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Performance analysis of low speed axial impulse turbine using two type nozzles for small-scale organic Rankine cycle

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  • Sun, Hongchuang
  • Qin, Jiang
  • Hung, Tzu-Chen
  • Huang, Hongyan
  • Yan, Peigang

Abstract

Expander is one of the core components in organic Rankine cycle (ORC). High rotating speed and limited working range are the key limitations on using turbine expander in small-scale ORC system. In this work, an axial turbine, consisting of 6 nozzles and 36 blades with mean diameter of 160 mm, is designed with partially admitted method to evaluate the feasibility of reducing rotating speed for small mass flow conditions and extending working range with R245fa used as working fluid. The results from 3D simulation show reasonable performance in design and off-design conditions. In design condition, the mass flow rate, turbine power output and turbine efficiency using Laval nozzle are 1.035 kg/s, 17.0 kW and 54.1%, higher than using convergent nozzle. However, the performance using convergent nozzle is better than using Laval nozzle when total mass flow is lower than 0.6 kg/s. Furthermore, reducing the number of nozzles can effectively increase power output of turbine and thermal efficiency of ORC for small mass flow conditions. With the designed turbine using 6 Laval nozzles and rotating at 10,000 RPM, the maximum net power output and thermal efficiency of bottoming ORC are 18.3 kW and 7.8%, respectively.

Suggested Citation

  • Sun, Hongchuang & Qin, Jiang & Hung, Tzu-Chen & Huang, Hongyan & Yan, Peigang, 2019. "Performance analysis of low speed axial impulse turbine using two type nozzles for small-scale organic Rankine cycle," Energy, Elsevier, vol. 169(C), pages 1139-1152.
    • Handle: RePEc:eee:energy:v:169:y:2019:i:c:p:1139-1152
    DOI: 10.1016/j.energy.2018.12.091
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    5. Liu, Zhen & Xu, Chuanli & Kim, Kilwon & Choi, Jongsu & Hyun, Beom-soo, 2021. "An integrated numerical model for the chamber-turbine system of an oscillating water column wave energy converter," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    6. 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).
    7. Sun, Hongchuang & Qin, Jiang & Li, Haowei & Huang, Hongyan & Yan, Peigang, 2019. "Research of a combined power and cooling system based on fuel rotating cooling air turbine and organic Rankine cycle on hypersonic aircraft," Energy, Elsevier, vol. 189(C).
    8. Witanowski, Łukasz & Klonowicz, Piotr & Lampart, Piotr & Ziółkowski, Paweł, 2023. "Multi-objective optimization of the ORC axial turbine for a waste heat recovery system working in two modes: cogeneration and condensation," Energy, Elsevier, vol. 264(C).

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