发表机构
ONERA; Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València; University of Michigan; KTH Royal Institute of Technology（法国国家航空航天研究院; 巴伦西亚理工大学纯数学与应用数学大学研究所; 密歇根大学; 瑞典皇家理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究用可解释深度学习结合Shapley值归因，揭示机翼湍流中传统范式未关注的三维高低速流体配对结构，为湍流控制提供新方法。
AI 中文摘要
飞机机翼湍流中相干结构的三维组织形式仍不明确，这限制了我们降低燃油消耗的能力。本研究采用可解释人工智能，通过预测流场演化而非经典预定义运动学准则来表征这些结构。在训练深度神经网络预测流场短期演化后，我们使用Shapley值归因方法识别出相关性最高的流场区域。研究发现，当流场向机翼后缘减速时，预测重要性从近壁低速结构转移到高低速流体的三维配对结构。这些配对结构不属于任何单一经典结构族，会逐渐在动态相关流场中占据主导，多数沿展向并排分布，包围着具有最强速度跃变的近垂直动量界面，其几何结构在粘性单位下保持不变，体积在接近分离时扩大。研究结果揭示了传统范式忽略的预测性组织形式，为湍流控制开辟了新途径。
英文摘要
The three-dimensional organization of coherent structures in the turbulent flow over aircraft wings remains elusive, limiting our ability to reduce fuel consumption. Here, we use explainable artificial intelligence to characterize these structures by predicted flow evolution rather than classical predefined kinematic criteria. After training a deep neural network to predict the short-term evolution of the flow, we identify the highest-relevance flow regions using Shapley-value attribution methods. We have found that as the flow decelerates towards the trailing edge, predictive importance shifts from near-wall low-speed structures to three-dimensional pairs of high- and low-speed fluid regions. Matching no single classical structure family, these pairs progressively dominate the dynamically relevant flow. Most stand spanwise side-by-side, enclosing a near-vertical momentum interface with the strongest velocity jump. Their geometry remains invariant in viscous units while their volume expands when approaching separation. Our results reveal a predictive organization that traditional paradigms overlook, opening new ways to control turbulent flows.
