Scramjet nozzle performance prediction based on NSGA-Ⅲ-SAM algorithm

The exhaust characteristics of a nozzle directly affect the overall performance of the scramjet engine. It is crucial to effectively predict the nozzle performance to prevent drastic changes for the stable operation of the engine. Numerical simulations of three-dimensional asymmetric nozzles under different flight conditions were conducted to build a dataset for predicting nozzle performance at various Mach numbers and nozzle pressure ratios. Considering the limitations of traditional multi-objective optimization algorithms, a Non-dominated Sorting Genetic Algorithm-Ⅲ-Simulated Annealing Mutation (NSGA-Ⅲ-SAM) was proposed to extract the optimal wall pressure measurement points for the nozzle. By using the optimal pressure characteristic data as input and the axial thrust coefficient, pitching moment coefficient, and lift coefficient as outputs, a nozzle performance parameter prediction model based on the One Dimension-Convolutional Neural Network (1D-CNN) was established and validated by the data of over-expanded states at the Mach numbers from 4.5 to 6.0. The results show that the optimal pressure positions extracted by the NSGA-Ⅲ-SAM algorithm enable the model to have high-precision and rapid prediction performance, with the overall average absolute error of all performance parameters being within 0.5%, the maximum absolute error not exceeding 0.8%, and the average prediction time being only about 0.6 ms. The proposed prediction model and method provide a reliable technical foundation for monitoring nozzle performance and adjusting exhaust conditions.