Intelligent optimization design of shell and helically coiled tube heat exchanger based on genetic algorithm

Abstract

The intelligent optimization design of a helically coiled tube heat exchanger is proposed. The structural design, meshing, and numerical calculations are integrated into the genetic algorithm to perform intelligent optimization in selecting the structural and thermodynamic parameters. Compared with the experimental results, the heat flux and heat transfer rate of the heat exchanger with the optimal structure increase by 110% and 101%, respectively, which can be proved by the decrease of the average intersection angle between the velocity vector and temperature gradient on both the shell side and tube side. Considering the pressure drop constraint, the maximum heat flux increases by 12% compared with the value obtained from the optimization criterion when the total heat transfer rate is maximized, indicating its potentials in reducing the financial cost. Finally, this method provides an automatic solution to the optimization design of a diversity of heat exchangers.