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Performance assessment of closed Brayton cycle-organic Rankine cycle lunar base energy system: Thermodynamic analysis, multi-objective optimization

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  • Liu, Zekuan
  • Wang, Zixuan
  • Cheng, Kunlin
  • Wang, Cong
  • Ha, Chan
  • Fei, Teng
  • Qin, Jiang

Abstract

The long lunar night, which cannot be powered by solar energy, brings a huge challenge to the lunar base energy system. Closed Brayton cycle (CBC) system is considered as an effective solution, but cannot be driven by low temperature heat sources. Organic Rankine cycle (ORC) system is used to couple into the CBC system to recover waste heat and produce more electricity. In this paper, a mathematical model of CBC-ORC system driven by collector or heat storage unit is developed, the variation of thermal efficiency, exergy destruction, and Brayton-Rankine rotating unit (BRRU) mass is evaluated during the whole lunar day. Results are as follows: when the helium mole fraction is 0.9, CBC stops on the day of 7.7 at night, which is earlier than the stop time for other helium mole fractions. The maximum power generation can reach 169.21 kW. Thermal and exergy efficiency can reach 34.49% and 31%, respectively. After three-objective optimization, the results of thermal efficiency (30.07%), exergy destruction (169.62 kW) are similar to the basic working condition, and the BRRU mass (720.3 kg) can be extremely reduced by 78.76% compared to the basic working condition, which is essential to the practical applications.

Suggested Citation

  • Liu, Zekuan & Wang, Zixuan & Cheng, Kunlin & Wang, Cong & Ha, Chan & Fei, Teng & Qin, Jiang, 2023. "Performance assessment of closed Brayton cycle-organic Rankine cycle lunar base energy system: Thermodynamic analysis, multi-objective optimization," Energy, Elsevier, vol. 278(PA).
    • Handle: RePEc:eee:energy:v:278:y:2023:i:pa:s0360544223013300
    DOI: 10.1016/j.energy.2023.127936
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    2. Lu, Shengdong & Yang, Xinle & Bu, Shujuan & Li, Weikang & Yu, Ning & Wang, Xin & Dai, Wenzhi & Liu, Xunan, 2024. "Performance and parameter prediction of SCR–ORC system based on data–model fusion and twin data–driven," Energy, Elsevier, vol. 290(C).

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