Performance analysis of geometrically optimized PaT at turbine mode: A perspective of entropy production evaluation

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE(2022)

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摘要
In the field of hydroelectric power, Pump as Turbine (PaT) is an important tool to achieve recycling the redundant kinetic energy in small stream/rivers. However, due to the contradiction between the reverse fluid flow working at turbine mode and the designed flow passage of pump geometry, the internal hydraulic loss in PaT is excessive. As a result, it renders the PaT performance extremely low affecting its wide applications with the demand of high efficiency operation. Therefore, this paper aims to optimize the geometric parameters of the impeller in PaT turbine mode under three different typical flow conditions (i.e. rated, best efficiency, and overload working conditions). After the geometric optimization, the internal energy losses among the flow passages are analyzed by using the entropy production method. The results show that the impeller passage exhibits obviously higher entropy loss in comparison to the volute and the outlet pipe. Inside the impeller, the blade leading and trailing edges on both suction and pressure sides are the locations where the majority of the impeller energy losses occur, and the high entropy area is concentrated at the impeller inlet area. Moreover, The energy loss gradually decreases from blade leading edge (BLE) to blade trailing edge (BTE), and the entropy production decreases from the blade pressure side (PS) to the blade suction side (SS) due to the large mixing flow loss from PS to SS. It is concluded that the entropy production analysis is effective in explaining the PaT power losses caused by the geometric mismatch of different components including inlet, impeller, and outlet, providing the reference value for the geometric optimization of other turbine machinery.
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关键词
Pump-as-turbine, geometric optimization, entropy production analysis, artificial neural network, pareto-base genetic algorithm
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