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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| Published in: | Journal of fluid mechanics Vol. 879; pp. 217 - 254 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Cambridge
Cambridge University Press
25.11.2019
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| Subjects: | |
| ISSN: | 0022-1120, 1469-7645 |
| Online Access: | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | You, Donghyun Lee, Sangseung |
| Author_xml | – sequence: 1 givenname: Sangseung orcidid: 0000-0001-7341-8289 surname: Lee fullname: Lee, Sangseung – sequence: 2 givenname: Donghyun orcidid: 0000-0003-2470-5411 surname: You fullname: You, Donghyun |
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| Copyright | 2019 Cambridge University Press |
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| Snippet | Unsteady flow fields over a circular cylinder are used for training and then prediction using four different deep learning networks: generative adversarial... |
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| SubjectTerms | 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 |
| Title | Data-driven prediction of unsteady flow over a circular cylinder using deep learning |
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