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首页> 外文期刊>Energy Conversion & Management >Optimization of irreversibility and thermal characteristics of a mini heat exchanger operated with a new hybrid nanofluid containing carbon nanotubes decorated with magnetic nanoparticles
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Optimization of irreversibility and thermal characteristics of a mini heat exchanger operated with a new hybrid nanofluid containing carbon nanotubes decorated with magnetic nanoparticles

机译：优化了一种小型热交换器的不可逆性和热特性，该小型热交换器与一种新型杂化纳米流体一起运行，该杂化纳米流体包含装饰有磁性纳米颗粒的碳纳米管

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摘要

A numerical investigation is performed to evaluate the effect of nanoparticle volume concentration and Reynolds number on the heat transfer and entropy generation characteristics of a hybrid nanofluid containing tetramethylammonium hydroxide (TMAH) coated Fe3O4 (magnetite) nanoparticles and gum arabic (GA) coated carbon nanotubes (CNTs) flowing inside a counter-flow double-pipe heat exchanger. Variable thermophysical properties are employed such that thermal conductivity is considered dependent on temperature and concentration, while viscosity is dependent on temperature, concentration and shear rate. The results demonstrate that the overall heat transfer coefficient and the entropy generation rate augment with the increase of Reynolds number, CNT concentration and magnetite concentration. In addition to the assessment and analysis of the outcomes, models of the overall heat transfer coefficient and global total entropy generation are developed in terms of the Fe3O4 and CNT concentrations and Reynolds number using neural network. Then, genetic algorithm is utilized in combination with compromise programming in order to obtain the optimal cases with maximum heat transfer and minimum total entropy generation. To achieve the minimum total entropy generation along with the maximum heat transfer, applying the nanofluids with great nanoparticle concentrations alongside low Reynolds numbers is suggested.
机译：进行了数值研究，以评估纳米颗粒体积浓度和雷诺数对包含四甲基氢氧化铵（TMAH）涂覆的Fe3O4（磁铁矿）纳米颗粒和阿拉伯胶（GA）涂覆的碳纳米管的杂化纳米流体的传热和熵产生特性的影响（碳纳米管）在逆流双管换热器内流动。采用可变的热物理性质，使得热导率取决于温度和浓度，而粘度取决于温度，浓度和剪切速率。结果表明，随着雷诺数，碳纳米管浓度和磁铁矿浓度的增加，总传热系数和熵产生率增加。除了对结果进行评估和分析之外，还使用神经网络根据Fe3O4和CNT的浓度以及雷诺数建立了总传热系数和全局总熵产生的模型。然后，将遗传算法与折衷编程结合使用，以获得具有最大传热和最小总熵产生的最佳情况。为了实现最小的总熵产生以及最大的热传递，建议应用具有高纳米颗粒浓度的纳米流体以及低雷诺数。

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