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涡轮级间混合架构的变循环发动机总体性能研究
刘备1,2,聂聆聪3,廖增步1,2,牟春晖3,宋志平1,2
1.西安交通大学 机械工程学院,陕西 西安 710049;2.西安交通大学 机械制造系统工程国家重点实验室,陕西 西安 710054;3.北京动力机械研究所,北京 100074
摘要:
为满足军用空优战斗机的超声速巡航和亚声速巡航需求,提出了一种涡轮级间混合的变循环发动机架构,该架构包含两种工作模式和八种变循环特征。首先阐述了本架构设计理念与任务目标;然后建立了部件级实时动态变循环发动机模型;最后,在经过特有的粒子群寻优算法对变循环发动机亚声速巡航油耗寻优,对比第四代涡扇发动机油耗产生了12.75%亚声速巡航收益。此外,即使在超声速巡航点本架构也实现了一定的油耗收益和推力提升。在超声速巡航和亚声速巡航之间,可实现0.29~0.82的涵道比大范围变化。结果表明,该架构有望成为一种适用于下一代空优战斗机的变循环发动机架构。
关键词:  变循环发动机  涡轮级间混合  部件级建模  粒子群寻优  总体性能
DOI:10.13675/j.cnki.tjjs.2205034
分类号:V231
基金项目:
Overall Performance of Interstage Turbine Mixed Architecture Variable Cycle Engine
LIU Bei1,2,NIE Ling-cong3,LIAO Zeng-bu1,2,MU Chun-hui3,SONG Zhi-ping1,2
1.School of Mechanical Engineering,Xi’an Jiaotong University,Xi’an 710049,China;2.State Key Laboratory for Manufacturing Systems Engineering,Xi’an Jiaotong University,Xi’an 710054,China;3.Beijing Power Machinery Institute,Beijing 100074,China
Abstract:
To meet the supersonic cruise and subsonic cruise requirements of military air superiority fighter aircraft, a new architecture called interstage turbine mixed architecture is proposed. It contains two operating modes and eight variable cycle features. Firstly, the design concept and mission requirements are described. Then, a component-level real-time dynamic variable cycle engine model is established. Finally, the subsonic cruise fuel consumption of this variable cycle engine is optimized by a special particle swarm optimization algorithm. Compared with the fourth-generation turbofan engine, this architecture reduces the fuel consumption by 12.75% in the subsonic cruise. In addition, it also reduces some fuel consumption and improves the thrust of the intermediate state even in the supersonic cruise. Between the supersonic cruise and the subsonic cruise, it can change the bypass ratio from 0.29 to 0.82. The results show that this architecture is expected to be a variable cycle engine architecture suitable for next-generation air superiority fighters.
Key words:  Variable cycle engine  Interstage turbine mixed  Component-level modeling  Particle swarm optimization  Overall performance