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FEATURE DETECTION ALGORITHMS AND MODAL DECOMPOSITION METHODS

Publicat a:World Congress In Computational Mechanics And Eccomas Congress. - 2022-01-01 (), DOI: 10.23967/eccomas.2022.117

Autors: Begiashvili B; Garicano-Mena J; Le Clainche S; Valero E

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Various modal decomposition techniques have been developed in the last decade [1-11]. 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 [1-3] and Dynamic Mode Decomposition [4-6,8-11]) on two data set testcases taken from fluid dynamics. The datasets considered (the velocity field of laminar wake around the mid-section of a very long cylinder at ReD = 100 and the pressure field of turbulent jet (axisymetric) at ReD = 106) represent different flow regimes. The performance of these algorithms is thoroughly assessed concerning both the accuracy of the results retrieved and the computational performance. From this assessment, those techniques that are potentially better suited for the applications are identified and after the possibility of parallelizing the algorithms will be studied with a final objective: To enable data-driven analysis of industrially relevant fluid mechanical problems.

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