Data-driven prediction of unsteady flow over a circular cylinder using deep learning

Unsteady flow fields over a circular cylinder are used for training and then prediction using four different deep learning networks: generative adversarial networks with and without consideration of conservation laws; and convolutional neural networks with and without consideration of conservation l...

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Veröffentlicht in:Journal of fluid mechanics Jg. 879; S. 217 - 254
Hauptverfasser: Lee, Sangseung, You, Donghyun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cambridge Cambridge University Press 25.11.2019
Schlagworte:
Artificial neural networks
Circular cylinders
Computer simulation
Conservation
Conservation laws
Conservation of mass
Cylinders
Datasets
Decomposition
Deep learning
Feature extraction
Fields
Laboratories
Machine learning
Momentum
Neural networks
Physics
Predictions
Reynolds number
Simulation
Training
Unsteady flow
Vortices
ISSN:0022-1120, 1469-7645
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Zusammenfassung:Unsteady flow fields over a circular cylinder are used for training and then prediction using four different deep learning networks: generative adversarial networks with and without consideration of conservation laws; and convolutional neural networks with and without consideration of conservation laws. Flow fields at future occasions are predicted based on information on flow fields at previous occasions. Predictions of deep learning networks are made for flow fields at Reynolds numbers that were not used during training. Physical loss functions are proposed to explicitly provide information on conservation of mass and momentum to deep learning networks. An adversarial training is applied to extract features of flow dynamics in an unsupervised manner. Effects of the proposed physical loss functions and adversarial training on predicted results are analysed. Captured and missed flow physics from predictions are also analysed. Predicted flow fields using deep learning networks are in good agreement with flow fields computed by numerical simulations.
Bibliographie:ObjectType-Article-1
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ISSN:0022-1120
1469-7645
DOI:10.1017/jfm.2019.700