Identifying large-scale coherent structures in homogeneous isotropic turbulence is crucial for advancing the understanding of turbulent phenomena, including intermittency and energy transfer. However, the current knowledge and statistical characterization of these structures remain limited due to the absence of efficient and consistent identification techniques. This thesis presents an automated methodology based on the HDBSCAN clustering algorithm to identify large-scale coherent structures.
The method successfully detects coherent large-scale structures, characterized by a quasi-uniform velocity direction. It has been tested across a wide range of Reynolds numbers, providing insights into their spatial organization and interactions. High dissipation regions were observed to occur between neighbouring structures, indicating a direct link between large-scale motions and small-scale intermittency through shearing mechanisms. Additionally, the methodology was extended to time-resolved datasets, enabling temporal tracking and analysis of the evolution of these structures in physical space.
This thesis provides a robust and efficient framework for coherent large-scale structure identification in homogeneous isotropic turbulence and provides new insights into their statistical properties, dynamics, and role in turbulent energy transfer.