Research on inverse design method of pitching moment for the scramjet nozzle under strong geometric constraint

Abstract

The traditional forward design method of the scramjet nozzle is difficult to obtain good performance under strong geometric constraints. Meanwhile, the existing optimal design methods rarely design from the perspective of the overall torque balance of the engine, and often only take into account the performance of the nozzle itself. This paper introduces an innovative inverse design method for the pitching moment of Single Expansion Ramp Nozzles (SERN). The core of this method integrates the Particle Swarm Optimization (PSO) algorithm with the Grey Wolf Optimization-based Kernel Extreme Learning Machine (GWO-KELM). A high-precision surrogate model of nozzle performance is constructed using a data-driven approach. Based on this surrogate model, performance constraints for PSO are established according to the desired moment. Nozzle design parameters are then iteratively optimized to achieve maximum thrust and minimum moment. The proposed method's effectiveness and accuracy are verified using Computational Fluid Dynamics (CFD). In twelve inverse design experiments, the average absolute percentage error between the designed and expected moment is 0.75 %. Compared to the reference nozzle profile, these designs achieve precise moment control while significantly improving thrust and reducing drag under strict geometric constraints. In conclusion, this paper presents an effective SERN design method, enhancing integration in hypersonic vehicles.