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Adaptive flow assignment for CO2 transcritical power cycle (CTPC): An engine operational profile-based off-design study

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  • Li, Ligeng
  • Tian, Hua
  • Shi, Lingfeng
  • Wang, Jingyu
  • Li, Min
  • Shu, Gequn

Abstract

CO2 transcritical power cycle (CTPC) is regarded as a promising energy conservation means in engine waste heat recovery. One of the most significant characteristics that should be noted is the considerable variation of waste heat caused by the variable engine operating conditions. Hence, the ability to follow the heat-sources variation should be enhanced to maximize its energy-saving potential. In this paper, an effective and efficient approach is proposed to deal with the different degree of variability of heat sources through a novel CTPC system configuration controlling the internal mass flow rate of the working fluid actively. An adaptive flow assignment strategy is also developed to enhance the ability to follow the heat-sources variation. A comprehensive input-optimization-output framework is constructed consisting of performances analysis under both design and off-design procedures. Results show that the CTPC adaptation to the heat-sources variation can be effectively improved with the proposed system and operational strategy. Promising energy-saving potential is found when the proposed system and strategy with adaptive flow assignment are implemented. The effective operating conditions are expanded from 8 to 22 points based on the engine operational profile, and the net generated power is increased by 47.5% considering the European stationary cycle (ESC) 13-mode.

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  • Li, Ligeng & Tian, Hua & Shi, Lingfeng & Wang, Jingyu & Li, Min & Shu, Gequn, 2021. "Adaptive flow assignment for CO2 transcritical power cycle (CTPC): An engine operational profile-based off-design study," Energy, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:energy:v:225:y:2021:i:c:s0360544221005119
    DOI: 10.1016/j.energy.2021.120262
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    Cited by:

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    4. Zhang, Xuanang & Wang, Xuan & Cai, Jinwen & Wang, Rui & Bian, Xingyan & He, Zhaoxian & Tian, Hua & Shu, Gequn, 2023. "Operation strategy of a multi-mode Organic Rankine cycle system for waste heat recovery from engine cooling water," Energy, Elsevier, vol. 263(PE).
    5. He, Jintao & Shi, Lingfeng & Tian, Hua & Wang, Xuan & Sun, Xiaocun & Zhang, Meiyan & Yao, Yu & Shu, Gequn, 2023. "Applying artificial neural network to approximate and predict the transient dynamic behavior of CO2 combined cooling and power cycle," Energy, Elsevier, vol. 285(C).
    6. Li, Ligeng & Tian, Hua & Shi, Lingfeng & Zhang, Yonghao & Huang, Guangdai & Zhang, Hongfei & Wang, Xuan & Shu, Gequn, 2022. "Experimental investigation of a splitting CO2 transcritical power cycle in engine waste heat recovery," Energy, Elsevier, vol. 244(PB).
    7. He, Jintao & Zhang, Yonghao & Tian, Hua & Wang, Xuan & Li, Ligeng & Cai, Jinwen & Shi, Lingfeng & Shu, Gequn, 2022. "Dynamic performance of a multi-mode operation CO2-based system combining cooling and power generation," Applied Energy, Elsevier, vol. 312(C).
    8. Cao, Yue & Zhan, Jun & Jia, Boqing & Chen, Ranjing & Si, Fengqi, 2023. "Optimum design of bivariate operation strategy for a supercritical/ transcritical CO2 hybrid waste heat recovery system driven by gas turbine exhaust," Energy, Elsevier, vol. 284(C).
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