A Streamline Upwind Petrov-Galerkin Reduced Order Method for Advection-Dominated Partial Differential Equations Under Optimal Control

IF 1 4区 数学 Q3 MATHEMATICS, APPLIED
Fabio Zoccolan, Maria Strazzullo, Gianluigi Rozza
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引用次数: 0

Abstract

In this paper we will consider distributed Linear-Quadratic Optimal Control Problems dealing with Advection-Diffusion PDEs for high values of the Péclet number. In this situation, computational instabilities occur, both for steady and unsteady cases. A Streamline Upwind Petrov–Galerkin technique is used in the optimality system to overcome these unpleasant effects. We will apply a finite element method discretization in an optimize-then-discretize approach. Concerning the parabolic case, a stabilized space-time framework will be considered and stabilization will also occur in both bilinear forms involving time derivatives. Then we will build Reduced Order Models on this discretization procedure and two possible settings can be analyzed: whether or not stabilization is needed in the online phase, too. In order to build the reduced bases for state, control, and adjoint variables we will consider a Proper Orthogonal Decomposition algorithm in a partitioned approach. It is the first time that Reduced Order Models are applied to stabilized parabolic problems in this setting. The discussion is supported by computational experiments, where relative errors between the FEM and ROM solutions are studied together with the respective computational times.
优化控制下对流偏微分方程的流线型上风 Petrov-Galerkin 降阶方法
在本文中,我们将考虑分布式线性-二次方最优控制问题,该问题涉及高佩克莱特数值的平流-扩散 PDE。在这种情况下,无论是稳定情况还是非稳定情况,都会出现计算不稳定性。在优化系统中采用了流线上风 Petrov-Galerkin 技术,以克服这些令人不快的影响。我们将采用先优化后离散的有限元法离散化方法。关于抛物线情况,我们将考虑一个稳定的时空框架,稳定也将发生在涉及时间导数的双线性形式中。然后,我们将在此离散化过程的基础上建立还原阶模型,并分析两种可能的设置:在线阶段是否也需要稳定化。为了建立状态变量、控制变量和邻接变量的还原基,我们将考虑采用分区方法中的适当正交分解算法。这是第一次在这种情况下将还原阶模型应用于稳定抛物线问题。讨论将通过计算实验来支持,在实验中将研究有限元求解和 ROM 求解之间的相对误差以及各自的计算时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.40
自引率
7.70%
发文量
54
期刊介绍: The highly selective international mathematical journal Computational Methods in Applied Mathematics (CMAM) considers original mathematical contributions to computational methods and numerical analysis with applications mainly related to PDEs. CMAM seeks to be interdisciplinary while retaining the common thread of numerical analysis, it is intended to be readily readable and meant for a wide circle of researchers in applied mathematics. The journal is published by De Gruyter on behalf of the Institute of Mathematics of the National Academy of Science of Belarus.
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