Modelling and optimization of thermal conductivity for MWCNT-SiO2(20:80)/hydraulic oil-based hybrid nanolubricants using ANN and RSM

IF 3 3区 工程技术 Q2 CHEMISTRY, ANALYTICAL
Abhisek Haldar, Sankhadeep Chatterjee, Ankit Kotia, Niranjan Kumar, Subrata Kumar Ghosh
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Abstract

This research article presents the experimental evaluation of thermal conductivity for hydraulic oil-based hybrid nanolubricants with an aim to enhance the heat transfer potential in engineering applications. The nanolubricant samples were formulated at concentrations ranging from 0.3 to 1.8%. Using transient hot wire method, the thermal conductivity of nanolubricants were evaluated for all the samples from 30 to 80 °C. The maximum enhancement in thermal conductivity was 62.93% for the highest concentration. In this paper, response surface methodology (RSM) and artificial neural network (ANN) have been employed for prediction of the thermal conductivity of nanolubricants. In RSM, analysis of variance (ANOVA) and 3D surface plot techniques were used to determine the significance of the interaction parameters on the output. A new correlation has been proposed to predict the thermal conductivity of the nanolubricants with a R2 value of 0.9992. A combination of concentration and temperature (1.5783 vol% and 72.5695 °C) yielded to the maximum optimal thermal conductivity of 0.204526 Wm−1 K−1. In addition, multilayer perceptron, a type of neural network model, has been trained and tested to predict the thermal conductivity of the nanolubricants. Experiments have revealed that the ANN model consisting of only 10 hidden neurons has been able to achieve an average R2 of 0.98567 and RMSE of 0.02463 thereby establishing its ingenuity. Comparatively, it turned out that the RSM model was slightly more accurate in predicting thermal conductivity than the ANN model.

Abstract Image

基于ANN和RSM的MWCNT-SiO2(20:80)/液压油基混合纳米润滑剂导热系数建模与优化
本文对液压油基混合纳米润滑剂的导热性能进行了实验评估,以期提高其在工程应用中的传热潜力。配制的纳米润滑剂样品的浓度范围为0.3至1.8%。采用瞬态热丝法,在30 ~ 80℃范围内对纳米润滑剂的导热性能进行了评价。当浓度最高时,热导率的最大增强率为62.93%。本文采用响应面法(RSM)和人工神经网络(ANN)对纳米润滑剂的导热系数进行了预测。在RSM中,使用方差分析(ANOVA)和3D曲面技术来确定交互参数对输出的显著性。提出了一种新的预测纳米润滑剂导热系数的相关系数,其R2值为0.9992。浓度和温度(1.5783 vol%和72.5695°C)的组合产生的最大最佳导热系数为0.204526 Wm−1 K−1。此外,多层感知器,一种神经网络模型,已经被训练和测试,以预测纳米润滑剂的导热性。实验表明,仅由10个隐藏神经元组成的人工神经网络模型能够实现平均R2为0.98567,RMSE为0.02463,从而建立了它的独创性。相比之下,RSM模型在预测导热系数方面的准确性略高于人工神经网络模型。
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来源期刊
CiteScore
8.50
自引率
9.10%
发文量
577
审稿时长
3.8 months
期刊介绍: Journal of Thermal Analysis and Calorimetry is a fully peer reviewed journal publishing high quality papers covering all aspects of thermal analysis, calorimetry, and experimental thermodynamics. The journal publishes regular and special issues in twelve issues every year. The following types of papers are published: Original Research Papers, Short Communications, Reviews, Modern Instruments, Events and Book reviews. The subjects covered are: thermogravimetry, derivative thermogravimetry, differential thermal analysis, thermodilatometry, differential scanning calorimetry of all types, non-scanning calorimetry of all types, thermometry, evolved gas analysis, thermomechanical analysis, emanation thermal analysis, thermal conductivity, multiple techniques, and miscellaneous thermal methods (including the combination of the thermal method with various instrumental techniques), theory and instrumentation for thermal analysis and calorimetry.
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