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Title: Convolutional neural networks to predict the onset of oscillatory instabilities in turbulent systems
Many fluid dynamic systems exhibit undesirable oscillatory instabilities due to positive feedback between fluctuations in their different subsystems. Thermoacoustic instability, aeroacoustic instability, and aeroelastic instability are some examples. When the fluid flow in the system is turbulent, the approach to such oscillatory instabilities occurs through a universal route characterized by a dynamical regime known as intermittency. In this paper, we extract the peculiar pattern of phase space attractors during the regime of intermittency by constructing recurrence networks corresponding to the phase space topology. We further train a convolutional neural network to classify the periodic and aperiodic structures in the recurrence networks and define a measure that indicates the proximity of the dynamical state to the onset of oscillatory instability. We show that this measure can predict the onset of oscillatory instabilities in three different fluid dynamic systems governed by different physical phenomena.  more » « less
Award ID(s):
2053671
PAR ID:
10586520
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
American Institute of Physics
Date Published:
Journal Name:
Chaos: An Interdisciplinary Journal of Nonlinear Science
Volume:
31
Issue:
9
ISSN:
1054-1500
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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