基于可解释深度学习揭示壁面摩擦生成的近壁循环
The near-wall cycle for skin-friction generation revealed through explainable deep learning
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中文总结 AI 辅助
该研究通过可解释深度学习训练神经网络预测湍流通道流的速度与切应力，结合SHAP揭示近壁成对结构的循环演化，重新定义了壁湍流近壁相干结构。
中文摘要 AI 辅助
壁湍流中的壁面摩擦由间歇性近壁运动产生，然而传统相干结构定义无法识别哪些单独事件会产生壁面切应力（即摩擦）。我们训练神经网络来预测湍流通道流中的未来速度场和壁面切应力分布，并使用SHAP导出的重要性图识别对每个预测最具影响力的输入区域。主导事件呈现为成对结构：上游与速度相关的区域对应向壁面的高动量运动，下游与摩擦相关的区域则标记其尾流中留下的增强壁面切应力。追踪这些对结构可揭示出一个周期性循环，包含生长、流向拉伸、衰减，以及偶尔分裂为新的壁面切应力生成事件。这些结果重新定义了近壁相干结构，不再依据流场的形态，而是依据它们对壁面切应力的作用。
英文摘要
Skin friction in wall-bounded turbulence is produced by intermittent near-wall motions, yet conventional coherent-structure definitions do not identify which individual events generate wall-shear stress, and thus friction. We train neural networks to predict the future velocity field and wall-shear-stress distribution in turbulent channel flow, and use SHAP-derived importance maps to identify the the input regions most influential for each prediction. The dominant events appear as paired objects: an upstream velocity-relevant region is associated with high-momentum motion toward the wall, while a downstream friction-relevant region marks the enhanced wall-shear-stress left in its wake. Tracking these pairs reveals a recurrent cycle of growth, streamwise elongation, decay, and occasional splitting into new wall-shear-producing events. These results redefine near-wall coherent structures not by what the flow looks like, but by what they do to wall-shear stress.
