Identifying regions of importance in wall-bounded turbulence through explainable deep learning
This work addresses the unresolved challenge of understanding turbulence interactions for researchers in fluid dynamics, offering incremental insights through a novel application of explainable AI.
The study tackled the problem of identifying important coherent structures in wall-bounded turbulence by applying an explainable deep-learning method, revealing that the most important structures are not necessarily those with the highest Reynolds shear stress contribution and identifying new structures in experimental data.
Despite its great scientific and technological importance, wall-bounded turbulence is an unresolved problem in classical physics that requires new perspectives to be tackled. One of the key strategies has been to study interactions among the energy-containing coherent structures in the flow. Such interactions are explored in this study for the first time using an explainable deep-learning method. The instantaneous velocity field obtained from a turbulent channel flow simulation is used to predict the velocity field in time through a U-net architecture. Based on the predicted flow, we assess the importance of each structure for this prediction using the game-theoretic algorithm of SHapley Additive exPlanations (SHAP). This work provides results in agreement with previous observations in the literature and extends them by revealing that the most important structures in the flow are not necessarily the ones with the highest contribution to the Reynolds shear stress. We also apply the method to an experimental database, where we can identify completely new structures based on their importance score. This framework has the potential to shed light on numerous fundamental phenomena of wall-bounded turbulence, including novel strategies for flow control.