Classically studied coherent structures only paint a partial picture of wall-bounded turbulence

TL;DR

This paper introduces a data-driven deep learning approach combined with explainability techniques to objectively identify and analyze high-importance regions in wall-bounded turbulence, revealing insights beyond classical coherent structures.

Contribution

The study presents a novel methodology using deep learning and SHAP explainability to identify important flow regions, offering a new perspective beyond traditional coherent structures.

Findings

High-importance regions differ from classical structures.

SHAP analysis provides an objective importance measure.

The method reveals flow features not captured by traditional structures.

Abstract

For the last 140 years, the mechanisms of transport and dissipation of energy in a turbulent flow have not been completely understood. Previous research has focused on analyzing the so-called coherent structures, organized flow patterns characterized by their spatial coherence, lifespan and significant contribution to momentum and energy transfer. However, the connection between these structures and the flow development is still uncertain. Here, we show a data-driven methodology for objectively identifying high-importance regions. A deep-learning model is trained to predict a future state of the flow and the gradient-SHAP explainability algorithm is used to calculate the importance of each grid point. Finally, high-importance regions are computed using the SHAP data and are compared to the other coherent structures. The SHAP analysis provides an objective way to identify the regions of higher importance, which exhibit different levels of agreement with the classical structures without being completely related to any particular one.