Recurrence networks to study dynamical transitions in a turbulent combustor
TL;DR: It is demonstrated that the measures derived from recurrence network can be used as tools to capture the transitions in the turbulent combustor and also as early warning measures for predicting impending thermoacoustic instability and blowout.
Abstract: Thermoacoustic instability and lean blowout are the major challenges faced when a gas turbine combustor is operated under fuel lean conditions. The dynamics of thermoacoustic system is the result of complex nonlinear interactions between the subsystems—turbulent reactive flow and the acoustic field of the combustor. In order to study the transitions between the dynamical regimes in such a complex system, the time series corresponding to one of the dynamic variables is transformed to an e-recurrence network. The topology of the recurrence network resembles the structure of the attractor representing the dynamics of the system. The transitions in the thermoacoustic system are then captured as the variation in the topological characteristics of the network. We show the presence of power law degree distribution in the recurrence networks constructed from time series acquired during the occurrence of combustion noise and during the low amplitude aperiodic oscillations prior to lean blowout. We also show the ab...
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TL;DR: In this article, the authors discuss various prognosis and mitigation strategies for thermo-acoustic instability based on complex system theory in turbulent combustors, where the authors view the thermoacoustic system in a turbulent combustor as a complex system and the dynamics exhibited by the system is perceived as emergent behaviors of this complex system.
Abstract: Thermoacoustic instability in turbulent combustors is a nonlinear phenomenon resulting from the interaction between acoustics, hydrodynamics, and the unsteady flame Over the years, there have been many attempts toward understanding, prognosis, and mitigation of thermoacoustic instabilities Traditionally, a linear framework has been used to study thermoacoustic instability In recent times, researchers have been focusing on the nonlinear dynamics related to the onset of thermoacoustic instability In this context, the thermoacoustic system in a turbulent combustor is viewed as a complex system, and the dynamics exhibited by the system is perceived as emergent behaviors of this complex system In this paper, we discuss these recent developments and their contributions toward the understanding of this complex phenomenon Furthermore, we discuss various prognosis and mitigation strategies for thermoacoustic instability based on complex system theory
88 citations
TL;DR: An experimental study on the characterization of dynamic behavior of flow velocity field during thermoacoustic combustion oscillations in a turbulent confined combustor from the viewpoints of statistical complexity and complex-network theory, involving detection of a precursor of thermoafiltration oscillations.
Abstract: We present an experimental study on the characterization of dynamic behavior of flow velocity field during thermoacoustic combustion oscillations in a turbulent confined combustor from the viewpoints of statistical complexity and complex-network theory, involving detection of a precursor of thermoacoustic combustion oscillations. The multiscale complexity-entropy causality plane clearly shows the possible presence of two dynamics, noisy periodic oscillations and noisy chaos, in the shear layer regions (1) between the outer recirculation region in the dump plate and a recirculation flow in the wake of the centerbody and (2) between the outer recirculation region in the dump plate and a vortex breakdown bubble away from the centerbody. The vertex strength in the turbulence network and the community structure of the vorticity field can identify the vortical interactions during thermoacoustic combustion oscillations. Sequential horizontal visibility graph motifs are useful for capturing a precursor of themoacoustic combustion oscillations.
51 citations
TL;DR: A possible asymmetric bidirectional coupling between q ˙ ' and p ' is observed to exert a stronger influence on p ' than vice versa, and the directional property of the network measure, namely, cross transitivity is used to analyze the type of coupling existing between the acoustic field and the heat release rate fluctuations.
Abstract: Thermoacoustic instability is a result of the positive feedback between the acoustic pressure and the unsteady heat release rate fluctuations in a combustor. We apply the framework of the synchronization theory to study the coupled behavior of these oscillations during the transition to thermoacoustic instability in a turbulent bluff-body stabilized gas-fired combustor. Furthermore, we characterize this complex behavior using recurrence plots and recurrence networks. We mainly found that the correlation of probability of recurrence ( C P R), the joint probability of recurrence ( J P R), the determinism ( D E T), and the recurrence rate ( R R) of the joint recurrence matrix aid in detecting the synchronization transitions in this thermoacoustic system. We noticed that C P R and D E T can uncover the occurrence of phase synchronization state, whereas J P R and R R can be used as indices to identify the occurrence of generalized synchronization (GS) state in the system. We applied measures derived from joint and cross recurrence networks and observed that the joint recurrence network measures, transitivity ratio, and joint transitivity are useful to detect GS. Furthermore, we use the directional property of the network measure, namely, cross transitivity to analyze the type of coupling existing between the acoustic field ( p ′) and the heat release rate ( q ˙ ′) fluctuations. We discover a possible asymmetric bidirectional coupling between q ˙ ′ and p ′, wherein q ˙ ′ is observed to exert a stronger influence on p ′ than vice versa.Thermoacoustic instability is a result of the positive feedback between the acoustic pressure and the unsteady heat release rate fluctuations in a combustor. We apply the framework of the synchronization theory to study the coupled behavior of these oscillations during the transition to thermoacoustic instability in a turbulent bluff-body stabilized gas-fired combustor. Furthermore, we characterize this complex behavior using recurrence plots and recurrence networks. We mainly found that the correlation of probability of recurrence ( C P R), the joint probability of recurrence ( J P R), the determinism ( D E T), and the recurrence rate ( R R) of the joint recurrence matrix aid in detecting the synchronization transitions in this thermoacoustic system. We noticed that C P R and D E T can uncover the occurrence of phase synchronization state, whereas J P R and R R can be used as indices to identify the occurrence of generalized synchronization (GS) state in the system. We applied measures derive...
