Steady Navier–Stokes Equations with Regularized Directional Do-Nothing Boundary Condition: Optimal Boundary Control for a Velocity Tracking Problem

IF 1.7 2区 数学 Q2 MATHEMATICS, APPLIED Applied Mathematics and Optimization Pub Date : 2025-01-27 DOI:10.1007/s00245-024-10216-4
Pedro Nogueira, Ana L. Silvestre, Jorge Tiago
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Abstract

We consider the steady Navier–Stokes equations with mixed boundary conditions, where a regularized directional do-nothing (RDDN) condition is defined on the Neumann boundary portion. An auxiliary Stokes reference flow, which also works as a lifting of the inhomogeneous Dirichlet boundary values, is used to define the RDDN condition. Our aim is to study the minimization of a velocity tracking cost functional with controls localized on a part of the boundary. We prove the existence of a solution for this optimal control problem and derive the corresponding first order necessary optimality conditions in terms of dual variables. All results are obtained under appropriate assumptions on the size of the data and the controls, which, however, are less restrictive when compared with the case of the classical do-nothing outflow condition. This is further confirmed by the numerical examples presented, which include scenarios where only noisy data is available.

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具有正则定向无行为边界条件的稳定Navier-Stokes方程:速度跟踪问题的最优边界控制
考虑具有混合边界条件的稳定Navier-Stokes方程,其中在Neumann边界部分上定义了正则定向无行为(RDDN)条件。一个辅助Stokes参考流,也可以作为非齐次Dirichlet边值的提升,被用来定义RDDN条件。我们的目标是研究速度跟踪代价函数的最小化,控制在边界的一部分上。证明了该最优控制问题解的存在性,并导出了对偶变量的一阶必要最优性条件。所有结果都是在对数据大小和控制的适当假设下获得的,然而,与经典的无所事事流出条件相比,这些假设的限制较少。所提出的数值例子进一步证实了这一点,其中包括只有噪声数据可用的情况。
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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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