Optimization for cavitation inception performance of pump-turbine in pump mode based on genetic algorithm

Abstract

Cavitation is a negative factor of hydraulic machinery because of its undesirable effects on the operation stability and safety. For reversible pump-turbines, the improvement of cavitation inception performance in pump mode is very important due to the strict requirements. The geometry of blade leading edge is crucial for the local flow separation which affects the scale and position of pressure drop. Hence, the optimization of leading edge shape is helpful for the improvement of cavitation inception performance. Based on the genetic algorithm, optimization under multiple flow rate conditions was conducted by modifying the leading edge ellipse ratio and blade thickness on the front 20% meanline. By using CFD simulation, optimization was completed with obvious improvements on the cavitation inception performance. CFD results show that the pressure drop location had moved downstream with the increasement of the minimum pressure coefficient. Experimental verifications also got an obvious enhancement of cavitation inception performance. The stability and safety was improved by moving the cavitation inception curve out of the operating range. This optimization is proved applicable and effective for the engineering applications of reversible pump-turbines.