首页> 外文期刊>Journal of Thermodynamics >Modified Lennard-Jones Potentials with a Reduced Temperature-Correction Parameter for Calculating Thermodynamic and Transport Properties: Noble Gases and Their Mixtures (He, Ne, Ar, Kr, and Xe)
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Modified Lennard-Jones Potentials with a Reduced Temperature-Correction Parameter for Calculating Thermodynamic and Transport Properties: Noble Gases and Their Mixtures (He, Ne, Ar, Kr, and Xe)

机译:具有降低的温度校正参数的修正Lennard-Jones势,用于计算热力学和输运性质:稀有气体及其混合物(He,Ne,Ar,Kr和Xe)

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The three-parameter Lennard-Jones(12-6)potential function is proposed to calculate thermodynamic property (second virial coefficient) and transport properties (viscosity, thermal conductivity, and diffusion coefficient) of noble gases (He, Ne, Ar, Kr, and Xe) and their mixtures at low density. Empirical modification is made by introducing a reduced temperature-correction parameterτto the Lennard-Jones potential function for this purpose. Potential parameters (σ,ε, andτ) are determined individually for each species when the second virial coefficient and viscosity data are fitted together within the experimental uncertainties. Calculated thermodynamic and transport properties are compared with experimental data by using a single set of parameters. The present study yields parameter sets that have more physical significance than those of second virial coefficient methods and is more discriminative than the existing transport property methods in most cases of pure gases and of gas mixtures. In particular, the proposed model is proved with better results than those of the two-parameter Lennard-Jones(12-6)potential, Kihara Potential with group contribution concepts, and other existing methods.
机译:提出了三参数Lennard-Jones(12-6)势函数来计算稀有气体(He,Ne,Ar,Kr,Cr)的热力学性质(第二维里系数)和传输性质(粘度,导热系数和扩散系数)和Xe)及其低密度混合物。为此,通过将减小的温度校正参数τ引入到Lennard-Jones势函数来进行经验修改。在实验不确定性范围内将第二维里系数和粘度数据拟合在一起时,分别确定每种物种的潜在参数(σ,ε和τ)。通过使用一组参数将计算的热力学和传输性质与实验数据进行比较。在大多数纯气体和气体混合物的情况下,本研究得出的参数集比第二维里系数方法具有更大的物理意义,并且比现有的传输特性方法更具判别力。尤其是,与两参数Lennard-Jones(12-6)势,具有基团贡献概念的Kihara势以及其他现有方法相比,所提出的模型具有更好的结果。

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