An Eulerian time filtering technique to study large-scale transient flow phenomena

Abstract

Unsteady fluctuating velocity fields can contain large-scale periodic motions with frequencies well separated from those of turbulence. Examples are the wake behind a cylinder or the processing vortex core in a swirling jet. These turbulent flow fields contain large-scale, low-frequency oscillations, which are obscured by turbulence, making it impossible to identify them. In this paper, we present an Eulerian time filtering (ETF) technique to extract the large-scale motions from unsteady statistical non-stationary velocity fields or flow fields with multiple phenomena that have sufficiently separated spectral content. The ETF method is based on non-causal time filtering of the velocity records in each point of the flow field. It is shown that the ETF technique gives good results, similar to the ones obtained by the phase-averaging method. In this paper, not only the influence of the temporal filter is checked, but also parameters such as the cut-off frequency and sampling frequency of the data are investigated. The technique is validated on a selected set of time-resolved stereoscopic particle image velocimetry measurements such as the initial region of an annular jet and the transition between flow patterns in an annular jet. The major advantage of the ETF method in the extraction of large scales is that it is computationally less expensive and it requires less measurement time compared to other extraction methods. Therefore, the technique is suitable in the startup phase of an experiment or in a measurement campaign where several experiments are needed such as parametric studies.