<p>Coherent structures (CSs) are characteristic features of turbulent flows, extensively studied but still ill-defined and poorly understood. This Review focuses on CS interpretations in wall turbulence, framing CSs as manageable components of turbulence. We discuss coherence and causality, covering foundational perspectives such as vortical CSs and ‘dynamic eddies’, alongside more recent approaches, including modal decompositions and causal coherence. Although there is no single universally accepted definition of CSs, we review diverse interpretations, highlighting differences, strengths and limitations, and discussing when one interpretation may be preferred over another. We clarify concepts often used interchangeably (vortices and CSs) and distinguish between modelling-based (attached and detached eddies) and observational (large-scale and very-large-scale motions) representations of CSs. Finally, we outline open questions and challenges, such as uncovering causal relationships among different CSs and leveraging machine learning for their detection and interpretation.</p>

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Interpreting coherence in wall turbulence

  • Daniele Massaro,
  • Fazle Hussain

摘要

Coherent structures (CSs) are characteristic features of turbulent flows, extensively studied but still ill-defined and poorly understood. This Review focuses on CS interpretations in wall turbulence, framing CSs as manageable components of turbulence. We discuss coherence and causality, covering foundational perspectives such as vortical CSs and ‘dynamic eddies’, alongside more recent approaches, including modal decompositions and causal coherence. Although there is no single universally accepted definition of CSs, we review diverse interpretations, highlighting differences, strengths and limitations, and discussing when one interpretation may be preferred over another. We clarify concepts often used interchangeably (vortices and CSs) and distinguish between modelling-based (attached and detached eddies) and observational (large-scale and very-large-scale motions) representations of CSs. Finally, we outline open questions and challenges, such as uncovering causal relationships among different CSs and leveraging machine learning for their detection and interpretation.