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

TL;DR

This paper introduces a hierarchical autoencoder based on convolutional neural networks that effectively extracts ordered nonlinear modes from fluid flow data, applicable to both laminar and turbulent flows, aiding reduced order modeling.

Contribution

The paper presents a novel hierarchical autoencoder architecture that preserves mode energy order in nonlinear feature extraction from fluid flow fields.

Findings

Successfully extracted flow features from laminar and turbulent data

Maintained energy order of modes in the latent space

Demonstrated applicability to complex turbulent flows

Abstract

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 $Re_D$ = 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 $Re_{\tau}$ = 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 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.