Entropy Scaling for Viscosity of Pure Lennard-Jones Fluids and Their Binary Mixtures

The-Cang Nguyen, Samy Khennache, G. Galliéro, Tuong Tran, Luc Tuong, Phuc Nguyen, H. Hoang, Hieu Khac Ho
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引用次数: 2

Abstract

In this work, entropy scaling approaches for viscosity of pure Lennard-Jones (LJ) fluids and their mixtures have been investigated. To do so, we have employed reliable viscosity database available in literature for the pure LJ fluids, and performed the molecular dynamics simulation to generate viscosity database over a wide range of thermodynamic condition for the LJ mixture fluids. It has shown that for the pure LJ fluid, the entropy scaling approaches using the macroscopic properties for the reduction of viscosity yield noticeably better collapse of data than the one using the zero-density viscosity in the dense fluid region. In addition, we have developed viscosity correlations based on these approaches. It has been obtained that the correlations of macroscopic properties approaches predict the pure LJ fluid viscosity with average absolute deviation of around 4% more coming from the low-density states, whereas it is of around 8.50% more coming from the dense states for the other one. Finally, the viscosity correlations have been applied to the LJ mixtures. Interestingly, the correlations of macroscopic properties approaches are able to provide good estimations for all mixtures studied. However, it deteriorates for the viscosity of dense mixtures when the other employed.
纯Lennard-Jones流体及其二元混合物粘度的熵标度
本文研究了纯Lennard-Jones (LJ)流体及其混合物粘度的熵标度方法。为此,我们采用文献中可靠的纯LJ流体粘度数据库,并进行分子动力学模拟,生成LJ混合流体在大范围热力学条件下的粘度数据库。结果表明,对于纯LJ流体,在致密流体区域,使用宏观性质来降低粘度的熵标度方法比使用零密度粘度的熵标度方法产生明显更好的数据崩塌。此外,我们还在这些方法的基础上建立了粘度相关性。结果表明,宏观性质相关性方法预测纯LJ流体粘度时,低密度状态下的平均绝对偏差约为4%,而致密状态下的平均绝对偏差约为8.50%。最后,将黏度关系式应用于LJ混合物。有趣的是,宏观性质的相关性方法能够为所研究的所有混合物提供良好的估计。然而,当使用另一种方法时,它的粘度会下降。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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