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Unsupervised modelling of a transitional boundary layer

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Aeroespaciales
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Equipo de Propulsión Espacial y Plasmas (EP2)es
dc.contributor.authorForoozan, Firoozeh
dc.contributor.authorGuerrero Lozano, Vanesa
dc.contributor.authorIaniro, Andrea
dc.contributor.authorDiscetti, Stefano
dc.contributor.funderComunidad de Madrides
dc.date.accessioned2022-02-14T18:29:47Z
dc.date.available2022-02-14T18:29:47Z
dc.date.issued2021-10-19
dc.description.abstractA data-driven approach for the identification of local turbulent-flow states and of their dynamics is proposed. After subdividing a flow domain in smaller regions, the K -medoids clustering algorithm is used to learn from the data the different flow states and to identify the dynamics of the transition process. The clustering procedure is carried out on a two-dimensional (2-D) reduced-order space constructed by the multidimensional scaling (MDS) technique. The MDS technique is able to provide meaningful and compact information while reducing the dimensionality of the problem, and therefore the computational cost, without significantly altering the data structure in the state space. The dynamics of the state transitions is then described in terms of a transition probability matrix and a transition trajectory graph. The proposed method is applied to a direct numerical simulation dataset of an incompressible boundary layer flow developing on a flat plate. Streamwise-spanwise velocity fields at a specific wall-normal position are referred to as observations. Reducing the dimensionality of the problem allows us to construct a 2-D map, representative of the local turbulence intensity and of the spanwise skewness of the turbulence intensity in the observations. The clustering process classifies the regions containing streaks, turbulent spots, turbulence amplification and developed turbulence while the transition matrix and the transition trajectories correctly identify the states of the process of bypass transition.en
dc.description.sponsorshipThis work has been supported by: (i) the Madrid Government (Comunidad de Madrid) under the Multiannual Agreement with UC3M in the line of ‘Fostering Young Doctors Research’ (PITUFLOW-CM-UC3M), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation); (ii) the COTURB project (Coherent Structures in Wall-bounded Turbulence), funded by the European Research Council (ERC), under grant ERC-2014.AdG-669505.en
dc.identifier.bibliographicCitationForoozan, F., Guerrero, V., Ianiro, A., & Discetti, S. (2021). Unsupervised modelling of a transitional boundary layer. Journal of Fluid Mechanics, 929, A3.es
dc.identifier.doihttps://doi.org/10.1017/jfm.2021.829
dc.identifier.issn0022-1120
dc.identifier.publicationfirstpageA3-1es
dc.identifier.publicationlastpageA3- 25es
dc.identifier.publicationtitleJOURNAL OF FLUID MECHANICSes
dc.identifier.publicationvolume929es
dc.identifier.urihttps://hdl.handle.net/10016/34123
dc.identifier.uxxiAR/0000028512
dc.language.isoenges
dc.publisherCambridge University Press.es
dc.relation.projectIDComunidad de Madrid. PITUFLOW-CM-UC3Mes
dc.relation.projectIDAT-2021
dc.rights© The Author(s)es
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaIngeniería Mecánicaes
dc.subject.otherTransition to turbulenceen
dc.subject.othermachine learningen
dc.subject.otherLow-dimensional modelsen
dc.titleUnsupervised modelling of a transitional boundary layeren
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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