Super-resolution analysis with machine learning for low-resolution flow data

Kai Fukami, Koji Fukagata, Kunihiko Taira

Research output: Contribution to conferencePaper

Abstract

Machine-learned super-resolution is performed to reconstruct the high-resolution flow (HR) field from low-resolution (LR) fluid flow data. As preliminary tests, we use two-dimensional cylinder and NACA0012 airfoil wake flow fields and observe good agreement with reference HR data. Next, we apply two machine-learned architectures based on the convolutional neural network (CNN) for two-dimensional decaying isotropic turbulence. The HR data sets are obtained from direct numerical simulation (DNS) and LR data sets are generated by max and average pooling operations. In this work, we present the hybrid Down-sampled Skip-Connection Multi-Scale (DSC/MS) model, which can reconstruct the flow field accurately from coarse input flow field data. Towards the end of the paper, we discuss the possibility of a machine-learned model for super-resolution in experimental and computational fluid dynamics.

Original languageEnglish
Publication statusPublished - 2019 Jan 1
Event11th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2019 - Southampton, United Kingdom
Duration: 2019 Jul 302019 Aug 2

Conference

Conference11th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2019
CountryUnited Kingdom
CitySouthampton
Period19/7/3019/8/2

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ASJC Scopus subject areas

  • Atmospheric Science
  • Aerospace Engineering

Cite this

Fukami, K., Fukagata, K., & Taira, K. (2019). Super-resolution analysis with machine learning for low-resolution flow data. Paper presented at 11th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2019, Southampton, United Kingdom.