Learning-based robust stabilization for reduced-order models of 2D and 3D Boussinesq equations
Learning-based robust stabilization for reduced-order models of 2D and 3D Boussinesq equations
复制标题
2D 和 3D Boussinesq 方程降阶模型的基于学习的鲁棒稳定性
DOI：
10.1016/j.apm.2017.04.032
复制
发表时间：
2017
影响因子：
5
通讯作者：
B. Kramer
中科院分区：
文献类型：
--
作者：
M. Benosman;J. Borggaard;O. San;B. Kramer
本文给出了热流体降阶模型的稳定性的一些结果。利用鲁棒李雅普诺夫控制理论设计了对参数不确定性具有鲁棒性的闭环模型，实现了系统的稳定。此外，采用数据驱动的多参数极值搜索（MES）算法对ROM稳定化方法中的自由参数进行了优化。2D和3D Boussinesq方程提供了具有挑战性的数值测试用例，用于证明所提出方法的优势。
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.