Cremades Andrés, Hoyas Sergio, Deshpande Rahul, Quintero Pedro, Lellep Martin, Lee Will Junghoon, Monty Jason P, Hutchins Nicholas, Linkmann Moritz, Marusic Ivan, Vinuesa Ricardo
FLOW, Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, SE-100 44, Sweden.
CMT-Motores Térmicos, Universitat Politècnica de València, Camino de Vera s/n, Valencia, 46022, Spain.
Nat Commun. 2024 May 13;15(1):3864. doi: 10.1038/s41467-024-47954-6.
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 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 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.
尽管壁面湍流在科学技术方面具有重要意义，但它仍是经典物理学中一个尚未解决的问题，需要新的视角来解决。关键策略之一是研究流场中含能相干结构之间的相互作用。本研究使用一种可解释的深度学习方法来探索这种相互作用。从湍流槽道流模拟中获得的瞬时速度场通过U-net架构用于预测未来时刻的速度场。基于预测的流场，我们使用SHapley Additive exPlanations（SHAP）的博弈论算法评估每个结构对该预测的重要性。这项工作的结果与文献中先前的观察结果一致，并通过揭示流场中最重要的结构不一定是对雷诺剪应力贡献最大的结构来扩展了这些结果。我们还将该方法应用于一个实验数据库，在那里我们可以根据结构的重要性得分来识别它们。这个框架有可能揭示壁面湍流的许多基本现象，包括新的流动控制策略。
