Scale-Dependent Proper Orthogonal Decomposition to Study Scale Interaction in Turbulent Pipe Flow Data

Scale-dependent proper orthogonal decomposition (SD-POD) is a modal analysis technique used to extract multiple sets of orthogonal modes from a scalar or vector field based on the phase of a large-scale structure present in the flow field. To evaluate structure interaction in turbulent pipe flow, SD-POD was applied to particle image velocimetry data collected at the recirculating water pipe facility at Princeton University (Saxton-Fox et al., 2019). Two sets of orthogonal modes for small-scale structures were computed based on two phases of the large scale structure. Non-linear interaction between large- and small-scale structure in fluids causes the energy of the small-scale structures to be influenced by the large-scale structures (Mathis et al., 2009, Chung et al., 2010). In this talk, the SD-POD algorithm will be reviewed along with results from the turbulent pipe flow data. The pipe flow data results show the distance from the wall where small-scale modes are the strongest changes depending on the phase of the large-scale structure.