Multi-objective performance optimization of turbofan engine for test run

面向检验试车的涡扇发动机多目标性能优化

¹ AECC Aviation Power Co., Ltd, Xi’an 710021, China
² School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China

Received: 2 September 2023

Abstract

Turbofan engines are widely used in military and civilian aviation fields due to their high propulsion efficiency and low fuel consumption rate, and their performance directly affects the safety and stability of flight mission. It is of great practical significance to optimize the turbine inlet temperature and high-pressure compressor speed under different thrust states, so as to improve the pass rate of the first test run. This paper proposes a multi-objective performance optimization framework for turbofan engines. On the historical production dataset of a certain type of turbofan engine, the turbine inlet temperature and high-pressure compressor speed under different thrusts are taken as target variables, and area variable a, area variable b, and angle variable c in the assembly stage are taken as attribute variables. Then, the multi-objective performance optimization model based on tree augmented naive bayes is established and compared and verified with the current mainstream algorithm for verification. Finally, combining with the posterior qualified probability inference and state combination global search method, a recommended state combination table is given to assist enterprises in the formulation of component production and manufacturing assembly standards, thereby optimizing turbofan engine performance, reducing reassembly requirements, and improving the pass rate of the first test run.

摘要

涡扇发动机因其高推进效率、低燃油消耗率等特点广泛应用于军民用飞机, 其性能直接影响飞行任务的安全与稳定。针对涡扇发动机不同状态下涡轮前温度与高压转速比指标进行性能优化, 从而提高其一次检验试车通过率, 具有重要现实意义。提出涡扇发动机的多目标性能优化框架, 在某型号涡扇发动机历史生产数据集上, 以检验试车过程中不同状态下涡轮前温度与高压转速比为目标变量, 以某面积a、某面积b、某角度c为属性变量, 建立涡扇发动机多目标性能模型并与目前主流算法模型对比验证, 最后结合通过检验试车的后验概率推理与状态组合全局搜索, 给出推荐状态组合表, 辅助企业制定零部件生产制造装配标准, 从而优化涡扇发动机性能、减少重新装配次数并提高一次检验试车通过率。