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Modeling and performance analysis of a pre-cooling and power generation system based on the supercritical CO2 Brayton cycle on turbine-based combined cycle engines

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  • Ma, Xiaofeng
  • Jiang, Peixue
  • Zhu, Yinhai

Abstract

Turbine-based combined cycle (TBCC) engines are a promising system of hypersonic vehicles, the main problem being the thrust gap during propulsion. To solve this problem, we proposed a pre-cooling system of turbofan based on the supercritical carbon dioxide (sCO2) Brayton cycle for TBCC engines fueled by hydrocarbon. A pre-cooler was set in the intake of the turbofan engine to achieve coupling between the Brayton cycle and the turbofan engine. The turbofan and Brayton cycle models were established, and several weight and limited cold source parameters were used to evaluate the system's performance. The effects of the inlet air temperature drop and pressure drop caused by the pre-cooler on the engine performance were obtained. The relationship between thermal performance and system weight of the sCO2 Brayton cycle under different pre-cooling target temperatures and recuperated heat loads was also analyzed. The results showed that it is feasible to use sCO2 to pre-cool the inlet air and generate power. When the pre-cooling target was set to Ma 2.2, the simple cycle could output 347 kW power with a power-to-weight ratio of 0.39 kW/kg. This study provides a new scheme for the pre-cooling and power generation technology of hydrocarbon-fueled TBCC engines.

Suggested Citation

  • Ma, Xiaofeng & Jiang, Peixue & Zhu, Yinhai, 2023. "Modeling and performance analysis of a pre-cooling and power generation system based on the supercritical CO2 Brayton cycle on turbine-based combined cycle engines," Energy, Elsevier, vol. 284(C).
  • Handle: RePEc:eee:energy:v:284:y:2023:i:c:s0360544223019345
    DOI: 10.1016/j.energy.2023.128540
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    References listed on IDEAS

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    2. 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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