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 (Nu_ave) 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, Nu_ave 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, Nu_ave=35.15. For the same values of φ and Re, when NB = 10, Nu_ave 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.

摘要

本研究的目的是评估各种参数的影响，以使用纳米流体、挡板、不同的雷诺数 ( Re )、不同的管布置和使用多目标遗传算法的各种几何尺寸最大化传热率( MOGA）算法。采用ANSYS FLUENT软件、SIMPLE算法和单相法进行仿真。该研究针对0% 至 4% 的体积分数 ( φ ) 和 10,000 < Re  < 20,000进行。结果显示为管的矩形和三角形排列。结果表明，在矩形配置中，_当挡板数( NB) 的 10, φ  = 4%, Re  = 20,000。对于相同的φ和Re值，当NB  = 10 时，与NB = 6 和 8的情况相比，Nu _ave分别增强了 7.4% 和 10.4%  。然而，对于管的三角形布置，Nu _ave =35.15。对于相同的φ和Re值，当NB  = 10 时，与NB 的情况相比，Nu _{ave增强了 5.7% 和 11.4%} = 6 和 8，分别。此外，当NB、φ和Re最大时，三角形布置的热效率比矩形布置高约 2.1% 。与其他研究相比，安装在热交换器中以传递热量的管子的较小数字不同，添加纳米流体和使用挡板导致将热交换器用于实际应用。然而，较大数量的挡板会导致较高的压降。因此，使用 MOGA 进行优化以减少压降。