Abstract
This study explores challenges in multivariate modal decomposition for various flow scenarios, emphasizing the problem of inconsistent physical modes in Proper Orthogonal Decomposition (POD). This inconsistency arises due to POD's inability to capture inter-variable relationships and common flow patterns, resulting in a loss of phase information. To address this issue, the study introduces two novel data-driven modal analysis methods, collectively called Information Sharing-Based Multivariate POD (IMPOD). These methods, namely, Shared Space Information Multivariate POD (SIMPOD) and Shared Time Information Multivariate POD (TIMPOD), aim to regularize modal decomposition by promoting information sharing among variables. TIMPOD, which assumes shared time information, successfully aligns multivariate modes and corrects their phases without significantly affecting reconstruction error, making it a promising corrective technique for multivariate modal decomposition. In contrast, SIMPOD, which assumes shared space information, reorders modes and may lead to a loss of meaningful insight and reconstruction error.
Funder
National Natural Science Foundation of China
Fundamental Research Fund for the Central Universities
Computation support of the Supercomputing Center of Dalian University of Technology
Subject
Condensed Matter Physics,Fluid Flow and Transfer Processes,Mechanics of Materials,Computational Mechanics,Mechanical Engineering
Reference37 articles.
1. Challenges for large eddy simulation of engineering flows,2018
2. Turbulence modeling in the age of data;Annu. Rev. Fluid Mech.,2019
3. Machine learning for fluid mechanics;Annu. Rev. Fluid Mech.,2020
4. The structure of inhomogeneous turbulent flows,1967
5. Dynamic mode decomposition of numerical and experimental data;J. Fluid Mech.,2010
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