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Series view method based thermodynamic modeling and analysis for innovative precooled aeroengines with different turbine-compressor coupling schemes

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  • Yu, Xuanfei
  • Wang, Cong
  • Yu, Daren

Abstract

Fuel direct precooled engines are proposed to propel hypersonic cruise and acceleration vehicles. The engine branch includes a variety of cycle configurations. It is realized that based on a series view modeling concept, the different configurations can be parameterized via a unified thermodynamic model through which the issues concerning cycle synthesis and design can be converted to a parameter/configuration optimization problem. With the proposed model, analysis for representative cycles characterized by different turbine-compressor coupling schemes is carried out to reveal how the cycle performances are determined by its arrangements. The results indicate that the engine using fuel turbine possesses a high comprehensive performance, and the one using gas turbine can achieve larger air pressure ratio — which is beneficial for component level synthesis with rocket engines. Besides, the optimum performances for the cycles evaluated are obtained when the fuel flow required for precooling is equal to that permitted for stoichiometric combustion. Moreover, the engine performance seems more sensitive to air side pressure loss of precooler for the design with smaller air pressure ratio, and for engines with larger pressure ratio, the efficiencies of air compressor and gas turbine are the dominant factors.

Suggested Citation

  • Yu, Xuanfei & Wang, Cong & Yu, Daren, 2020. "Series view method based thermodynamic modeling and analysis for innovative precooled aeroengines with different turbine-compressor coupling schemes," Energy, Elsevier, vol. 205(C).
  • Handle: RePEc:eee:energy:v:205:y:2020:i:c:s0360544220310884
    DOI: 10.1016/j.energy.2020.117981
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    References listed on IDEAS

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    1. Yu, Xuanfei & Wang, Cong & Yu, Daren, 2019. "Precooler-design & engine-performance conjugated optimization for fuel direct precooled airbreathing propulsion," Energy, Elsevier, vol. 170(C), pages 546-556.
    2. Yu, Xuanfei & Pan, Xin & Zheng, Jialin & Wang, Cong & Yu, Daren, 2017. "Thermodynamic spectrum of direct precooled airbreathing propulsion," Energy, Elsevier, vol. 135(C), pages 777-787.
    3. Dong, Pengcheng & Tang, Hailong & Chen, Min & Zou, Zhengping, 2018. "Overall performance design of paralleled heat release and compression system for hypersonic aeroengine," Applied Energy, Elsevier, vol. 220(C), pages 36-46.
    4. Zhang, Duo & Yang, Shengbo & Zhang, Silong & Qin, Jiang & Bao, Wen, 2015. "Thermodynamic analysis on optimum performance of scramjet engine at high Mach numbers," Energy, Elsevier, vol. 90(P1), pages 1046-1054.
    5. Yu, Xuanfei & Wang, Cong & Yu, Daren, 2019. "Thermodynamic assessment on performance extremes of the fuel indirect precooled cycle for hypersonic airbreathing propulsion," Energy, Elsevier, vol. 186(C).
    6. Zhao, Wei & Huang, Chen & Zhao, Qingjun & Ma, Yingqun & Xu, Jianzhong, 2018. "Performance analysis of a pre-cooled and fuel-rich pre-burned mixed-flow turbofan cycle for high speed vehicles," Energy, Elsevier, vol. 154(C), pages 96-109.
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    1. Lv, Chengkun & Xu, Haiqi & Chang, Juntao & Wang, Youyin & Chen, Ruoyu & Yu, Daren, 2022. "Mode transition analysis of a turbine-based combined-cycle considering ammonia injection pre-compressor cooling and variable-geometry ram-combustor," Energy, Elsevier, vol. 261(PB).
    2. Cai, Changpeng & Chen, Haoying & Fang, Juan & Zheng, Qiangang & Chen, Cheng & Zhang, Haibo, 2023. "Thermodynamic analysis of a novel precooled supersonic turbine engine based on aircraft/engine integrated optimal design," Energy, Elsevier, vol. 280(C).
    3. Zhang, Duo & Chen, Chen & Yu, Xuanfei, 2023. "Control law synthetizing for an innovative indirect precooled airbreathing engine under off-design operation conditions," Energy, Elsevier, vol. 263(PE).
    4. Lv, Chengkun & Lan, Zhu & Wang, Ziao & Chang, Juntao & Yu, Daren, 2024. "Intelligent ammonia precooling control for TBCC mode transition based on neural network improved equilibrium manifold expansion model," Energy, Elsevier, vol. 288(C).

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