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Analysis of institutional authors

Otmani KeCorresponding AuthorNtoukas GAuthorMariÑo Sanchez, Oscar AndresAuthorFerrer EAuthor

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February 27, 2023
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Toward a robust detection of viscous and turbulent flow regions using unsupervised machine learning

Publicated to:Physics Of Fluids. 35 (2): 027112- - 2023-02-01 35(2), DOI: 10.1063/5.0138626

Authors: Otmani, KE; Ntoukas, G; Mariño, OA; Ferrer, E

Affiliations

Univ Politecn Madrid, Ctr Computat Simulat, Campus Montegancedo, Madrid 28660, Spain - Author
Univ Politecn Madrid, Sch Aeronaut, ETSIAE UPM, Plaza Cardenal Cisneros 3, E-28040 Madrid, Spain - Author
Universidad Politécnica de Madrid - Author

Abstract

We propose an invariant feature space for the detection of viscous-dominated and turbulent regions (i.e., boundary layers and wakes). The developed methodology uses the principal invariants of the strain and rotational rate tensors as input to an unsupervised Machine Learning Gaussian mixture model. The selected feature space is independent of the coordinate frame used to generate the processed data, as it relies on the principal invariants of the strain and rotational rate, which are Galilean invariants. This methodology allows us to identify two distinct flow regions: a viscous-dominated, rotational region (a boundary layer and a wake region) and an inviscid, irrotational region (an outer flow region). We have tested the methodology on a laminar and a turbulent (using Large Eddy Simulation) case for flows past a circular cylinder at Re = 40 and Re = 3900 and a laminar flow around an airfoil at Re = 1 × 10 5. The simulations have been conducted using a high-order nodal Discontinuous Galerkin Spectral Element Method. The results obtained are analyzed to show that Gaussian mixture clustering provides an effective identification method of viscous-dominated and rotational regions in the flow. We also include comparisons with traditional sensors to show that the proposed clustering does not depend on the selection of an arbitrary threshold, as required when using traditional sensors.

Keywords

algorithmdeepdynamicselement-methodnoiseNeural-networks

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Physics Of Fluids due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2023, it was in position 2/40, thus managing to position itself as a Q1 (Primer Cuartil), in the category Physics, Fluids & Plasmas. Notably, the journal is positioned above the 90th percentile.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.51. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 1.45 (source consulted: FECYT Feb 2024)
  • Field Citation Ratio (FCR) from Dimensions: 3.56 (source consulted: Dimensions Jul 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-21, the following number of citations:

  • WoS: 9
  • Scopus: 9
  • Google Scholar: 5

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-21:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 9.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 8 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 0.25.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

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 (KHEIR-EDDINE, OTMANI) and Last Author (FERRER VACCAREZZA, ESTEBAN).

the author responsible for correspondence tasks has been KHEIR-EDDINE, OTMANI.