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Analysis of institutional authors

Begiashvili BAuthorGaricano-Mena JAuthorLe Clainche SAuthorValero EAuthor

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February 6, 2023
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Proceedings Paper
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FEATURE DETECTION ALGORITHMS AND MODAL DECOMPOSITION METHODS

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

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

Affiliations

Universidad Politécnica de Madrid - Author
Universidad Politécnica de Madrid , Center for Computational Simulation (CCS) - Author

Abstract

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.

Keywords

Dynamic mode decompositionFeature detectionLow order algorithmsMachine learningMatrix factorizationModal decompositionsProper orthogonal decomposition

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal World Congress In Computational Mechanics And Eccomas Congress, Q4 Agency Scopus (SJR), its regional focus and specialization in Mechanical Engineering, give it significant recognition in a specific niche of scientific knowledge at an international level.

Impact and social visibility

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (BEGIASHVILI, BEKA) and Last Author (VALERO SANCHEZ, EUSEBIO).