面向检验试车的涡扇发动机多目标性能优化

Turbofan engines are widely used in military and civilian aviation fields due to their high propulsion effi⁃ ciency 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 un⁃ der 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 at⁃ tribute 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 assem⁃ bly standards, thereby optimizing turbofan engine performance, reducing reassembly requirements, and improving the pass rate of the first test run.