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Optimization of multi-blade centrifugal fan based on WPSO-BP and L-MBWO

Published:2024-10-30 author:XU Ren, LI Junyu, ZHOU Ming, et al. Browse: 152 Check PDF documents

Optimization of multi-blade centrifugal fan based on

WPSO-BP and L-MBWO

XU Ren1,2, LI Junyu1,2, ZHOU Ming2,3, LIU Linbo2,3, ZHANG Zhifu4, HUANG Qibai1,2

(1.School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;

2.ValeoHuazhong University of Science and Technology Joint Laboratory of Vibration and Noise, Wuhan 430074, China;

3.Valeo Automotive Air Conditioning Hubei Co., Ltd., Jingzhou 430074, China; 4.School of Mechanical and Electrical

Engineering, Hainan University, Haikou 570228, China)

Abstract: Aiming at the problem that it is difficult to improve the aerodynamic and noise performance of multi-blade centrifugal fans simultaneously, a back-propagation neural network fan performance prediction model based on variable weight particle swarm optimization algorithm (WPSO-BP), and a multi-objective beluga whale optimization algorithm based on logistic chaos initialization (L-MBWO) were proposed, and they were applied to the optimization design of multi-blade centrifugal fan. Firstly, the inlet and outlet angles of the blades, the maximum inclined tongue radius, and the blade cutting angle were selected as design variables, with the total pressure, efficiency, and sound pressure level of the fan as optimization objectives. Then, the WPSO-BP prediction model was constructed to reflect the relationship between design variables and optimization objectives. Quantitative analysis was used to compare the reliability of the WPSO-BP prediction model and the BP neural network prediction model, it indicated that the predicted values could be used to optimize the performance of the fan. Subsequently, the logistic chaos initialization was introduced into the beluga whale optimization (BWO), and L-MBWO optimization algorithm was constructed based on the third-generation non-dominated sorting genetic algorithm (NSGA-III). Finally, the proposed prediction model and optimization algorithm were applied to the optimization of the fan under the premise of experimentally verifying the reliability of the simulation, and the optimization effect was comprehensively analyzed. The research results show that the total pressure of the optimized fan is increased by 34.79 Pa, the efficiency is increased by 0.67%, and the noise is decreased by 1.73 dB, achieving a balance between multiple optimization objectives, effectively improving the comprehensive performance of the fan, and providing a new idea for the optimization design of multi-blade centrifugal fans.

Key words: multi-blade centrifugal fan; variable weight; back-propagation neural network fan performance prediction model based on variable weight particle swarm optimization algorithm (WPSO-BP); beluga whale optimization (BWO); multi-objective beluga whale optimization algorithm based on logistic chaos initialization (L-MBWO); prediction model; fan total pressure; fan efficiency; fan noise

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