Identification of piecewise constant filtration parameters and boundaries of their constancy domains

Consideration was given to the numerical solution of the problem of parametric identification of the processes obeying the parabolic equations using an example of the processes of underground oil filtration. The identified parameters belong to the given functional classes such as the piecewise const...

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Bibliographic Details
Published in:Automation and remote control Vol. 78; no. 8; pp. 1404 - 1416
Main Authors: Aida-zade, K. R., Rahimov, A. B.
Format: Journal Article
Language:English
Published: Moscow Pleiades Publishing 01.08.2017
Springer Nature B.V
Subjects:
CAE) and Design
Calculus of Variations and Optimal Control; Optimization
Computer-Aided Engineering (CAD
Control
Filtration
Linear Systems
Mathematical models
Mathematics
Mathematics and Statistics
Mechanical Engineering
Mechatronics
Optimization
Parameter identification
Robotics
Systems Theory
parametric identification
inverse problem
numerical methods
constancy domain
object with distributed parameters
mathematical model of filtration
methods of optimization
functional gradient
partial differential equations
ISSN:0005-1179, 1608-3032
Online Access:Get full text
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Summary:Consideration was given to the numerical solution of the problem of parametric identification of the processes obeying the parabolic equations using an example of the processes of underground oil filtration. The identified parameters belong to the given functional classes such as the piecewise constant and piecewise linear functions. In the problem, needed is not only to determine the values of the coefficients, but also to identify the constancy boundaries of the coefficients. For numerical solution of the problem, an approach was suggested based on reduction of the initial problem to that of finite-dimensional optimization with a special structure of constraints. Obtained were the formulas for the gradient of the objective functional in the discretized problem allowing one to apply the efficient methods of first-order optimization. The results of numerical experiments on the model problems were presented.
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ISSN:0005-1179
1608-3032
DOI:10.1134/S0005117917080021