Abstract
Molecular dynamics simulations are performed for several monatomic metals and Fe0.9Ni0.1 metallic alloy to study the transport properties and entropy of liquids along melting curve. Our results show that the self-diffusion coefficients and viscosity of liquids increase with increasing pressure along the melting curves. Analysis suggests that, at high pressure conditions, the pair correlation entropy S2 of liquids along melting curve is bout −3.71kB, independent of the pressure and variety of liquids, which indicates that there is no obvious change in liquid structure along the melting curve. The Rosenfeld entropy-scaling laws with S2 = −3.71kB and the special values of scaling parameters can give reasonable estimates for the self-diffusion coefficients and viscosity of liquid metals along melting curves. The effect of pressure on transport coefficients can be quantified through its corresponding effect on the melting temperature and number density, and this result is in consistent with the Andrade’s model. In addition, the variation of S2 provides a useful, experimentally accessible, structure-based criterion for freezing of liquid metals.
Highlights
Molecular dynamics simulations are performed for several monatomic metals and Fe0.9Ni0.1 metallic alloy to study the transport properties and entropy of liquids along melting curve
Our results show that the self-diffusion coefficients and viscosity of liquids increase with increasing pressure along the melting curves
The Rosenfeld entropy-scaling laws with. Transport coefficients, such as self-diffusion coefficient and viscosity, of liquid metals are of immense importance for understanding thermophysical behaviors of liquids, and for studying their flow behavior in practical engineering applications.[1,2]
Summary
Molecular dynamics simulations are performed for several monatomic metals and Fe0.9Ni0.1 metallic alloy to study the transport properties and entropy of liquids along melting curve. Entropy and transport properties of liquid metals along the melting curve Qi-Long Cao,1,2,a Pan-Pan Wang,[2] Ju-Xiang Shao,[1,2] and Fan-Hou Wang1 1Key Laboratory of Computational Physics, Yibin University, Yibin 644007, China 2School of Physics and Electronic Engineering, Yibin University, Yibin 644007, China (Received 7 December 2016; accepted 5 February 2017; published online 24 February 2017)
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