Explaining wall-bounded turbulence through deep learning
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Despite its great scientific and technological importance, wall-bounded turbulence is an unresolved problem that requires new perspectives to be tackled. One of the key strategies has been to study interactions among the 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 in a turbulent channel is used to predict the velocity field in time through a convolutional neural network. The predicted flow is used to assess the importance of each structure for this prediction using a game-theoretic algorithm (SHapley Additive exPlanations). This work provides results in agreement with previous observations in the literature and extends them by quantifying the importance of the Reynolds-stress structures, finding a causal connection between these structures and the dynamics of the flow. The process, based on deep-learning explainability, has the potential to shed light on numerous fundamental phenomena of wall-bounded turbulence, including the objective definition of new types of flow structures.
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