Aerodynamics Optimization of Multi-Blade Centrifugal Fan Based on Extreme Learning Machine Surrogate Model and Particle Swarm Optimization Algorithm

被引：1
|
作者：
Meng, Fannian [1 ]
Wang, Liujie [1 ]
Ming, Wuyi [1 ,2 ]
Zhang, Hongxiang [1 ]
机构：
[1] Zhengzhou Univ Light Ind, Mech & Elect Engn Inst, Zhengzhou 450002, Peoples R China
[2] Guangdong HUST Ind Technol Res Inst, Guangdong Prov Key Lab Digital Mfg Equipment, Dongguan 523808, Peoples R China
来源：
METALS | 2023年 / 13卷 / 07期
关键词：
multi-blade centrifugal fan; ELM surrogate model; PSO algorithm; optimization; DESIGN OPTIMIZATION; CFD;
D O I：
10.3390/met13071222
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The centrifugal fan is widely used in converting mechanical energy to aerodynamic energy. To improve the pressure of the multi-blade centrifugal fan used in an air purifier, an optimization process was proposed based on extreme learning machine (ELM) combined with particle swarm optimization (PSO). The blade definition position parameter and blade definition radian parameter were designed using the full-factor simulation experimental method. The steady numerical simulation of each experimental point was carried out using ANSYS CFX software. The total pressure of the multi-blade centrifugal fan was selected as the optimization response. The optimized ELM combined with the PSO algorithm considering the total pressure response value and the two multi-blade centrifugal fan parameters were built. The PSO algorithm was used to optimize the approximation blade profile to obtain the optimum parameters of the multi-blade centrifugal fan. The total pressure was improved from 140.6 Pa to 151 Pa through simulation experiment design and improved surrogate optimization. The method used in the article is meant for improving multi-blade centrifugal total pressure. The coupling optimization of impellers, volutes, and air intakes should be comprehensively considered to further improve the performance of centrifugal fans.
引用
收藏
页数：14
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