New Regularization Method for Calibrated POD Reduced-Order Models

Mathematical Modelling and Analysis

Badr Abou El Majd¹  Laurent Cordier² 

[1] LIAD Laboratory, Faculty of Science Ain Chock, Hassan II University 20100 Casablanca, Morocco;PPRIME Institute, CEAT 43 route de l’a´erodrome, 86036 Poitiers, France;

关键词: POD reduced-order model;    regularization;    singular value decomposition;    optimization;    Lcurve;   

DOI  :  10.3846/13926292.2016.1132486

来源: DOAJ

【 摘 要 】

Reduced-order models based on Proper orthogonal decomposition are known to suffer from a lack of accuracy due to the truncation effect introduced by keeping only the most energetic modes. In this paper, we propose a new regularized calibration method aiming at minimizing a weighted average of normalized error, and a term measuring the change of the coefficients from their value obtained by Galerkin projection. We also determine the optimal value of the regularization parameter by analogy of the L-curve method. This paper is a sequel of [8] in which we compared various methods of calibration and introduced a Tikhonov-based regularization method. The proposed approach is assessed for a two dimensional wake flow around a cylinder, characteristic of the configurations of interest.

【 授权许可】

Unknown