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Super-resolution reconstruction of turbulent flows with machine learning · Around

2018

Super-resolution reconstruction of turbulent flows with machine learning

Fukami, Kai, Fukagata, Koji, Taira, Kunihiko

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We use machine learning to perform super-resolution analysis of grossly under-resolved turbulent flow field data to reconstruct the high-resolution flow field.

Two machine-learning models are developed; namely the convolutional neural network (CNN) and the hybrid Downsampled Skip-Connection Multi-Scale (DSC/MS) models.
These machine-learning models are applied to two-dimensional cylinder wake as a preliminary test and show remarkable ability to reconstruct laminar flow from low-resolution flow field data.
We further assess the performance of these models for two-dimensional homogeneous turbulence.

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Zhang, Y., Sung, W. & Mavris, D
1903
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Shanker, M., S., Hu, M., Y. & Hung, M., S
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Lecun, Y., Bottou, L., Bengio, Y. & Haffner, P
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Prechelt, L
1998
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Taira, K. & Colonius, T
2007
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Colonius, T. & Taira, K
2008
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Le, Q., Ngiam, J., Chen, Z., Chia, D. & Koh, P
2010
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Nair, V. & Hinton, G., E
2010
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