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

TL;DR

This paper develops a machine learning reduced order model combining CNN autoencoder and LSTM to efficiently simulate three-dimensional turbulent channel flows, capturing key dynamics with statistical accuracy.

Contribution

It introduces a novel ML-ROM framework that integrates CNN-AE and LSTM for modeling complex turbulence, demonstrating effective spatio-temporal flow predictions from low-dimensional latent space.

Findings

ML-ROM reproduces flow statistics consistent with DNS data.

The combined CNN-AE and LSTM effectively captures turbulence dynamics.

Performance depends on vortical structures and prediction time intervals.

Abstract

We investigate the applicability of 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 $Re_\tau=110$ in a minimum domain which can maintain coherent structures of turbulence. Training data set 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 CNN-AE and LSTM can represent the spatio-temporal 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.