| Abstract:Infrared suppression measures, such as bleed air cooling, have a significant impact on overall engine performance.
However, the influence of infrared stealth measures is often not taken into account during the preliminary engine design stage. In order to
develop a comprehensive design method for low infrared turbofan engines, a radial basis function (RBF) model for a low bypass ratio
turbofan engine with central cone film cooling was established, and the adaptive mutation genetic algorithm (AMGA) was used for optimiza?
tion design. Based on design experiences, the optimal design scheme for the low infrared turbofan engine was determined. The data for the
surrogate model was obtained from the component level models of the low infrared turbofan engine, and Gaussian functions were used to
establish the RBF algorithm for creating the surrogate model. The results indicate that the accuracy of the RBF model surpasses that of
traditional Kriging algorithms. Based on the RBF model, the AMGA algorithm was used for optimization. Compared with benchmark
engines, the optimal solution resulted in a 1.03% reduction in specific thrust, a 0.86% decrease in specific fuel consumption, and a remark?
able 47.75% reduction in tail infrared radiation, achieving superior infrared suppression effects. |
