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Analysis and optimization of a nuclear power S-CO2 recompression Brayton CHP system on Mars using hybrid CPO-SVR-COA metaheuristics

Author

Listed:
  • Lu, Tong
  • Zhang, Haochun
  • Zhou, Ziyang
  • You, Ersheng

Abstract

Among the planets Mars most closely mirrors Earth and is thus a more viable option for human habitation. Its exceptional strategic significance and value further highlight the crucial necessity of establishing a base on Mars. The primary challenge in constructing a Mars base lies in ensuring a stable supply of electricity and heat. Nuclear reactors, with their high power output, extended operational lifespan, and all-weather stability, serve as an optimal energy source for such a base. Moreover, the Brayton cycle within a megawatt-scale nuclear energy system can enhance efficiency in energy conversion. In this study, a supercritical carbon dioxide (S-CO2) recompression Brayton cycle combined heat and power system (RCBC-CHP) was proposed in the context of Mars base, utilizing a nuclear reactor as its heat source. The thermodynamic model and mass estimation model were developed, analyzing the effects of key parameters—such as compressor inlet temperature and pressure, turbine inlet temperature, pressure ratio, and splitting ratio—on total thermal efficiency, power generation efficiency, total mass, and heating to power ratio. The system prediction model was developed using support vector regression (SVR) optimized by the crested porcupine optimizer (CPO), forming the CPO-SVR approach. Simultaneously, the coati optimization algorithm (COA) was employed to optimize the system, with total thermal efficiency, power generation efficiency, and total mass set as the optimization objectives. Balancing the goals of maximizing total thermal efficiency and power generation efficiency while minimizing total mass, the optimal results obtained were a total thermal efficiency of 79.40 %, a power generation efficiency of 38.16 %, a total mass of 19631.37 kg, and a heating to power ratio of 1.0434.

Suggested Citation

  • Lu, Tong & Zhang, Haochun & Zhou, Ziyang & You, Ersheng, 2025. "Analysis and optimization of a nuclear power S-CO2 recompression Brayton CHP system on Mars using hybrid CPO-SVR-COA metaheuristics," Energy, Elsevier, vol. 333(C).
    • Handle: RePEc:eee:energy:v:333:y:2025:i:c:s0360544225029950
    DOI: 10.1016/j.energy.2025.137353
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    References listed on IDEAS

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    1. Miao, Xinyu & Zhang, Haochun & Ma, Fangwei & Deng, MingHao & You, Ersheng, 2024. "Thermodynamic, exergoeconomic evaluation and optimization of S–N2O/t-N2O nuclear power cycle for the construction of the lunar base," Energy, Elsevier, vol. 302(C).
    2. Hu, Lian & Chen, Deqi & Huang, Yanping & Li, Le & Cao, Yiding & Yuan, Dewen & Wang, Junfeng & Pan, Liangming, 2015. "Investigation on the performance of the supercritical Brayton cycle with CO2-based binary mixture as working fluid for an energy transportation system of a nuclear reactor," Energy, Elsevier, vol. 89(C), pages 874-886.
    3. Zhao, Chengxuan & Yang, Xiao & Yu, Jie & Yang, Minghan & Wang, Jianye & Chen, Shuai, 2023. "Interval type-2 fuzzy logic control for a space nuclear reactor core power system," Energy, Elsevier, vol. 280(C).
    4. Liu, Liuchen & Wu, Jinlu & Zhong, Fen & Gao, Naiping & Cui, Guomin, 2021. "Development of a novel cogeneration system by combing organic rankine cycle and heat pump cycle for waste heat recovery," Energy, Elsevier, vol. 217(C).
    5. Yang, Yiping & Huang, Yulei & Jiang, Peixue & Zhu, Yinhai, 2020. "Multi-objective optimization of combined cooling, heating, and power systems with supercritical CO2 recompression Brayton cycle," Applied Energy, Elsevier, vol. 271(C).
    6. Vladimir N. Vapnik, 1995. "The Nature of Statistical Learning Theory," Springer Books, Springer, number 978-1-4757-2440-0, March.
    7. 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).
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    1. Tang, Xiangrong & Yang, Zongxin & Liu, ChuangKan & Huang, Linyu & Liu, Haosen & Ning, Qian, 2025. "Design of closed Brayton cycle power generation system for megawatt-scale space nuclear reactor," Energy, Elsevier, vol. 335(C).

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