Optimization study of spherical tuyere based on BP neural network and new evaluation index

作     者：Mengchao Liu Ran Gao Yi Wang Angui Li Mengchao Liu;Ran Gao;Yi Wang;Angui Li

作者机构：School of Building Services Science and EngineeringXi’an University of Architecture and TechnologyXi’an710055China 

出 版 物：《Building Simulation》 (建筑模拟（英文）)

年 卷 期：2024年第17卷第2期

页      面：223-234页

核心收录：

学科分类：08[工学] 0807[工学-动力工程及工程热物理] 081404[工学-供热、供燃气、通风及空调工程] 0814[工学-土木工程] 

基　　金：This research project was sponsored by the National Natural Science Foundation of China General Program(No.52178090 and No.51878533) the Science and Technology New Star Project of Shaanxi Province(No.2020KJXX-013) 

主　　题：spherical tuyeres STEI CFD ventilation 

摘      要：The energy consumption of heating,ventilation,and air conditioning(HVAC)systems holds a significant position in building energy usage,accounting for about 65%of the total energy ***,with the advancement of building automation,the energy consumption of ventilation systems continues to *** study focuses on improving the performance of spherical tuyeres in HVAC *** primarily utilizes neural networks and multi-island genetic algorithms(MIGA)for multi-parameter *** employing methods such as structural parameterization,accurate and fast computational fluid dynamics(CFD)simulations,a minimized sample space,and a rational optimization strategy,the time cycle of the optimization process is ***,a new comprehensive evaluation index is proposed in this research to describe the performance of spherical tuyeres,which can be used to more accurately assess spherical tuyeres with different *** results show that by establishing a neural network prediction model and combining it with the multi-island genetic algorithm,a novel spherical tuyere design was successfully *** optimized novel spherical tuyeres achieved a 27.05%reduction in the spherical tuyeres effective index(STEI)compared to the traditional spherical ***,the resistance decreased by 15.68%,and the jet length increased by 7.57%.The experimental results demonstrate that our proposed optimization method exhibits high accuracy,good generalization capability,and excellent agreement at different Reynolds numbers.