The paper deals with the Lennard-Jones fluid and presents the mathematical model of computating thermodynamic functions of state in the liquid and gas domain by means of statistical thermodynamics. To calculate the thermodynamic properties of a real fluid, we used the Johnson-Zollweg-Gubbins model based on the modified Benedict-Webb-Rubin equation of state, the Chunxi-Yigui-Jiufang equation of state based on the simple perturbation theory, and the complex Tang-Tong-Lu model based on the solution of the Ornstein-Zernike equation obtained by means of the perturbation theory. The analytical results are compared with the thermodynamical data, and with the results obtained from classical thermodynamics.
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In various fields of contemporary research information and dynamics are becoming the key terms. Theoretic information reasoning is well known in physics, especially in thermodynamics where the relationship between the statistical (or, informational) entropy of the system and its thermodynamical entropy has been studied since a long time. Information theory is especially relevant to data processing and statistical inference. Generally speaking, the apparatus of information theory is applicable to any probabilistic system of observations since whenever we make statistical observations (or design and conduct statistical experiments) we seek information. When the language of information theory (the concepts of enropy, mutual information between random variables and processes, information rate, maximum entropy formalism, information flow etc.) is used in connection with system dynamics we come to the notion of information dynamics. The objective of this report is to show a potential of the basic information theoretic methodology for the analysis of various problems of system dynamics. In particular, we wish to indicate some challenges and expound our recent results on the maximum information entropy approach to the analysis of stochastic dynamical systems.
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