2020
DOI: 10.1016/j.jcp.2019.109216
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Deep unsupervised learning of turbulence for inflow generation at various Reynolds numbers

Abstract: A B S T R A C TA realistic inflow boundary condition is essential for successful simulation of the developing turbulent boundary layer or channel flows. Recent advances in artificial intelligence (AI) have enabled the development of an inflow generator that performs better than the synthetic methods based on intuitions. In the present work, we applied generative adversarial networks (GANs), a representative of unsupervised learning, to generate an inlet boundary condition of turbulent channel flow. Upon learni… Show more

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Cited by 66 publications
(36 citation statements)
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References 51 publications
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“…Good solutions should include transfer learning (Guastoni et al. 2020; Kim & Lee 2020 a ), data augmentation using symmetry, physics-informed NNs that impose constraints of governing equation (continuity or momentum equations) (Raissi, Perdikaris & Karniadakis 2019; Jiang et al. 2020), and physical constraints added to the NN (Mohan et al.…”
Section: Resultsmentioning
confidence: 99%
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“…Good solutions should include transfer learning (Guastoni et al. 2020; Kim & Lee 2020 a ), data augmentation using symmetry, physics-informed NNs that impose constraints of governing equation (continuity or momentum equations) (Raissi, Perdikaris & Karniadakis 2019; Jiang et al. 2020), and physical constraints added to the NN (Mohan et al.…”
Section: Resultsmentioning
confidence: 99%
“…For example, it is possible to account for temporally successive data by adding a discriminator that considers temporal effects (Xie et al. 2018; Kim & Lee 2020 a ). Fifth, although in channel turbulence, we found 5.2 times extrapolation ability of our model in Reynolds number, we could not tell whether our model works well for the even higher Reynolds number.…”
Section: Resultsmentioning
confidence: 99%
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“…As a remedy to this problem, unsupervised learning could be considered. Kim & Lee (2019) showed that it is possible to learn a similarity between simulations with different Reynolds numbers through unsupervised learning of turbulence. Likewise, connecting data from low-resolution and high-resolution simulations would be possible.…”
Section: Resultsmentioning
confidence: 99%