Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow

Taichi Nakamura, Kai Fukami, Kazuto Hasegawa, Yusuke Nabae, Koji Fukagata

Research output: Contribution to journalArticlepeer-review

109 Citations (Scopus)


We investigate the applicability of the machine learning based reduced order model (ML-ROM) to three-dimensional complex flows. As an example, we consider a turbulent channel flow at the friction Reynolds number of R e τ = 110 in a minimum domain, which can maintain coherent structures of turbulence. Training datasets are prepared by direct numerical simulation (DNS). The present ML-ROM is constructed by combining a three-dimensional convolutional neural network autoencoder (CNN-AE) and a long short-term memory (LSTM). The CNN-AE works to map high-dimensional flow fields into a low-dimensional latent space. The LSTM is, then, utilized to predict a temporal evolution of the latent vectors obtained by the CNN-AE. The combination of the CNN-AE and LSTM can represent the spatiotemporal high-dimensional dynamics of flow fields by only integrating the temporal evolution of the low-dimensional latent dynamics. The turbulent flow fields reproduced by the present ML-ROM show statistical agreement with the reference DNS data in time-ensemble sense, which can also be found through an orbit-based analysis. Influences of the population of vortical structures contained in the domain and the time interval used for temporal prediction on the ML-ROM performance are also investigated. The potential and limitation of the present ML-ROM for turbulence analysis are discussed at the end of our presentation.

Original languageEnglish
Article number025116
JournalPhysics of Fluids
Issue number2
Publication statusPublished - 2021 Feb 1

ASJC Scopus subject areas

  • Computational Mechanics
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering
  • Fluid Flow and Transfer Processes


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