Multi-objective arrangement optimization of a tube bundle in cross-flow using CFD and genetic algorithm

Abstract

Recently, energy saving problem attracts increasingly attention from researchers. The present study is aimed at determining the optimal arrangement of a tube bundle consisting of four heat exchange tubes. The tube bundle with temperature of 353.15 K on the outer surface, is cooled by forced convection in a channel. The two-dimensional (2-D) axisymmetric model is solved by the finite element method. By determining four optimal coordinates of the discrete tubes, multi-objective genetic algorithm is employed to obtain the Pareto solutions, where the objective functions are the pressure drop Δp and the average heat flux q, respectively. A decision maker method, TOPSIS is subsequently applied to determine the best solution from Pareto front. Compared with the initial tube bundle, the optimal solution increases 53.51% in q and decreases 1.27% in Δp. Overall, the TOPSIS method could improve the heat transfer performance of the tube bundle without bringing a higher pressure drop. Besides, the Pareto-based multi-objective optimization technique is more comprehensive, compared with single-objective genetic algorithm using performance evaluation criteria (PEC) or the efficiency evaluation criterion (EEC) as evaluation criterion.