The results show that optimized drag force increases with increased mixing efficiency and total pressure recovery efficiency, and that there must be a compromise between objective functions. The larger swept angle promotes the mixing of the fuel and the supersonic airstream, and the smaller diameter of the injector is beneficial for reducing the drag force and improving both mixing efficiency and total pressure recovery efficiency.