Abstract
Subcritical pipe flow transition has received a great deal of attention over the past decades, as it constitutes a quintessential bifurcation process between two metastable fluid states: the laminar and turbulent solutions. Coherent lower-branch structures, forming flow states that facilitate between these two attracting equilibria, have been proposed that together form an edge manifold in phase space separating relaminarizing from transitioning perturbations. Typically, direct numerical simulations or low-dimensional model equations have been used to study this edge manifold with bisection methods. In the article by Kaszás & Haller (J. Fluid Mech., vol. 979, 2024, A48), an effective nonlinear invariant-manifold technique has been applied to extract a low-dimensional, global representation of the phase-space dynamics directly from simulation data. It allows the computation of the intersection of the edge manifold with a low-dimensional surface that is strikingly accurate in predicting the long-term dynamics of perturbations about the lower-branch solution and thus provides an accessible parameterization of the edge manifold for subcritical pipe flow transition.
Original language | English (US) |
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Article number | F1 |
Journal | Journal of Fluid Mechanics |
Volume | 983 |
DOIs | |
State | Published - Mar 12 2024 |
Bibliographical note
Publisher Copyright:© 2024 Cambridge University Press. All rights reserved.
Keywords
- transition to turbulence, machine learning
ASJC Scopus subject areas
- Condensed Matter Physics
- Mechanics of Materials
- Mechanical Engineering
- Applied Mathematics