45 citations
TL;DR: The present review can boost future network-based research on turbulent and vortical flows, promoting the establishment of complex networks as a widespread tool for turbulence analysis.
Abstract: Turbulent and vortical flows are ubiquitous and their characterization is crucial for the understanding of several natural and industrial processes. Among different techniques to study spatio-temporal flow fields, complex networks represent a recent and promising tool to deal with the large amount of data on turbulent flows and shed light on their physical mechanisms. The aim of this review is to bring together the main findings achieved so far from the application of network-based techniques to study turbulent and vortical flows. A critical discussion on the potentialities and limitations of the network approach is provided, thus giving an ordered portray of the current diversified literature. The present review can boost future network-based research on turbulent and vortical flows, promoting the establishment of complex networks as a widespread tool for turbulence analysis.
39 citations
01 Jan 2021
TL;DR: In this article, a range of problems involving unsteady combustion such as thermoacoustic instability, flame blowout, fire propagation, reaction chemistry and flow flame interaction are discussed. But the focus is not on the effects of these processes on the actual combustion system.
Abstract: Reacting flow fields are often subject to unsteadiness due to flow, reaction, diffusion, and acoustics. Further, flames can also exhibit inherent unsteadiness caused by various intrinsic instabilities. Interaction between various unsteady processes across multiple scales often makes combustion dynamics complex. Characterizing such complex dynamics is essential to ensure the safe and reliable operation of high efficiency combustion systems. Tools from nonlinear dynamics and complex systems theory provide new perspectives to analyze and interpret the data from real systems. They could also provide new ways of monitoring and controlling combustion systems. We discuss recent advances in studying unsteady combustion dynamics using the tools from dynamical systems theory and complex systems theory. We cover a range of problems involving unsteady combustion such as thermoacoustic instability, flame blowout, fire propagation, reaction chemistry and flow flame interaction.
35 citations
References
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TL;DR: The aim of this work is to provide the readers with the know how for the application of recurrence plot based methods in their own field of research, and detail the analysis of data and indicate possible difficulties and pitfalls.
2,993 citations
TL;DR: In this article, a graphical tool for measuring the time constancy of dynamical systems is presented and illustrated with typical examples, and the tool can be used to measure the time complexity of a dynamical system.
Abstract: A new graphical tool for measuring the time constancy of dynamical systems is presented and illustrated with typical examples.
2,843 citations
TL;DR: A practical method to determine the minimum embedding dimension from a scalar time series that has the following advantages: does not contain any subjective parameters except for the time-delay for the embedding.
1,485 citations
TL;DR: A simple and fast computational method, the visibility algorithm, that converts a time series into a graph, which inherits several properties of the series in its structure, enhancing the fact that power law degree distributions are related to fractality.
Abstract: In this work we present a simple and fast computational method, the visibility algorithm, that converts a time series into a graph. The constructed graph inherits several properties of the series in its structure. Thereby, periodic series convert into regular graphs, and random series do so into random graphs. Moreover, fractal series convert into scale-free networks, enhancing the fact that power law degree distributions are related to fractality, something highly discussed recently. Some remarkable examples and analytical tools are outlined to test the method's reliability. Many different measures, recently developed in the complex network theory, could by means of this new approach characterize time series from a new point of view.
1,320 citations
TL;DR: Standard measures of structure in complex networks can be applied to distinguish different dynamic regimes in time series and application to human electrocardiograms shows that such statistical properties are able to differentiate between the sinus rhythm cardiograms of healthy volunteers and those of coronary care patients.
Abstract: We construct complex networks from pseudoperiodic time series, with each cycle represented by a single node in the network. We investigate the statistical properties of these networks for various time series and find that time series with different dynamics exhibit distinct topological structures. Specifically, noisy periodic signals correspond to random networks, and chaotic time series generate networks that exhibit small world and scale free features. We show that this distinction in topological structure results from the hierarchy of unstable periodic orbits embedded in the chaotic attractor. Standard measures of structure in complex networks can therefore be applied to distinguish different dynamic regimes in time series. Application to human electrocardiograms shows that such statistical properties are able to differentiate between the sinus rhythm cardiograms of healthy volunteers and those of coronary care patients.
682 citations