In this article, the rheological behavior of MWCNT/SiO based nano-hydraulic oil nanolubricant is evaluated using experimental and Artificial Neural Network (ANN) approach. Viscosities of the hybrid nanolubricant samples were measured at temperature and shear rate range of 10–80 °C and 10–200 s respectively. A new regression model is being proposed to predict the dynamic viscosity of nanolubricants. The proposed regression model (R 0.98338–0.99583) predicts the viscosity of nanolubricants closer to experimental results (least deviation 2.62%). Consistency index () and power law index () values reveal that nanolubricant samples are non-Newtonian fluid with shear thinning behavior. To improve the accuracy in predicting the viscosity of nanolubricants, the ANN model was designed having input variables among temperature, solid volume fraction and shear rate. In the first phase, temperature and solid volume fraction were taken as input variables, and in the second phase shear rate was introduced as an additional input parameter. The entire data was split into 70:30 proportions for the training and testing phases of the ANN model. The testing results of ANN revealed better accuracy than the proposed correlation in terms of average values of Root Mean Square Error (RMSE) and R.

中文翻译：

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利用回归和人工神经网络分析MWCNT/SiO2液压油纳米润滑油的流变特性


在本文中，使用实验和人工神经网络（ANN）方法评估了基于MWCNT/SiO的纳米液压油纳米润滑剂的流变行为。混合纳米润滑剂样品的粘度分别在 10-80 °C 和 10-200 s 的温度和剪切速率范围内测量。提出了一种新的回归模型来预测纳米润滑剂的动态粘度。所提出的回归模型（R 0.98338–0.99583）预测纳米润滑剂的粘度更接近实验结果（最小偏差2.62％）。稠度指数 () 和幂律指数 () 值表明纳米润滑剂样品是具有剪切稀化行为的非牛顿流体。为了提高预测纳米润滑剂粘度的准确性，设计了具有温度、固体体积分数和剪切速率输入变量的人工神经网络模型。在第一阶段，将温度和固体体积分数作为输入变量，在第二阶段引入剪切速率作为附加输入参数。整个数据被分成 70:30 的比例，用于 ANN 模型的训练和测试阶段。 ANN 的测试结果显示，在均方根误差 (RMSE) 和 R 的平均值方面，ANN 的精度优于所提出的相关性。