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. 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 quantifying the importance of the Reynolds-stress structures, finding a 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.

翻译：尽管壁面湍流具有重要的科学和技术意义，但它仍是一个需要新视角来解决的未解问题。关键策略之一一直是研究流场中相干结构之间的相互作用。本研究首次采用可解释深度学习方法探索了此类相互作用。通过卷积神经网络利用湍流通道中的瞬时速度场预测时间上的速度场。基于预测流场，我们使用博弈论算法SHapley Additive exPlanations (SHAP)评估每个结构对该预测的重要性。本研究结果与文献中的先前观测一致，并通过量化雷诺应力结构的重要性进行了扩展，发现了这些结构与流场动力学之间的联系。基于深度学习可解释性的这一过程，有望揭示壁面湍流的许多基本现象，包括新类型流场结构的客观定义。