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New Regularization Method for Calibrated POD Reduced-Order Models

    Badr Abou El Majd Affiliation
    ; Laurent Cordier Affiliation

Abstract

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.

Keyword : POD reduced-order model, regularization, singular value decomposition, optimization, Lcurve

How to Cite
El Majd, B. A., & Cordier, L. (2016). New Regularization Method for Calibrated POD Reduced-Order Models. Mathematical Modelling and Analysis, 21(1), 47-62. https://doi.org/10.3846/13926292.2016.1132486
Published in Issue
Jan 26, 2016
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This work is licensed under a Creative Commons Attribution 4.0 International License.