Super-resolution reconstruction of turbulent flows with machine learning

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-s...

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Bibliographic Details
Published in:	Journal of fluid mechanics Vol. 870; pp. 106 - 120
Main Authors:	Fukami, Kai, Fukagata, Koji, Taira, Kunihiko
Format:	Journal Article
Language:	English
Published:	Cambridge, UK Cambridge University Press 10.07.2019
Subjects:	Accuracy Artificial intelligence Artificial neural networks Computational fluid dynamics Computer simulation Cylinders Data Data compression Datasets Fluid dynamics Fluid flow Fluid mechanics Homogeneous turbulence Image reconstruction JFM Papers Laminar flow Learning algorithms Machine learning Neural networks Paper machines Performance assessment Physics Resolution Reynolds number Spatial discrimination Spatial resolution Training Turbulence Turbulence models Turbulent flow Two dimensional models United States > US computational methods wakes homogeneous turbulence
ISSN:	0022-1120, 1469-7645
Online Access:	Get full text
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