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.

翻译：尽管具有重大的科学和技术意义，壁面湍流仍是经典物理学中一个尚未解决的问题，需要新的视角来应对。关键策略之一一直是研究流动中含能相干结构之间的相互作用。本研究首次利用可解释深度学习方法探索此类相互作用。通过U-net架构，使用从湍流通道流模拟中获得的瞬时速度场来预测时间上的速度场。基于预测的流场，我们利用博弈论算法SHapley Additive exPlanations (SHAP)评估每个结构对此预测的重要性。这项工作提供的结论与文献中的先前观察一致，并进一步揭示，流动中最重要的结构不一定是对雷诺剪应力贡献最大的结构。我们还将该方法应用于实验数据库，根据重要性评分识别出全新的结构。该框架有潜力阐明壁面湍流的诸多基本现象，包括创新的流动控制策略。