Learning-based robust stabilization for reduced-order models of 2D and 3D Boussinesq equations

被引：34

作者：

Benosman, Mouhacine ^{[1
]}

Borggaard, Jeff ^{[2
]}

San, Omer ^{[3
]}

Kramer, Boris ^{[4
]}

机构：

[1] MERL, Cambridge, MA 02139 USA

[2] Virginia Tech, Interdisciplinary Ctr Appl Math, Blacksburg, VA 24061 USA

[3] Oklahoma State Univ, Dept Mech & Aerosp Engn, 218 Engn NorthStillwater, Stillwater, OK 74078 USA

[4] MIT, Dept Aeronaut & Astronaut, Cambridge, MA 02139 USA

来源：

APPLIED MATHEMATICAL MODELLING | 2017年 / 49卷

基金：

美国国家科学基金会;

关键词：

Reduced-order models; Closure models; Robust Lyapunov control; Extremum-seeking; Boussinesq; LARGE-EDDY SIMULATIONS; VISCOSITY METHOD; OPTIMIZATION; REDUCTION; SCHEMES; SYSTEMS;

D O I：

10.1016/j.apm.2017.04.032

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

We present some results on the stabilization of reduced-order models (ROMs) for thermal fluids. The stabilization is achieved using robust Lyapunov control theory to design a new closure model that is robust to parametric uncertainties. Furthermore, the free parameters in the proposed ROM stabilization method are optimized using a data-driven multi parametric extremum seeking (MES) algorithm. The 2D and 3D Boussinesq equations provide challenging numerical test cases that are used to demonstrate the advantages of the proposed method. (C) 2017 Elsevier Inc. All rights reserved.

引用

页码：162 / 181

页数：20

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