On the emergence of critical regions at the onset of thermoacoustic instability in a turbulent combustor

Abstract

We use complex network theory to investigate the dynamical transition from stable operation to thermoacoustic instability via intermittency in a turbulent combustor with a bluff body stabilized flame. A spatial network is constructed, representing each of these three dynamical regimes of combustor operation, based on the correlation between time series of local velocity obtained from particle image velocimetry. Network centrality measures enable us to identify critical regions of the flow field during combustion noise, intermittency, and thermoacoustic instability. We find that during combustion noise, the bluff body wake turns out to be the critical region that determines the dynamics of the combustor. As the turbulent combustor transitions to thermoacoustic instability, during intermittency, the wake of the bluff body loses its significance in determining the flow dynamics and the region on top of the bluff body emerges as the most critical region in determining the flow dynamics during thermoacoustic instability. The knowledge about this critical region of the reactive flow field can help us devise optimal control strategies to evade thermoacoustic instability.

On the emergence of large clusters of acoustic power sources at the onset of thermoacoustic instability in a turbulent combustor

“…The bluff body shape in figures 3(b) and (c) looks dissimilar due to the fact that in figure 3(b) the nodes are the pixels whereas in figure 3(c), the nodes are the interrogation windows used in the analysis of PIV. The size of the interrogation windows is 32 pixels × 32 pixels (Unni et al 2018). Hence, the area that a node occupies in figure 3(c) is bigger than that in figure 3(b).…”

“…A bandpass optical filter centred at 527 nm is mounted in front of the camera to filter the flame and other background luminosity. A detailed description of the analysis of PIV with the post-processing algorithms that are used to detect spurious vectors can be found in Unni et al (2018). Simultaneous acoustic pressure measurement, OH * chemiluminescence imaging and PIV were performed for different equivalence ratios during the intermittency route to thermoacoustic instability.…”

“…Also, the flame front network was resilient against a random removal of nodes but susceptible to a targeted removal of hubs, thus opening up the possibility of control of turbulent flames using network theory. Recently, Unni et al (2018) performed spatial network analysis, in a turbulent combustor with a bluff body stabilized flame, based on the correlation between the time series of local velocity obtained from particle image velocimetry (PIV). The authors identified the critical regions in the flow field using network centrality measures, during combustion noise, intermittency and thermoacoustic instability.…”

Multifractal analysis of flame dynamics during transition to thermoacoustic instability in a turbulent combustor

“…Recent studies indicate that the transition states prior to thermoacoustic instability exhibit rich dynamic behaviour (Gotoda et al 2011(Gotoda et al , 2015Nair, Thampi & Sujith 2014;Unni & Sujith 2015;Pawar et al 2017). A turbulent combustor exhibits the characteristics of a complex system during these states (Juniper & Sujith 2018;Unni et al 2018). Results from these studies indicate that there is a gradual emergence of periodicity from aperiodicity in the temporal dynamics ) and an emergence of order from disorder in the spatiotemporal dynamics during the transition from combustion noise to thermoacoustic instability via intermittency (Mondal, Unni & intermittency, bursts of high amplitude periodic fluctuations appear amidst epochs of low amplitude aperiodic fluctuations in an apparently random manner.…”

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

“…Although different research fields have taken advantage of network science during last decades (e.g., social, biological, or technological networks [15]), only recently complex networks have emerged as an effective framework also to study fluid flows. The main applications of network science to fluid flows involve the study of two-phase flows [16,17], turbulent jets [18,19], isotropic and wall-bounded turbulence [20][21][22], reacting flows [23,24], Lagrangian mixing [25,26] and geophysical flows [27,28]. Among several techniques that have been developed so far to study time-series by means of complex networks [29], the visibility graph approach [30] was here adopted since it is a simple but powerful tool to extract non-trivial insights into the non-linear process from which the time-series are obtained [29].…”