Abstract
Cluster analysis is applied to a DNS dataset of a transitional boundary layer developing over a flat plate. The stream-wise-span-wise plane at a wall normal distance close to the wall is sampled at several time instants and discretized into small sub-regions, which are the observations analysed in this work. Using K-medoids clustering algorithm, a partition of the observations is sought such that the medoids in each cluster represent the main local states. The clustering has been carried out on a two-dimensional reduced-order feature space, constructed with the multi-dimensional scaling technique. The clustered feature space provides a partitioning which consists of five different regions. The observations are automatically classified as laminar, turbulent spots, amplification of disturbances, or fully-developed turbulence. The Lagrangian evolution of the regions and the state transitions are described as a Markov process in terms of transition probability matrix and transition trajectory graph to determine the transition dynamics between different states.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
S.L. Brunton, B.R. Noack, P. Koumoutsakos, Machine Learning for Fluid Mechanics. J Fluid Mech 52, 477–508 (2020)
S.L. Brunton, B.R. Noack, Closed-Loop Turbulence Control: Progress and Challenges. Appl. Mech. Rev. 67(5), 050801 (2015)
T.A. Zaki, From Streaks to Spots and on to Turbulence: Exploring the Dynamics of Boundary Layer Transition. Flow, Turbulence and Combustion 91(3), 451–473 (2013)
Z. Wu, J. Lee, C. Meneveau, T.A. Zaki, Application of a self-organizing map to identify the turbulent-boundary-layer interface in a transitional flow. Physical Review Fluid 4, 023902 (2019)
E. Kaiser, B.R. Noack, L. Cordier, A. Spohn, M. Segond, M. Abel, G. Daviller, J östh, S Krajnović, and R Niven, , Cluster-based reduced-order modelling of a mixing layer. J Fluid Mech 754, 365–414 (2014)
R.L. Thorndike, Who belongs in the family? Psychometrika 18, 267–276 (1953)
X. Wu, R.G. Jacobs, J.C.R. Hunt, P.A. Durbin, Simulation of boundary layer transition induced by periodically passing wakes. J Fluid Mech 398, 109–153 (1999)
Acknowledgements
This work was partially supported by the research projects:
1. PITUFLOW-CM-UC3M, funded by the call “Programa de apoyo a la realización de proyectos interdisciplinares de I+D para jóvenes investigadores de la Universidad Carlos III de Madrid 2019–2020” under the frame of the Convenio Plurianual Comunidad de Madrid-Universidad Carlos III de Madrid.
2. COTURB, funded by the European Research Council, under grant \(ERC-2014.AdG-669505\).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Foroozan, F., Guerrero, V., Ianiro, A., Discetti, S. (2021). Data-Driven Dynamics Description of a Transitional Boundary Layer. In: Örlü, R., Talamelli, A., Peinke, J., Oberlack, M. (eds) Progress in Turbulence IX. iTi 2021. Springer Proceedings in Physics, vol 267. Springer, Cham. https://doi.org/10.1007/978-3-030-80716-0_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-80716-0_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80715-3
Online ISBN: 978-3-030-80716-0
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)