Engine Performance Optimization of Small Turbofan Engines Based on a Stackelberg Game

With global air traffic surpassing pre-pandemic levels, improving the fuel economy of aircraft engines has become increasingly urgent, especially for small turbofan engines that suffer from high specific fuel consumption (SFC) at high power settings. This study aims to address the trade-off between compressor pressure ratio (CPR) and turbine entering temperature (TET), which strongly influence both engine performance and fuel utilization. A hierarchical optimization framework based on Stackelberg game theory is developed, and an Adaptive Chaotic Particle Swarm Optimization (ACPSO) algorithm is employed to solve the game-theoretic model. Surrogate models using artificial neural networks are integrated to capture nonlinear relationships among design parameters and performance metrics. The results show that the proposed Stackelberg-ACPSO method outperforms conventional multi-objective particle swarm optimization (MOPSO) in balancing thrust, SFC, and thermal efficiency. Specifically, it achieves a 0.1609% reduction in SFC and a 0.0904% increase in thrust, while also providing a 0.56% decrease in SFC with a 0.51% improvement in thermal efficiency under trade-off conditions. In addition, ambient temperature is found to significantly affect the interactions between objectives, further validating the robustness of the approach. Overall, this work demonstrates the effectiveness of applying Stackelberg game theory to small turbofan engine optimization and offers valuable insights into the coupling of fuel economy and performance for future engine design.