Near-wall regions in wall-bounded turbulent flows experience intermittent ejection of slow-moving fluid packets away from the wall and sweeps of faster moving fluid towards the wall. These extreme events play a central role in regulating the energy budget of the boundary layer, and are analyzed here with the help of a three-dimensional (3D) Convolutional Neural Network (CNN). A CNN is trained on Direct Numerical Simulation data from a periodic channel flow to deduce the intensity of such extreme events, and more importantly, to reveal contiguous three-dimensional salient structures in the flow that are determined autonomously by the network to be critical to the formation and evolution of ejection events. These salient regions, reconstructed using a multilayer Gradient-weighted Class Activation Mapping (GradCAM) technique proposed here, correlate well with bursting streaks and coherent fluid packets being ejected away from the wall. The focus on explainable interpretation of the network's learned associations also reveals that ejections are not associated with regions where turbulent kinetic energy (TKE) production reaches a maximum, but instead with regions that entail extremely low dissipation and a significantly higher tendency for positive TKE production than negative production. This is a key finding of the study, and indicates that CNNs can help reveal dynamically important three-dimensional salient regions using a single scalar-valued metric provided as the quantity of interest, which in the present case is the ejection intensity. While the current work presents an alternate means of analyzing nonlinear spatial correlations associated with near-wall bursts, the framework presented is sufficiently general so as to be extendable to other scenarios where the underlying spatial dynamics are not known a-priori.
Renormalized flow theory of wave turbulence: Kolmogorov-Zakharov spectra as emergent asymptotic states
Universidad Complutense de Madrid（马德里康普顿斯大学）；Instituto de Investigación Sanitaria Hospital 12 de Octubre (Imas12)（马德里12月医院卫生研究所）；Universidad Autónoma Metropolitana, Unidad Cuajimalpa（墨西哥自治都市大学夸希马尔帕分校）
Nonhomogeneous elastic turbulence in the two-dimensional Taylor-Couette flow
Univ. Lille, CNRS, ONERA, Arts et Métiers Institute of Technology, Centrale Lille（里尔大学、法国国家科学研究中心、法国航空国防实验室、巴黎国立高等工艺学院、里尔中央理工学院）；Univ. Lille（里尔大学）；Laboratoire de Thermique et Energie de Nantes, CNRS, Nantes Université（南特热能与实验室、法国国家科学研究中心、南特大学）
Unveiling wing turbulence dynamics through explainable deep learning
ONERA（法国国家航空航天研究院）；Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València（巴伦西亚理工大学纯数学与应用数学大学研究所）；University of Michigan（密歇根大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）
Objective barriers to the transport of dynamically active vector fields
我们推导出一个理论的材料表面，最大限度地抑制扩散运输的动态活跃的矢量场，如线动量，角动量或涡量，在一般的流体流动。这些特殊的材料表面（\emph{Lagrangian active barriers}）提供了基于物理学的、与边界无关的动态活动相干结构的边界。我们发现，拉格朗日主动障碍演变从一个相关的稳定和不可压缩的\emph{barrier equation}，其右手边是时间平均拉回的动态主动矢量场的演化方程中的粘性应力项的不变表面。这些屏障的瞬时极限标志着动态活动矢量场短期扩散传输的目标\emph{Eulerian active barriers}。我们得到，在非定常贝尔特拉米流，拉格朗日和欧拉主动障碍完全符合纯平流运输障碍边界观察到的相干结构。在更一般的流动中，可以通过将拉格朗日相干结构（LCS）诊断（诸如有限时间李雅普诺夫指数和极旋转角）应用于适当的活动障碍方程来识别活动障碍。与它们的被动对应物相比，这些\emph{active LCS diagnostics}不需要显著的流体颗粒分离，因此从时间上较短的速度数据集提供实质上更高分辨率的拉格朗日和欧拉相干结构边界。我们说明了这些结果和他们的物理解释二维，均匀，各向同性湍流和三维湍流槽道流。
Hydrodynamics of Dense Active Fluids: Turbulence-Like States and the Role of Advected Activity
International Centre for Theoretical Sciences, Tata Institute of Fundamental Research（国际理论科学中心，塔塔基础研究所）；Department of Mechanical Engineering, Indian Institute of Technology Kanpur（坎普尔印度理工学院机械工程系）
Weak Dominant Balance for Robust Identification of Dynamically Consistent Fluid Flow Structure
Department of Mechanical Engineering, University of Washington, Seattle, WA, USA（华盛顿大学机械工程系）；AI Institute in Dynamic Systems, University of Washington, Seattle, WA, USA（动态系统人工智能研究所）；Lynn Booth and Kent Kresa Department of Aerospace, California Institute of Technology, Pasadena, CA, USA（加州理工学院航空航天系）；Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, CA, USA（加州大学洛杉矶分校机械与航空航天工程系）；Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI, USA（密歇根大学航空航天工程系）
