Abstract
This work presents an automated process minimising input parameters for the study of turbulent flows. The goal is to gain insight into the flow dynamics by deriving a data-driven reduced-order model (ROM). Spectral proper orthogonal decomposition (SPOD) is used to efficiently separate the flow dynamics and project the flow field onto a low-dimensional subspace to represent the dominating dynamics with a reduced set of modes. A polynomial combinations of the temporal modal coefficients defines a function library to describe the dynamics by a linear system of ordinary differential equations. In a two-stages cross-validation procedure (conservative and restrictive sparsification), the most important functions are identified and combined in a final ROM. The process is demonstrated for PIV data of a circular cylinder undergoing vortex induced vibration (VIV) Re = 4000.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
K. Taira, S.L. Brunton, S.T.M.. Dawson, C.W. Rowley, T. Colonius, B.J. McKeon, O.T. Schmidt, S. Gordeyev, V. Theofilis, L.S. Ukeiley, Modal Analysis of Fluid Flows: An Overview. AIAA J 55, 4013–4041 (2017)
B.R. Noack, K. Afanasiev, M. Morzyński, G. Tadmor, F. Thiele, A hierarchy of low-dimensional models for the transient and post-transient cylinder wake. J Fluid Mech 497, 335–363 (2003)
M. Sieber, C.O. Paschereit, K. Oberleithner, Spectral proper orthogonal decomposition. J Fluid Mech 792, 798–828 (2016)
S.L. Brunton, J.L. Proctor, J.N. Kutz, Discovering governing equations from data by sparse identification of nonlinear dynamical systems. PNAS USA 113, 3932–3937 (2016)
G. Riches, C. Morton, One degree-of-freedom vortex-induced vibrations at constant Reynolds number and mass-damping. Exp. Fluids 59, 1–16 (2018)
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
Schubert, Y., Sieber, M., Oberleithner, K., Martinuzzi, R.J. (2021). Data-Driven Identification of Robust Low-Order Models for Dominant Dynamics in Turbulent Flows. 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_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-80716-0_21
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)