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| + | ==Summary== | ||
| + | Various modal decomposition techniques have been developed in the last decade [111]. We focus on data-driven approches, and since data flow volume is increasing day by day, it is important to study the performance of order reduction and feature detection algorithms. In this work we compare the performance and feature detection behaviour of energy and frequency based algorithms (Proper Orthogonal Decomposition [13] and Dynamic Mode Decomposition [46, 811]) on two data set testcases taken from fluid dynamics. | ||
| + | |||
| + | == Abstract == | ||
| + | <pdf>Media:Draft_Sanchez Pinedo_343111130417_abstract.pdf</pdf> | ||
| + | |||
| + | == Full Paper == | ||
| + | <pdf>Media:Draft_Sanchez Pinedo_343111130417_paper.pdf</pdf> | ||
Latest revision as of 16:06, 25 November 2022
Summary
Various modal decomposition techniques have been developed in the last decade [111]. We focus on data-driven approches, and since data flow volume is increasing day by day, it is important to study the performance of order reduction and feature detection algorithms. In this work we compare the performance and feature detection behaviour of energy and frequency based algorithms (Proper Orthogonal Decomposition [13] and Dynamic Mode Decomposition [46, 811]) on two data set testcases taken from fluid dynamics.
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Published on 24/11/22
Accepted on 24/11/22
Submitted on 24/11/22
Volume Computational Fluid Dynamics, 2022
DOI: 10.23967/eccomas.2022.117
Licence: CC BY-NC-SA license
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