Abstract In this article, the rheological behavior of MWCNT/SiO2 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−1 respectively. A new regression model is being proposed to predict the dynamic viscosity of nanolubricants. The proposed regression model (R2 0.98338–0.99583) predicts the viscosity of nanolubricants closer to experimental results (least deviation 2.62%). Consistency index (m) and power law index (n) 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 R2.

中文翻译：

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使用回归和人工神经网络分析MWCNT/SiO2液压油纳米润滑剂的流变特性

摘要 在本文中，使用实验和人工神经网络 (ANN) 方法评估了基于 MWCNT/SiO2 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-1 的温度和剪切速率范围内测量。正在提出一种新的回归模型来预测纳米润滑剂的动态粘度。建议的回归模型 (R2 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 (m) 和幂律指数 (n) 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 ANN 模型，其中输入变量包括温度、固体体积分数和剪切速率。在第一阶段，温度和固体体积分数被作为输入变量，在第二阶段，剪切速率被引入作为额外的输入参数。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。就均方根误差 (RMSE) 和 R2 的平均值而言，人工神经网络的测试结果显示出比建议的相关性更好的准确性。