Background. When designing and creating technical facilities, in particular components of aviation systems, the question arises of the reliability and quality of their operation. Ways to improve the reliability and quality of operation of systems are: diagnostics and forecasting, as well as the management of these objects. To solve the described problems, a mathematical model of the studied technical object is required, which the author obtains from the analysis of physical and chemical processes by the potential-streaming method and from the results of testing these systems. The complexity of the described actions necessitates their software implementation, generally distributed (if the dimension of the system is large). The aim of this work is to develop methods for the software implementation of the construction of mathematical models of technical objects from potentialstreaming equations of processes in them and the results of testing these objects.
Materials and methods. The potential- streaming method was implemented by the author using model-oriented design (modelica standard). The software implements the calculation of various dynamics of physicochemical processes at various parameters of potential-streaming equations with further approximation on the set of these possible dynamics of the formal model in their given class. Experimental studies of the formal parameters of these models with the further construction of models (from formal models) are carried out by statistical methods.
Results. Based on the described methods, the present paper presents the structure of the software implementation of the methods for constructing mathematical models of systems from the analysis of physical and chemical processes in them and the test results of these systems.
Conclusions. The results of the work make it possible to create a hardware-software complex that allows one to build formal mathematical models from a user- defined structure of physicochemical processes in a technical object, and then build mathematical models of these systems from available experimental data (in particular, collected by this complex). Because in the general case, this complex is a horizontally scalable computing cluster, this makes it possible to build the described models of systems of arbitrary complexity.
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