Experimental investigation of vertical turbulent transport of a passive scalar in a boundary layer: Statistics and visibility graph analysis

Abstract

The dynamics of a passive scalar plume in a turbulent boundary layer is experimentally investigated via vertical turbulent transport time-series. Experimental data are acquired in a rough-wall turbulent boundary layer that develops in a recirculating wind tunnel set-up. Two source sizes in an elevated position are considered in order to investigate the influence of the emission conditions on the plume dynamics. The analysis is focused on the effects of the meandering motion and the relative dispersion of the plume with respect to its center of mass. First, classical statistics are investigated. We found that (in accordance with previous studies) the meandering motion is the main responsible for differences in the variance and intermittency, as well as the kurtosis and power spectral density, between the two source sizes. On the contrary, the mean and the skewness are slightly affected by the emission conditions. With the aim to characterize the temporal structure of the turbulent transport series, the visibility algorithm is exploited to carry out a complex network-based analysis. In particular, two network metrics -the average peak occurrence and the assortativity coefficient -are analysed, as they are able to capture the temporal occurrence of extreme events and their relative intensity in the series. The effects of the meandering motion and the relative dispersion of the plume are discussed in the view of the network metrics, revealing that a stronger meandering motion is associated with higher values of both the average peak occurrence and the assortativity coefficient. The network-based analysis advances the level of information of classical statistics, by characterizing the impact of the emission conditions on the temporal structure of the signals in terms of extreme events (namely, peaks and pits) and their relative intensity.In this way, complex networks provide -through the evaluation of network metrics -an effective tool for time-series analysis of experimental data. *

Identifying vortical network connectors for turbulent flow modification

“…2019 b ), wall turbulence (Iacobello, Scarsoglio & Ridolfi 2018 b ), mixing in turbulent channel flow (Iacobello et al. 2018 a , 2019 a , b ) and isotropic magnetohydrodynamic turbulence (Gürcan 2018). There have also been recent advances in using complex network analysis for turbulent flow control.…”

“…The induced velocity amongst vortical elements (Nair & Taira 2015), Lagrangian motion of fluid elements (Ser-Giacomi et al 2015;Hadjighasem et al 2016), oscillator-based representation of the energy fluctuations (Nair, Brunton & Taira 2018), time series of fluid-flow properties (Scarsoglio, Cazzato & Ridolfi 2017), triadic interactions in turbulence (Gürcan 2017;Gürcan, Li & Morel 2020) and the effects of perturbations on time-varying vortical flows (Yeh, Gopalakrishnan Meena & Taira 2020) have been studied using a network-theoretic framework. The formulations have been extended to characterize various turbulent flows, including two-dimensional isotropic turbulence (Taira, Nair & Brunton 2016), turbulent premixed flames and combustors (Godavarthi et al 2017;Singh et al 2017;Krishnan et al 2019b), wall turbulence (Iacobello, Scarsoglio & Ridolfi 2018b), mixing in turbulent channel flow (Iacobello et al 2018a(Iacobello et al , 2019a and isotropic magnetohydrodynamic turbulence (Gürcan 2018). There have also been recent advances in using complex network analysis for turbulent flow control.…”

Solution for the statistical moments of scalar turbulence

“…[38], the following acronyms are used for the source configurations: Concentration measurements were made with a fast flame ionization detector (FID) [40] with a sampling tube 0.3 m long, which allowed us to acquire concentration signals with a frequency of 800 Hz. The sampling frequency is sufficiently high to correctly capture the intermittency of the concentration signal in the near field [41]. The time series of concentration were measured for 300 s. To take into account of the drift induced by the recirculation of air in the wind tunnel, the background concentrations were recorded before and after acquiring any of the concentration time series.…”

Large-to-small scale frequency modulation analysis in wall-bounded turbulence via visibility networks

“…2020), wall-bounded turbulent flows (Liu, Zhou & Yuan 2010; Iacobello, Scarsoglio & Ridolfi 2018 b ), passive scalar plumes (Iacobello et al. 2018 a , 2019 a ) and turbulent combustors (Murugesan & Sujith 2015, 2016; Singh et al. 2017).…”