Multistage centrifugal pumps (MSCPs) are critical for high-pressure fluid transport, and their hydraulic efficiency directly affects the energy consumption of energy systems. However, flow instabilities result in substantial energy loss. This study employed blade loading theory, which is closely related to the flow field state, to achieve a parametric blade design. A non-expert-driven optimization framework was constructed by integrating the Metamodel of Optimal Prognosis (MoP) with the technique for order of preference by similarity to the ideal solution based on the entropy weight method (EW-TOPSIS). The optimization objective was to improve the hydraulic efficiency of the pump in the preferred operating range, with a constant pressure-boosting performance as a constraint. The results demonstrated that the efficiency improvement exceeded 2 % across the targeted operating range. Moreover, the MoP exhibited a strong predictive capability, even in multi-parameter scenarios with limited sample data. Further vortex dynamics analysis revealed that loading redistribution reduced the incidence angle, suppressed flow separation on the blade suction surface, and, under high-flow conditions, regulated the dominant vortex transport mechanisms governed by vortex diffusion and dissipation. This research demonstrated that optimizing blade loading serves as an effective passive flow control strategy for MSCPs, enabling significant improvements in energy conservation.
Energy efficiency optimization of multistage centrifugal pumps based on blade loading control: Insights into flow instability suppression mechanism
Pavesi G.
2025
Abstract
Multistage centrifugal pumps (MSCPs) are critical for high-pressure fluid transport, and their hydraulic efficiency directly affects the energy consumption of energy systems. However, flow instabilities result in substantial energy loss. This study employed blade loading theory, which is closely related to the flow field state, to achieve a parametric blade design. A non-expert-driven optimization framework was constructed by integrating the Metamodel of Optimal Prognosis (MoP) with the technique for order of preference by similarity to the ideal solution based on the entropy weight method (EW-TOPSIS). The optimization objective was to improve the hydraulic efficiency of the pump in the preferred operating range, with a constant pressure-boosting performance as a constraint. The results demonstrated that the efficiency improvement exceeded 2 % across the targeted operating range. Moreover, the MoP exhibited a strong predictive capability, even in multi-parameter scenarios with limited sample data. Further vortex dynamics analysis revealed that loading redistribution reduced the incidence angle, suppressed flow separation on the blade suction surface, and, under high-flow conditions, regulated the dominant vortex transport mechanisms governed by vortex diffusion and dissipation. This research demonstrated that optimizing blade loading serves as an effective passive flow control strategy for MSCPs, enabling significant improvements in energy conservation.
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
