Feature identification in complex fluid flows by convolutional neural networks
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Date
2023-11
Publication Type
Journal Article
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yes
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Abstract
Recent advancements have established machine learning's utility in predicting nonlinear fluid dynamics, with predictive accuracy being a central motivation for employing neural networks. However, the pattern recognition central to the networks function is equally valuable for enhancing our dynamical insight into the complex fluid dynamics. In this paper, a single-layer convolutional neural network (CNN) was trained to recognize three qualitatively different subsonic buffet flows (periodic, quasi-periodic and chaotic) over a high-incidence airfoil, and a near-perfect accuracy was obtained with only a small training dataset. The convolutional kernels and corresponding feature maps, developed by the model with no temporal information provided, identified large-scale coherent structures in agreement with those known to be associated with buffet flows. Sensitivity to hyperparameters including network architecture and convolutional kernel size was also explored. The coherent structures identified by these models enhance our dynamical understanding of subsonic buffet over high-incidence airfoils over a wide range of Reynolds numbers.
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published
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Journal / series
Volume
13 (6)
Pages / Article No.
100482
Publisher
Elsevier
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Edition / version
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Software
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Date collected
Date created
Subject
Subsonic buffet flows; Feature identification; Convolutional neural network; Long-short term memory
Organisational unit
02501 - Seminar für Angewandte Mathematik / Seminar for Applied Mathematics