The emerging applications of nanofluids in heat transfer makes it imperative to study their viscous properties. The knowledge and assessment of physical properties with changes in concentration and temperature are essential for the practical applications of nanofluids. The first part of the current study is the synthesis of Titania (TiO) nanotubes via a conventional method. The experimental investigation of viscosity behavior of TiO nanotubes dispersed in ethylene glycol/water-based nanofluid by different process parameters such as the mass concentration of nanotubes (0 to 1%), temperature (25–65 °C) and shear rate (150–500 s). The results showed a 30% increase in viscosity at 55 °C by increasing the mass concentration of nanotubes from 0 to 1%, while 22% increase was observed at 25 °C. In the second part, a multivariable correlation, and Artificial Neural Network (ANN) have been used to predict the viscosity at varying temperatures and shear rates based on the experimental data. Statistical analyses were done to investigate the accuracy of both empirical correlation and ANN modeling. It was observed from the results that ANN prediction is highly accurate, with 0.1981 AAD% and 0.999 R as compared to empirical correlations (2.68 AAD%, 0.9872 R).

中文翻译：

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乙二醇/水基纳米流体中二氧化钛纳米管分散体的动态粘度：经验相关性和人工神经网络的实验评估和预测

纳米流体在传热中的新兴应用使得研究它们的粘性势在必行。随着浓度和温度变化的物理特性的知识和评估对于纳米流体的实际应用至关重要。当前研究的第一部分是通过常规方法合成二氧化钛 (TiO) 纳米管。通过纳米管的质量浓度（0-1%）、温度（25-65°C）和剪切速率（150-）等不同工艺参数对分散在乙二醇/水基纳米流体中的二氧化钛纳米管的粘度行为进行实验研究500 秒）。结果表明，通过将纳米管的质量浓度从 0% 增加到 1%，在 55 °C 时粘度增加了 30%，而在 25 °C 时观察到了 22% 的增加。在第二部分，多变量相关性，和人工神经网络 (ANN) 已被用于根据实验数据预测不同温度和剪切速率下的粘度。进行了统计分析以研究经验相关性和人工神经网络建模的准确性。从结果中可以看出，ANN 预测是高度准确的，与经验相关性（2.68 AAD%，0.9872 R）相比，具有 0.1981 AAD% 和 0.999 R。