Modeling and optimization of a multi tube heat exchanger (MTHE) network considering the effects of different nanoparticles on the tube side are carried out using Fast and elitist non-dominated sorting genetic algorithm. After thermal modeling in $\epsilon -\mathit{NTU}$ method, optimization is performed by increasing the effectiveness and decreasing total annual cost as two objective functions using eight design parameters such as number of MTHE and particles volumetric concentration. In addition, optimization is performed at three various cold mass flow rates and different nanoparticles including Al₂O₃, CuO and ZrO₂ and results are compared with the base fluid (water). For the reliability of the present code, the modeling results are validated with the results obtained from both the numerical and experimental model. The results show that the optimal Pareto front is improved in nanoparticles case, and the rate of improvement in CuO nanoparticles case, especially in higher effectiveness and lower cold mass flow rate is more significant compared with the other studied cases. In addition, because of improvement in the thermal performance of MTHE network with nanoparticles, the heat transfer surface area and consequently the total volume of MTHE network for the fixed values of effectiveness are noticeably reduced. Finally, the effects of design parameters versus effectiveness are demonstrated and discussed.

使用快速和精英非支配排序遗传算法，进行了考虑不同纳米颗粒在管侧影响的多管热交换器（MTHE）网络的建模和优化。在进行热建模之后$\epsilon -\mathit{南大}$这种方法通过使用MTHE数量和颗粒体积浓度等八个设计参数，通过提高有效性和降低年度总成本作为两个目标函数来进行优化。此外，优化是在三种不同的冷质量流量和包括Al ₂ O ₃，CuO和ZrO _2的不同纳米颗粒下进行的并将结果与​​基础流体（水）进行比较。为了本代码的可靠性，使用从数值模型和实验模型获得的结果来验证建模结果。结果表明，与其他研究案例相比，在纳米粒子情况下，最佳帕累托前沿得到了改善，而在CuO纳米粒子情况下，尤其是在更高的效率和更低的冷质量流量下，改善的速率更为显着。另外，由于改善了具有纳米颗粒的MTHE网络的热性能，因此显着降低了固定有效性值的传热表面积和MTHE网络的总体积。最后，论证和讨论了设计参数对有效性的影响。