Purpose

The purpose of this study is to investigate thermal conductivity and dynamic viscosity of graphene nanoplatelet-based (GNP) nanolubricant.

Design/methodology/approach

Nanolubricants in concentrations of 0.025, 0.05, 0.1 and 0.5 Wt% were prepared by means of two-step method. The stability of nanolubricants was monitored by visual inspection and dynamic light scattering tests. Thermal conductivity and dynamic viscosity of nanolubricants in various temperatures between 25°C–70°C were measured with KD2-Pro analyser device and a rotational viscometer MRC VIS-8, respectively. A comparison between experimentally achieved results and those obtained from existing models was performed. New correlations were proposed and artificial neural network (ANN) model was used for predicting thermal conductivity and dynamic viscosity.

Findings

The designed nanolubricant showed good stability after at least 21 days. Thermal conductivity and dynamic viscosity increased with particles concentration. In addition, as the temperature increased, thermal conductivity increased but dynamic viscosity decreased. Compared to the base oil, maximum enhancements were achieved at 70°C with the concentration of 0.5 Wt.% for dynamic viscosity and at 55°C with the same concentration for thermal conductivity. Besides, ANN results showed better performance than proposed correlations.

Practical implications

This study outcomes will contribute to enhance thermophysical properties of conventional lubricating oils.

Originality/value

To the best of our knowledge, there is no paper related to experimental study, new correlations and modelling with ANN of thermal conductivity and dynamic viscosity of GNPs/SAE 5W40 nanolubricant in the available literature.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2020-0088/

中文翻译：

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GNPs / SAE 5W40纳米润滑剂的粘度和导热系数的实验分析和建模

目的

这项研究的目的是研究石墨烯纳米片基（GNP）纳米润滑剂的导热系数和动态粘度。

设计/方法/方法

通过两步法制备浓度为0.025、0.05、0.1和0.5Wt％的纳米润滑剂。通过目测和动态光散射测试来监测纳米润滑剂的稳定性。分别使用KD2-Pro分析仪设备和旋转粘度计MRC VIS-8测量了在25°C–70°C之间的各种温度下纳米润滑剂的热导率和动态粘度。将实验获得的结果与从现有模型获得的结果进行比较。提出了新的相关性，并使用人工神经网络（ANN）模型来预测导热系数和动态粘度。

发现

所设计的纳米润滑剂在至少21天后显示出良好的稳定性。导热系数和动态粘度随颗粒浓度而增加。另外，随着温度升高，热导率增加，但动态粘度降低。与基础油相比，在70°C时动态粘度为0.5 Wt。％时达到最大增强，在55°C时导热率相同时达到最大增强。此外，人工神经网络的结果显示出比建议的相关性更好的性能。

实际影响

这项研究成果将有助于提高常规润滑油的热物理性能。

创意/价值

据我们所知，现有文献中没有与GNPs / SAE 5W40纳米润滑剂的导热性和动态粘度的实验研究，新的相关性和与ANN相关的模型。

同行评审

本文的同行评审历史记录可在以下网址获得：https://publons.com/publon/10.1108/ILT-03-2020-0088/