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Aerodynamic design of the high pressure and low pressure axial turbines for the improved coal-fired recompression SCO2 reheated Brayton cycle

Author

Listed:
  • Han, Wanlong
  • Zhang, Yifan
  • Li, Hongzhi
  • Yao, Mingyu
  • Wang, Yueming
  • Feng, Zhenping
  • Zhou, Dong
  • Dan, Guangju

Abstract

A new type of improved coal-fired recompression SCO2 Brayton cycle with a second split flow to the boiler was introduced. The design parameters of the high and low pressure turbines of the above SCO2 Brayton cycle were determined based on a consideration of the economics of the SCO2 system and the realization of the equipment. The self-compiled mean-line design program for two-stage SCO2 axial turbines was used to design the high and low pressure turbines for the 5 MW SCO2 Brayton cycle, with the important design data of the two turbines being given. RANS equations and the SST Turbulence Model were chosen for numerical simulation of the aerodynamic performance of two turbines in the design point and variable operating conditions that used ANSYS CFX and the real physical property data file of SCO2 from NIST software. Numerical results indicated that the isentropic efficiency of the SCO2 high pressure turbine and low pressure turbine with a good variable operating performance are 82.88% and 82.26% in the design point, respectively. The output power of the high pressure turbine and low pressure turbine are 3,251 kW and 6,156 kW, respectively, in the design point, which can meet the design requirements. The Cps curves, limited streamline spectrums, and the Mach number contours of the root, middle and top flow field of the turbine blades is reasonable.

Suggested Citation

  • Han, Wanlong & Zhang, Yifan & Li, Hongzhi & Yao, Mingyu & Wang, Yueming & Feng, Zhenping & Zhou, Dong & Dan, Guangju, 2019. "Aerodynamic design of the high pressure and low pressure axial turbines for the improved coal-fired recompression SCO2 reheated Brayton cycle," Energy, Elsevier, vol. 179(C), pages 442-453.
  • Handle: RePEc:eee:energy:v:179:y:2019:i:c:p:442-453
    DOI: 10.1016/j.energy.2019.05.016
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    References listed on IDEAS

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    1. Zhang, Yifan & Li, Hongzhi & Han, Wanlong & Bai, Wengang & Yang, Yu & Yao, Mingyu & Wang, Yueming, 2018. "Improved design of supercritical CO2 Brayton cycle for coal-fired power plant," Energy, Elsevier, vol. 155(C), pages 1-14.
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    Cited by:

    1. Mohamed Ali Kammoun & Sadok Turki & Nidhal Rezg, 2020. "Optimization of Flight Rescheduling Problem under Carbon Tax," Sustainability, MDPI, vol. 12(14), pages 1-19, July.
    2. Zhou, Kai & Zheng, Xinqian, 2022. "Novel wave-shaped tip-shroud contours towards reducing turbine leakage loss," Energy, Elsevier, vol. 254(PA).
    3. Zhang, Yifan & Li, Hongzhi & Li, Kailun & Yang, Yu & Zhou, Yujia & Zhang, Xuwei & Xu, Ruina & Zhuge, Weilin & Lei, Xianliang & Dan, Guangju, 2022. "Dynamic characteristics and control strategies of the supercritical CO2 Brayton cycle tailored for the new generation concentrating solar power," Applied Energy, Elsevier, vol. 328(C).
    4. Wang, Qi & Yang, Li & Rao, Yu, 2021. "Establishment of a generalizable model on a small-scale dataset to predict the surface pressure distribution of gas turbine blades," Energy, Elsevier, vol. 214(C).
    5. Li, Jinxing & Liu, Tianyuan & Wang, Yuqi & Xie, Yonghui, 2022. "Integrated graph deep learning framework for flow field reconstruction and performance prediction of turbomachinery," Energy, Elsevier, vol. 254(PC).
    6. Mahdi Erfanian Nakhchi & Shine Win Naung & Mohammad Rahmati, 2023. "Direct Numerical Simulations of Turbulent Flow over Low-Pressure Turbine Blades with Aeroelastic Vibrations and Inflow Wakes," Energies, MDPI, vol. 16(6), pages 1-21, March.
    7. Zhang, Ruiyuan & Su, Wen & Lin, Xinxing & Zhou, Naijun & Zhao, Li, 2020. "Thermodynamic analysis and parametric optimization of a novel S–CO2 power cycle for the waste heat recovery of internal combustion engines," Energy, Elsevier, vol. 209(C).
    8. Du, Qiuwan & Yang, Like & Li, Liangliang & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Aerodynamic design and optimization of blade end wall profile of turbomachinery based on series convolutional neural network," Energy, Elsevier, vol. 244(PA).

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