Feedback Stabilization of Convective Brinkman-Forchheimer Extended Darcy Equations

IF 1.6 2区 数学 Q2 MATHEMATICS, APPLIED
Sagar Gautam, Kush Kinra, Manil T. Mohan
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引用次数: 0

Abstract

In this article, the following controlled convective Brinkman-Forchheimer extended Darcy (CBFeD) system is considered in a d-dimensional torus:

$$\begin{aligned} \frac{\partial {\varvec{y}}}{\partial t}-\mu \Delta {\varvec{y}}+({\varvec{y}}\cdot \nabla ){\varvec{y}}+\alpha {\varvec{y}}+\beta \vert {\varvec{y}}\vert ^{r-1}{\varvec{y}}+\gamma \vert {\varvec{y}}\vert ^{q-1}{\varvec{y}}+\nabla p={\varvec{g}}+{\varvec{u}},\ \nabla \cdot {\varvec{y}}=0, \end{aligned}$$

where \(d\in \{2,3\}\), \(\mu ,\alpha ,\beta >0\), \(\gamma \in {\mathbb {R}}\), \(r,q\in [1,\infty )\) with \(r>q\ge 1\). We prove the exponential stabilization of CBFeD system by finite- and infinite-dimensional feedback controllers. The solvability of the controlled problem is achieved by using the abstract theory of m-accretive operators and density arguments. As an application of the above solvability result, by using infinite-dimensional feedback controllers, we demonstrate exponential stability results such that the solution preserves an invariance condition for a given closed and convex set. By utilizing the unique continuation property of controllability for finite-dimensional systems, we construct a finite-dimensional feedback controller which exponentially stabilizes CBFeD system locally, where the control is localized in a smaller subdomain. Furthermore, we establish the local exponential stability of CBFeD system via proportional controllers.

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来源期刊
CiteScore
3.30
自引率
5.60%
发文量
103
审稿时长
>12 weeks
期刊介绍: The Applied Mathematics and Optimization Journal covers a broad range of mathematical methods in particular those that bridge with optimization and have some connection with applications. Core topics include calculus of variations, partial differential equations, stochastic control, optimization of deterministic or stochastic systems in discrete or continuous time, homogenization, control theory, mean field games, dynamic games and optimal transport. Algorithmic, data analytic, machine learning and numerical methods which support the modeling and analysis of optimization problems are encouraged. Of great interest are papers which show some novel idea in either the theory or model which include some connection with potential applications in science and engineering.
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