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Various methods of operation of complex transport systems imply knowledge of mathematical models of their components. To obtain adequate models of such components, it is necessary to take into account the physical and chemical processes occurring in them. Previously, the authors developed a potential-flow method within the framework of modern nonequilibrium thermodynamics – a unified approach to the analysis and modeling of processes of various physical and chemical nature. In accordance with this approach, as well as with the methods of mechanics, the theory of electric and magnetic circuits, electrodynamics, etc., state functions for the properties of substances and the processes under consideration are set up to the experimentally studied constant coefficients. The system of equations of the considered processes dynamics is obtained from the given state functions. The desired model (digital portrait) of the considered component is constructed by numerical-analytical transformation of the dynamic equations system based on the use of experimental data. The need to automate the proposed method of obtaining digital portraits is due to its complexity and the need to process a large amount of data. An information and computing system is proposed, which implies the construction of a block diagram of the processes in the component under consideration (model-oriented approach). Modeling these processes using a block diagram at different values of unknown parameters allows us to approximate the model (digital portrait) a component based on the resulting set of output characteristic dynamics using machine learning libraries. Process modeling and further approximation of the model is parallelized. This paper is devoted to a distributed information-computing system that implements the creation of various complex systems digital portraits.

Distributed computing system for creating digital portraits of complex systems