Hydrodynamic analysis of centrifugal pump impellers optimized with the multi-objective genetic algorithm and the non-dominated sorting genetic algorithm III

Unstable internal flow structures significantly compromise the mechanical reliability of centrifugal pumps. This study elucidates the coupling mechanism between impeller geometry, vortex dynamics, and hydrodynamic forces. A hybrid optimization framework, integrating a neural-network surrogate model with both multi-objective genetic algorithm and non-dominated sorting genetic algorithm III (NSGA-III), was employed to refine the impeller. Comparative analysis reveals that the NSGA-III design offers superior stability, achieving a 31.1% reduction in peak radial force and eliminating axial-force directional reversals, while simultaneously increasing efficiency by 5.4% and reducing shaft power by 6.3%. Crucially, the stabilization mechanism is decoded using multiresolution dynamic mode decomposition (MRDMD) and entropy generation analysis. MRDMD results demonstrate that the optimized geometry attenuates low-frequency unstable modes driven by rotor–stator interaction, shifting spectral energy to stable high-frequency structures. Furthermore, thermodynamic analysis identifies a fundamental shift in dissipation pathways: the suppression of large-scale coherent vortex shedding reduces internal volumetric entropy generation by 63.6%, yielding a flow field stabilized by controlled wall shear layers. These findings provide a physics-based rationale for mitigating flow instabilities through targeted geometric refinement.