Parametric Design of an Ultrahigh-Head Pump-Turbine Runner Based on Multiobjective Optimization

Linhai Liu, Baoshan Zhu, Li Bai, Xiaobing Liu and Yue Zhao
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Linhai Liu: Department of Thermal Engineering, State Key Laboratory of Hydro Science and Engineering, Tsinghua University, Beijing 100084, China
Baoshan Zhu: Department of Thermal Engineering, State Key Laboratory of Hydro Science and Engineering, Tsinghua University, Beijing 100084, China
Li Bai: School of Energy and Power Engineering, Xihua University, Chengdu 610039, China
Xiaobing Liu: School of Energy and Power Engineering, Xihua University, Chengdu 610039, China
Yue Zhao: Harbin Institute of Large Electrical Machinery, Harbin 150040, China

Energies, 2017, vol. 10, issue 8, 1-16

Abstract: Pumped hydro energy storage (PHES) is currently the only proven large-scale energy storage technology. Frequent changes between pump and turbine operations pose significant challenges in the design of a pump-turbine runner with high efficiency and stability, especially for ultrahigh-head reversible pump-turbine runners. In the present paper, a multiobjective optimization design system is used to develop an ultrahigh-head runner with good overall performance. An optimum configuration was selected from the optimization results. The effects of key design parameters—namely blade loading and blade lean—were then investigated in order to determine their effects on runner efficiency and cavitation characteristics. The paper highlights the guidelines for application of inverse design method to high-head reversible pump-turbine runners. Middle-loaded blade loading distribution on the hub, back-loaded distribution on the shroud, and large positive blade lean angle on the high pressure side are good for the improvement of runner power performance. The cavitation characteristic is mainly influenced by the blade loading distribution near the low pressure side, and large blade lean angles have a negative impact on runner cavitation characteristics.

Keywords: ultrahigh-head pump-turbine; multiobjective optimization; blade loading; blade lean (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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