Optimization of heat transfer in shell-and-tube heat exchangers using MOGA algorithm: adding nanofluid and changing the tube arrangement

Abstract

The purpose of this study is to assess the impact of a wide variety of parameters to maximize the heat transfer rate using nanofluid, baffles, different Reynolds numbers (Re), different tube arrangements, and various geometry dimensions using the multi-objective genetic algorithm (MOGA) algorithm. The ANSYS FLUENT software, the SIMPLE algorithm as well as single-phase approach are employed for simulations. The study was performed for volume fractions (φ) of 0% to 4% and 10,000 < Re < 20,000. The results are presented for rectangular and triangular arrangements of tubes. It is demonstrated that in the rectangular configuration, the average Nusselt number (Nuave) is 34.38 when number of baffles (NB) of 10, φ = 4%, Re = 20,000. For the same values of φ and Re, when NB = 10, Nuave is enhanced by 7.4% and 10.4% compared to the cases in which NB = 6 and 8, respectively. However, for the triangular arrangement of tubes, Nuave=35.15. For the same values of φ and Re, when NB = 10, Nuave is enhanced by 5.7% and 11.4% compared to the cases in which NB = 6 and 8, respectively. Also, the triangular arrangement has about 2.1% more thermal efficiency than the rectangular one when NB, φ, and Re are maximum. Unlike the smaller figure for tubes mounted in the heat exchanger to transfer heat compared to other studies, the addition of nanofluid and using baffles lead to employing the heat exchanger for practical applications. However, a larger number of baffles causes a higher pressure drop. Hence, the optimization is performed using MOGA to reduce the pressure drop