The precessing vortex core (PVC) oscillation is a commonly observed self-excited flow instability associated with the precession of the vortex breakdown bubble around the flow axis in swirl flows. The flow region where internal feedback forcing sustains the PVC is called the wavemaker. Linear stability analysis (LSA) methods, in conjunction with large-eddy simulation (LES), can be used to identify a wavemaker. Here, we assess a data-driven approach using complex network analysis (CNA) to identify the wavemaker of a nominally axisymmetric turbulent swirl nozzle flow using unsteady data from a prior LES for which wavemaker predictions from LSA are available. Networks are constructed using time-resolved velocity data from the LES. Internodal connectivity is defined using either correlation or mutual information, and weighted closeness centrality is used to identify network hubs in each case. These hubs identify the wavemaker when mapped back to physical space. Both networks predict the position of the wavemaker in good agreement with the prior LSA result, with the mutual information network providing a closer match to the wavemaker’s spatial extent. These results show that CNA can be applied reliably to extract wavemaker information from time-series data of turbulent swirl flow.
Identification of Wavemaker Region in Swirling Flows Using Complex Network Analysis
AIAA Journal ; 1-12
2025-02-28
Conference paper , Article (Journal)
Electronic Resource
English
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