| As widely utilized in industrial refrigeration,air conditioning and other fields,spiral fin-and-tube heat exchanger is a high-efficiency heat exchanger.By optimizing its structural parameters,it can maximize its heat transfer efficiency and reduce Energy consumption,reduce environmental pollution.The traditional structural optimization of tube heat exchangers is mainly focused on straight fins and louvered fins,and most of the research is based on physical experiments,which is expensive and takes a long time.Therefore,this thesis conducts heat dissipation simulation experiments to analyze the heat transfer performance and resistance performance of spiral finned tube(SFT),and uses the multi-objective optimization algorithm to optimize the design of the structural parameters of SFT.First of all,this thesis parametrically model SFT using SolidWorks,divide the finite element grid using Ansys-Meshing,and carry out heat dissipation simulation experiments using Fluent.These experiments focuses on the influence of the structural parameters of SFT and the wind speed on its performance of heat transfer and pressure loss.The experimental results indicates that the heat transfer capacity of SFT increase as the flow velocity increases.However,as the air flow velocity increases,the pressure drop of air also increases.Secondly,the multiple linear regression model between the structural parameters of SFT and characteristic length(De),the BP neural network regression model among the parameters of SFT characteristic length(De),the Nusselt coefficient(Nu),the pressure drop(ΔP),and the velocity of wind at the minimum section(vmax)are established to calculate the j factor and f factor,which are the evaluation indexes of heat transfer performance resistance performance.On the basis of that,the genetic algorithm(GA)and the non-dominated algorithm(NSGA-Ⅱ)with an elite strategy are used to optimize the structural parameters of SFT,so that SFT has better heat transfer performance and lower resistance.The optimization results show that the optimization effect of the NSGA-Ⅱ algorithm is better than that of the GA algorithm.Finally,SFT is modeled according to the optimized structural parameters and numerical simulation experiment is carried out.The experimental result shows that the j factor increased by 6.2%and the resistance f factor decreased by 46.8%compared with before optimization.The comprehensive performance evaluation index JF increased by 99.58%compared with before.The structural optimization method which the thesis proposes for SFT heat exchangers using NSGA-Ⅱ combined with BP neural network provides a new idea for the optimal design of spiral finned tube heat exchangers. |
