Multi-Objective Parameters Optimization of Centrifugal Fan Based on GA-BP Neural Network and NSGA- II

In order to achieve the research objective of simultaneously improving the air volume and reducing the noise of centrifugal fans, a combination of orthogonal experimental design, BP neural network modelling and multi-objective genetic algorithm (NSGA- II) was used to find the optimal method, and the worm tongue placement angle φ, worm tongue radius R, expansion angle θ and outlet expansion section height L of the worm casing were selected as optimization variables. The air volume and noise of the centrifugal fan under the design working condition were calculated by non-constant and constant calculations, and the air volume and noise were used as the optimization objectives. The results demonstrate that, compared to the initial design, the optimized fan model achieved a noise reduction of 10.99 dB and an airflow increase of 1.76%. Furthermore, the amplitude of the pressure pulsation coefficient at the blade passing frequency was significantly reduced at the monitoring point near the volute tongue. This suggests a reduction in the intensity of dynamic and static interactions between the impeller and the volute tongue, thereby enhancing the operational stability of the fan. The proposed optimization method has certain reference significance for improving the aerodynamic performance of centrifugal fans.