Kanti Praveen Kumar, Paramasivam Prabhu, Wanatasanappan V Vicki, Dhanasekaran Seshathiri, Sharma Prabhakar
Institute of Power Engineering, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, Selangor, 43000, Malaysia.
University Centre for Research & Development (UCRD), Chandigarh University, Mohali, 140413, Punjab, India.
Sci Rep. 2024 Dec 28;14(1):30967. doi: 10.1038/s41598-024-81955-1.
This study explores the thermal conductivity and viscosity of water-based nanofluids containing silicon dioxide, graphene oxide, titanium dioxide, and their hybrids across various concentrations (0 to 1 vol%) and temperatures (30 to 60 °C). The nanofluids, characterized using multiple methods, exhibited increased viscosity and thermal conductivity compared to water, with hybrid nanofluids showing superior performance. Graphene oxide nanofluids displayed the highest thermal conductivity and viscosity ratios, with increases of 52% and 177% at 60 °C and 30 °C, respectively, for a concentration of 1 vol% compared to base fluid. Similarly, graphene oxide-TiO hybrid nanofluids achieved thermal conductivity and viscosity ratios exceeding 43% and 144% compared to the base fluid at similar conditions. This data highlights the significance of nanofluid concentration in influencing thermal conductivity, while temperature was found to have a more pronounced effect on viscosity. To tackle the challenge of modeling the thermophysical properties of these hybrid nanofluids, advanced machine learning models were applied. The Random Forest (RF) model outperformed others (Gradient Boosting and Decision Tree) in both the cases of thermal conductivity and viscosity with greater adaptability to handle fresh data during model testing. Further analysis using shapely additive explanations based on cooperative game theory revealed that relative to temperature, nanofluid concentration contributes more to the predictions of the thermal conductivity ratio model. However, the effect of nanofluid concentration was more dominant in the case of viscosity ratio model.
本研究探讨了含有二氧化硅、氧化石墨烯、二氧化钛及其混合物的水基纳米流体在不同浓度（0至1体积%）和温度（30至60°C）下的热导率和粘度。通过多种方法表征的纳米流体与水相比，粘度和热导率有所增加，其中混合纳米流体表现出更优异的性能。氧化石墨烯纳米流体在60°C和30°C下，对于1体积%的浓度，相对于基础流体，热导率和粘度的增加率分别高达52%和177%，显示出最高的热导率和粘度比。同样，在类似条件下，氧化石墨烯 - 二氧化钛混合纳米流体相对于基础流体的热导率和粘度比分别超过43%和144%。这些数据突出了纳米流体浓度对热导率的影响，而温度对粘度的影响更为显著。为应对模拟这些混合纳米流体热物理性质的挑战，应用了先进的机器学习模型。在热导率和粘度两种情况下，随机森林（RF）模型均优于其他模型（梯度提升和决策树），在模型测试期间对处理新数据具有更强的适应性。基于合作博弈论使用Shapely加法解释进行的进一步分析表明，相对于温度，纳米流体浓度对热导率比模型的预测贡献更大。然而，在粘度比模型中，纳米流体浓度的影响更为显著。
