2018/07/27 by Giovanni Iacobello, Stefania Scarsoglio, J. G. M. Kuerten +1
Earth and Planetary Sciences · Economics, Econometrics and Finance · Engineering · Mathematics · Physics and Astronomy · #Aerospace engineering #Artificial intelligence #Channel (broadcasting) #Characterization (materials science) #Classical mechanics #Complex Systems and Time Series Analysis #Computer science #Engineering #Flow (mathematics) #Fluid Dynamics and Turbulent Flows #Kinematics #Mathematics #Mechanics #Meteorological Phenomena and Simulations #Open-channel flow #Optics #Perspective (graphical) #Physics #Range (aeronautics) #Spatial analysis #Spatial correlation #Statistical physics #Telecommunications #Topology (electrical circuits) #Turbulence #Vector field #physics.flu-dyn
paper · pdf · doi:10.1103/physreve.98.013107
published as Physical Review E 98.1 (2018): 013107
openalex publication_date 2018/07/27 · arxiv created 2018/08/30 · arxiv updated 2018/08/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
A network-based analysis of a turbulent channel flow numerically solved at Reτ=180 is proposed as an innovative perspective for the spatial characterization of the flow field. Two spatial networks corresponding to the streamwise and wall-normal velocity components are built, where nodes represent portions of volume of the physical domain. For each network, links are active if the correlation coefficient of the corresponding velocity component between pairs of nodes is sufficiently high, thus unveiling the strongest kinematic relations. Several network measures are studied in order to explore the interrelations between nodes and their neighbors. Specifically, long-range links are localized between near-wall regions and associated with the temporal persistence of coherent patterns, namely high and low speed streaks. Furthermore, long-range links play a crucial role as intermediary for the kinematic information flow, as emerges from the analysis of indirect connections between nodes. The proposed approach provides a framework to investigate spatial structures of the turbulent dynamics, showing the full potential of complex networks. Although the network analysis is based on the two-point correlation, it is able to advance the level of information, by exploiting the texture created by active links in all directions. Based on the observed findings, the current approach can pave the way for an enhanced spatial interpretation of the turbulence dynamics.