Assessment of supervised machine learning methods for fluid flows

Kai Fukami, Koji Fukagata, Kunihiko Taira

研究成果: Article査読

47 被引用数 (Scopus)

抄録

We apply supervised machine learning techniques to a number of regression problems in fluid dynamics. Four machine learning architectures are examined in terms of their characteristics, accuracy, computational cost, and robustness for canonical flow problems. We consider the estimation of force coefficients and wakes from a limited number of sensors on the surface for flows over a cylinder and NACA0012 airfoil with a Gurney flap. The influence of the temporal density of the training data is also examined. Furthermore, we consider the use of convolutional neural network in the context of super-resolution analysis of two-dimensional cylinder wake, two-dimensional decaying isotropic turbulence, and three-dimensional turbulent channel flow. In the concluding remarks, we summarize on findings from a range of regression-type problems considered herein.

本文言語English
ページ(範囲)497-519
ページ数23
ジャーナルTheoretical and Computational Fluid Dynamics
34
4
DOI
出版ステータスPublished - 2020 8月 1

ASJC Scopus subject areas

  • 計算力学
  • 凝縮系物理学
  • 工学(全般)
  • 流体および伝熱

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