Development of a Partial Clustering Model of Alloy Viscosity

The purpose of this paper is to obtain a partial clustering model of viscosity including the influence of clusters. This paper also establishes a quantitative correlation between the dynamic viscosity of alloys and temperature of liquidus in isotherms. The research methods are a theoretical substantiation of possibility of the isolated use of the Boltzmann distribution (energy spectrum) for the kinetic energy of the chaotic (thermal) motion and particle collisions as applied to a condensed state of matter. In this paper, the author’s concept of chaotic particles is applied to substantiate the existence of an energy class of particles present in the liquid in the form of clusters. The novelty of the paper is that it obtains a quantitative physical and mathematical model of temperature dependences of the dynamic viscosity based on destruction of clusters as the temperature increases. The mathematical model is compared with viscosity data from the state diagram, starting from the liquidus barrier. This approach was developed first and allows constructing viscosity isotherms based on the thermochemical initial data with extrapolation to the region of ultra-high temperatures. The proposed new model is verified in an example of a Cu-Sn alloy. The high correlation coefficient indicates the correctness of the derived equations and possibility of predicting the distribution of the viscosity of the alloy at high temperatures based on its state diagram. But the main fundamental novelty of the work is the discovery of the relationship between the activation energy of viscous flow and the barrier of randomization, which is present in the partial clustering model. The application of the new partial clustering viscosity model can be utilized across various fields involving fluid dynamics. In our study, the practical implementation of this novel partial clustering viscosity model will ensure the effective execution of metallurgical processes designed using these values at extremely high temperatures, determine optimal operating conditions, and provide more substantiated requirements for metal and alloy production technologies.