Simulation Models for Solving Multiscale Combustion Problems
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computer simulation
multiphase media
neural networks

How to Cite

Smirnov N.N., Tyurenkova V.V., Nikitin V.F. Simulation Models for Solving Multiscale Combustion Problems // Russian Journal of Cybernetics. 2021. Vol. 2, № 4. P. 30-41. DOI: 10.51790/2712-9942-2021-2-4-3.


The development of algorithms and software for analyzing multiscale combustion processes is a relevant field of fundamental research that combines the methods of information technologies, mechanics of multicomponent continua, combustion chemistry, and simulation. It gains relevance year to year due to the intensive development of computational methods and models, and with the increase in supercomputing performance.

The applications of the proposed computational models and methods include energy, engine manufacturing, explosion and fire safety fields, as well as thermochemical mineral recovery stimulation methods.

The key simulation problems are a. the problem is multiscale: all the processes involved cannot be simulated with the same grid, even a scalable one; b. the rigidity and large dimensionality of the system of differential equations that describes chemical kinetics. Its solution may take up to 80 % of the processor time. This paper is an overview of the research conducted at the Scientific Research Institute for System Analysis and an analysis of the difficulties faced by the researchers. It also proposes new ways for overcoming the computational difficulties and give some implementation considerations.

To solve the multi-scale issue, multi-level modeling approaches can be used: a detailed solution to a smaller-scale problem is processed and introduced as a component of a larger-scale model. To reduce the integration time of the multi-stage chemical kinetics equations, the current approach is applying neural networks and methods to the existing computational models. This approach is currently being developed at the Department of Computing Systems in collaboration with the Center for Optical-Neural Technologies, SRISA.
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