Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data

Kai Fukami, Taichi Nakamura, Koji Fukagata

研究成果: Article査読

19 被引用数 (Scopus)

抄録

We propose a customized convolutional neural network based autoencoder called a hierarchical autoencoder, which allows us to extract nonlinear autoencoder modes of flow fields while preserving the contribution order of the latent vectors. As preliminary tests, the proposed method is first applied to a cylinder wake at ReD = 100 and its transient process. It is found that the proposed method can extract the features of these laminar flow fields as the latent vectors while keeping the order of their energy content. The present hierarchical autoencoder is further assessed with a two-dimensional y-z cross-sectional velocity field of turbulent channel flow at Reτ = 180 in order to examine its applicability to turbulent flows. It is demonstrated that the turbulent flow field can be efficiently mapped into the latent space by utilizing the hierarchical model with a concept of an ordered autoencoder mode family. The present results suggest that the proposed concept can be extended to meet various demands in fluid dynamics including reduced order modeling and its combination with linear theory-based methods by using its ability to arrange the order of the extracted nonlinear modes.

本文言語English
論文番号095110-1
ジャーナルPhysics of Fluids
32
9
DOI
出版ステータスPublished - 2020 9 1

ASJC Scopus subject areas

  • 計算力学
  • 凝縮系物理学
  • 材料力学
  • 機械工学
  • 流体および伝熱

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