Abstract

The thermal conductivity of working fluids has been dramatically improved by the implementation of nanoparticles. In this experimental study, the influence of TiO₂ and SiO₂ nanoparticles on the pool boiling heat transfer coefficient (HTC) of the nanofluid is thoroughly investigated. The results indicate that HTC of hybrid nanofluid of TiO₂–SiO₂–water is considerably higher than that of single nanofluids TiO₂–water and SiO₂–water systems. The effects of the nanofluid concentration and surface modification on HTC were investigated for two main working fluids of water and a mixture of ethylene glycol (EG)-water. Experimental evidence shows that the highest values of heat flux and HTC are obtained at 0.05% concentration of the hybrid nanofluid. Furthermore, the results show that changing the plain surface to the surface with circular channels and intersection lines (CC-IL) leads to considerable enhancement in HTC for all nanofluids. Two intelligent methods of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) have also been developed for the prediction of the experimental HTCs. The results indicate the prediction precision of the ANN model is higher than that of ANFIS. The RMSE and AARD of the ANN model are 1.05 \( \frac{\text{KW}}{{{\text{m}}^{2} {\text{C}}}} \) and 3.02%, respectively.

Graphic abstract

中文翻译：

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池沸腾过程中改性表面上TiO 2 / SiO 2杂化纳米流体传热增强的实验与模型研究

摘要

通过采用纳米颗粒，工作流体的导热系数得到了显着提高。在这项实验研究中，彻底研究了TiO ₂和SiO ₂纳米颗粒对纳米流体池沸腾传热系数（HTC）的影响。结果表明，TiO ₂ -SiO _2-水混合纳米流体的HTC明显高于单一TiO _2-水和SiO ₂纳米流体的HTC。–水系统。研究了水和乙二醇（EG）-水的两种主要工作流体的纳米流体浓度和表面改性对HTC的影响。实验证据表明，当杂化纳米流体的浓度为0.05％时，热通量和HTC最高。此外，结果表明，使用圆形通道和相交线（CC-IL）将平整表面更改为表面，会导致所有纳米流体的HTC显着提高。人工神经网络（ANN）和自适应神经模糊推理系统（ANFIS）的两种智能方法也已被开发出来，用于预测实验性HTC。结果表明，ANN模型的预测精度高于ANFIS。ANN模型的RMSE和AARD为1.05 \（\ frac {\ text {KW}} {{{\ text {m}} ^ {2} {\ text {C}}}} \）和3.02％。

图形摘要