Data-driven feature identification and sparse representation of turbulent flows
| Autor: | Andrew Wynn, Mohammad Beit-Sadi, Jakub Krol |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Fluid Flow and Transfer Processes
Computer science Mechanical Engineering Mode (statistics) Graph theory 02 engineering and technology Sparse approximation Condensed Matter Physics 01 natural sciences Measure (mathematics) 010305 fluids & plasmas Data-driven Reduction (complexity) 020303 mechanical engineering & transports 0203 mechanical engineering Feature (computer vision) 0103 physical sciences Dynamic mode decomposition Algorithm |
| Zdroj: | International Journal of Heat and Fluid Flow. 88:108766 |
| ISSN: | 0142-727X |
| DOI: | 10.1016/j.ijheatfluidflow.2020.108766 |
| Popis: | Identifying coherent structures in fluid flows is of great importance for reduced order modelling and flow control. However, extracting such structures from experimental or numerical data obtained from a turbulent flow can be challenging. A number of modal decomposition algorithms have been proposed in recent years which decompose time-resolved snapshots of data into spatial modes, each associated with a single frequency and growth-rate. Most prominently among them is dynamic mode decomposition (DMD). However, DMD-like algorithms create an arbitrary number of modes. It is common practice to then choose a smaller subset of these modes, for the purpose of model reduction and analysis, based on some measure of significance. In this work, we present a method of post-processing DMD modes for extracting a small number of dynamically relevant modes. We achieve this through an iterative approach based on the graph-theoretic notion of maximal cliques to identify clusters of modes and representing each cluster with a single representative mode. |
| Databáze: | OpenAIRE |
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