Matrix model of data and knowledge presentation to revealing regularities of the fluid flow regime in a pipeline based on hydrodynamics parameters

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Matrix model of data and knowledge presentation to revealing regularities of the fluid flow regime in a pipeline based on hydrodynamics parameters	436 KB	Adobe Acrobat PDF	Read	Download
DOI Доступ к ресурсу на сайте издателя			10.18287/1613-0073-2017-1903-54-58

Title: Matrix model of data and knowledge presentation to revealing regularities of the fluid flow regime in a pipeline based on hydrodynamics parameters
Creator: Yankovskaya, Anna Efimovna
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Travkov, A.
Date: 2017
DOI: 10.18287/1613-0073-2017-1903-54-58
Description: The study offers an original solution to one of the problems of hydrodynamics, namely revealing the regularities in the flow regime of fluid in a pipeline depending on the hydrodynamic parameters. The solution is based on using the intelligent system of the regularities revealing and decision-making. For the first time, a matrix model of data and knowledge representation (MM) is used for these purposes in the form of two matrices: descriptions of the fluid state in the space of characteristic features of hydrodynamics (pressure, velocity, temperature, and others); its rows are associated with various combinations of characteristic features values, and distinguishing of the diagnostic type, whose rows are associated with the corresponding rows of the description matrix, and its columns are associated with the two classifying features. The first classifying features takes four values corresponding to four fluid flow regimes, and the second classifying features takes three values only for the turbulent flow regime value from the first classifying features.

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collection(s): Институт прикладной математики и компьютерных наук (c 01.09.2017 г.)
Identifier: смотреть в электронном каталоге НБ ТГУ
Type: статьи в журналах
Source: CEUR workshop proceedings. 2017. Vol. 1903 : DS-ITNT 2017. Data Science. Information Technology and Nanotechnology : proceedings of the International Conference Information Technology and Nanotechnology. Session Data Science, Samara, Russia, 24-27 April, 2017. P. 54-58
Language: eng