Dynamic Mode Decomposition Analysis of Spatially Agglomerated Flow Databases

Binghua Li, Jesús Garicano-Mena, Yao Zheng and Eusebio Valero
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Binghua Li: Center for Engineering and Scientific Computation, School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China
Jesús Garicano-Mena: ETSI Aeronáutica y del Espacio, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Yao Zheng: Center for Engineering and Scientific Computation, School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China
Eusebio Valero: ETSI Aeronáutica y del Espacio, Universidad Politécnica de Madrid, 28040 Madrid, Spain

Energies, 2020, vol. 13, issue 9, 1-23

Abstract: Dynamic Mode Decomposition ( DMD ) techniques have risen as prominent feature identification methods in the field of fluid dynamics. Any of the multiple variables of the DMD method allows to identify meaningful features from either experimental or numerical flow data on a data-driven manner. Performing a DMD analysis requires handling matrices V ∈ R n p × N , where n p and N are indicative of the spatial and temporal resolutions. The DMD analysis of a complex flow field requires long temporal sequences of well resolved data, and thus the memory footprint may become prohibitively large. In this contribution, the effect that principled spatial agglomeration (i.e., reduction in n p via clustering ) has on the results derived from the DMD analysis is investigated. We compare twelve different clustering algorithms on three testcases, encompassing different flow regimes: a synthetic flow field, a R e D = 60 flow around a cylinder cross section, and a R e τ ≈ 200 turbulent channel flow. The performance of the clustering techniques is thoroughly assessed concerning both the accuracy of the results retrieved and the computational performance. From this assessment, we identify DBSCAN/HDBSCAN as the methods to be used if only relatively high agglomeration levels are affordable. On the contrary, Mini-batch K-means arises as the method of choice whenever high agglomeration n p ˜ / n p ? 1 is possible.

Keywords: modal decompositions; turbulent flows; flow reconstruction; machine learning; clustering algorithms; spatial agglomeration; dynamic mode decomposition; feature detection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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