The density of nanofluids is an important thermophysical property whose value is required to evaluate various heat-transfer parameters such as the Reynolds number, the Nusselt number, pressure loss, and the Darcy friction factor. The determination of these parameters is central to the design of many heat-transfer applications. Notably, the density of nanofluids has received relatively little research attention compared with other thermophysical properties. The present study thus focuses on the development of a support vector regression model to estimate the densities of aluminum nitride, titanium nitride, and silicon nitride nanoparticles dispersed in ethylene glycol solution. As inputs, the proposed model uses the mass fraction, temperature, nanoparticle size, and the molecular weight of the nanoparticles. The proposed model predicts the nanofluid densities with high accuracy, as determined by a correlation coefficient of 99.87% and an absolute average relative deviation of 0.0701. To further highlight the accuracy of the proposed model, we compare its results with those of the model of Pak and Cho. The Pak and Cho results deviate considerably from the experimental data except at 298 K. Overall, the proposed support vector regression model is much more accurate than the Pak and Cho model. We thus conclude that the machine learning approach is more reliable for obtaining rapid estimates of the density of nanofluids.

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一种基于机器学习的模型，用于估计乙二醇中氮化物纳米流体的密度

纳米流体的密度是一个重要的热物理特性，需要其值来评估各种传热参数，例如雷诺数、努塞尔数、压力损失和达西摩擦因数。这些参数的确定是许多传热应用设计的核心。值得注意的是，与其他热物理特性相比，纳米流体的密度受到的研究关注相对较少。因此，本研究的重点是开发支持向量回归模型，以估计分散在乙二醇溶液中的氮化铝、氮化钛和氮化硅纳米粒子的密度。作为输入，所提出的模型使用质量分数、温度、纳米颗粒尺寸和纳米颗粒的分子量。所提出的模型以高精度预测纳米流体密度，相关系数为 99.87%，绝对平均相对偏差为 0.0701。为了进一步突出所提出模型的准确性，我们将其结果与 Pak 和 Cho 模型的结果进行了比较。除了 298 K 之外，Pak 和 Cho 的结果与实验数据有很大差异。总的来说，所提出的支持向量回归模型比 Pak 和 Cho 模型准确得多。因此我们得出结论，机器学习方法对于获得纳米流体密度的快速估计更可靠。我们将其结果与 Pak 和 Cho 模型的结果进行比较。除了 298 K 之外，Pak 和 Cho 的结果与实验数据有很大差异。总的来说，所提出的支持向量回归模型比 Pak 和 Cho 模型准确得多。因此我们得出结论，机器学习方法对于获得纳米流体密度的快速估计更可靠。我们将其结果与 Pak 和 Cho 模型的结果进行比较。除了 298 K 之外，Pak 和 Cho 的结果与实验数据有很大差异。总体而言，所提出的支持向量回归模型比 Pak 和 Cho 模型准确得多。因此我们得出结论，机器学习方法对于获得纳米流体密度的快速估计更可靠。