The flow field inside the heat exchangers is associated with maximum heat transfer and minimum pressure drop. Designing a heat exchanger and employing various techniques to enhance its overall performance has been widely investigated and is still an active research. The application of elliptic tube is an effective alternative to circular tube which can reduce the pressure drop significantly. In this study, numerical simulation and optimization of variable tube ellipticity is studied. The three-dimensional numerical analysis and a multi-objective genetic algorithm (MOGA) with surrogate modeling are performed. Tubes in staggered arrangement in fin-and-tube heat exchanger are investigated for combination of various elliptic ratios and Reynolds numbers. Results show that increasing elliptic ratio increases the friction factor due to increased flow blocking area, however, the effect on the Colburn factor is not significant. Moreover, tube with lower elliptic ratio followed by higher elliptic ratio tube has better thermal-hydraulic performance. To achieve the best overall performance, the Pareto optimal strategy is adopted for which the computational fluid dynamics (CFD) results, artificial neural network (ANN), and MOGA are combined. The tubes elliptic ratio and Reynolds number are the design variables. The objective functions include Colburn factor (j) and friction factor (f). The CFD results are input into ANN model. Once the ANN is computed, it is then used to estimate the model responses as a function of inputs. The final trained ANN is used to drive the MOGA to obtain the Pareto optimal solution. The optimal values of these parameters are finally presented.

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