علی احمدیان

As global energy and environmental concerns grow, reducing energy consumption and emissions has become crucial. Centrifugal pumps, widely used in various applications, are significant energy consumers, making energy loss reduction essential. This paper presents an optimization method for centrifugal pumps to minimize entropy generation losses while maintaining the same head. Using Computational Fluid Dynamics (CFD) and intelligent optimization algorithms, the study focuses on a double-suction centrifugal pump with a European specific speed of 43.8. The impeller blade profile is optimized with the goal of reducing entropy generation and preserving head. A hybrid Artificial Neural Network (ANN) and NSGA-II algorithm are employed to optimize impeller blade angles. The results show that the optimized design reduces the impeller’s turbulence kinetic energy entropy generation by 19.3%, and the volute’s by 24.2%. The optimized pump exhibits a more uniform pressure distribution, suppressing flow separation and vortices. Entropy generation in the impeller outlet and front cover plate is reduced, while the low-pressure zone on the rear cover plate shrinks. The total entropy generation loss decreases by 15.16%, and the efficiency increases by 2%, confirming the effectiveness of the optimization method in improving pump performance and energy efficiency.