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Combustion is a major source of energy production for a wide range of applications to meet the
increasing demand for power. In recent times, there has been a drive towards clean energy and
lower emissions. Towards this goal, engines are operated under fuel lean conditions, where the
temperature of the products is low, thereby reducing the production of oxides of nitrogen, which
are harmful. However, the development and operation of such engines are marred by the
occurrence of combustion instability (also known as thermoacoustic instability) and blowout of
flame.
Inherent fluctuations in the flow get amplified when the unsteady heat release rate from
combustion interacts in phase with the acoustic field of the combustion chamber. Consequently,
detrimental, high-amplitude, pressure oscillations known as thermoacoustic instability occurs in
combustion systems. These oscillations often cause losses in billions of dollars to the engine
companies. Meanwhile, the blowout of flame is another dangerous problem which can even cause
sudden descent of an airplane, in addition to the financial losses. These detrimental thermoacoustic
instability and flame blowout occur in the system when combustors are operated in a fuel-lean
condition. However, clean combustion as well cannot be avoided to meet the stringent emission
norms. Hence, an understanding of the transition to thermoacoustic instability and blowout is
absolutely critical.
Traditionally, thermoacoustic systems are analyzed from a reductionist approach which
attempts to analyze a complex system in terms of its constituent elements. Recently, it was shown
that the combustion noise and the near blowout dynamics display multifractal characteristics. The
presence of multifractality in the combustion dynamics is a reflection of the complexity of the thermoacoustic systems. The traditional reductionist approach fails to explain the complex
behaviours in the thermoacoustic systems.
In the present thesis, the complex behaviours in the dynamics of thermoacoustic systems are
investigated in the framework of complex networks. First, the pattern in the dynamics of the
combustion noise generated during the stable operation of the combustor is investigated. The
unsteady pressure data from a backward-facing step combustor is converted into obtain complex
networks using the visibility condition. The scale invariance of combustion noise in a confinement
is hard to discern from the frequency spectra due to the presence of low-amplitude peaks, arising
from the coupling of combustion noise with the confinement modes. The complex network
representation reveals the scale invariance of combustion noise as scale-free structure in the
topology of the complex network. The dynamics of the combustion noise is mapped as nodes and
links between them and the power-law behavior in the distribution of links in the network is a clear
reflection of the scale invariant property of the combustion noise generated in a turbulent
environment.
Further, the structure of the complex network during thermoacoustic instability possess regular
topology that represent order. The transition to thermoacoustic instability from combustion noise
is reflected as a transition from scale-free to order in the networks topology. The measures for
quantifying the topological features of the networks such as clustering coefficient, characteristic
path length, network diameter and global efficiency are calculated at each operating conditions
during the transition from combustion noise to thermoacoustic instability. These network measures
change significantly well before the onset of thermoacoustic instability and can be used as the
precursors to thermoacoustic instability.
The transition in the system dynamics from combustion noise to the onset flame blowout is
investigated in the framework of complex networks. The regular structure in the complex networks
during thermoacoustic instability transition to scale-free structure at the onset of blowout. The
network properties are computed and used as the early warning measures to the onset of blowout. The transition to thermoacoustic instability and blowout from the stable operation happened
via intermittency. In order to investigate the physical reasons for such transitions in thermoacoustic
systems, we investigated the intermittent dynamics that presages the onset of thermoacoustic
instability and blowout in a turbulent lifted jet flame combustor. The simultaneous measurement
of acoustic pressure, chemiluminescence images and Mie scattering images are performed in order
to characterize the acoustics-flame-flow interactions during intermittency.
The intermittent dynamics prior to the onset of thermoacoustic instability occurs due to the
alternating (either positively or negatively) coupled interaction of the flame, flow and duct acoustics. In contrast, the intermittency that presages the onset of blowout is caused by the interplay between the blowout precursor events and the driving of high-amplitude pressure oscillations as the flame propagate towards the fuel tube. Alternatively, the commonality between the intermittency prior to thermoacoustic instability and blowout is investigated using first return maps and recurrence plots.

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... Such an investigation is significant since complexity is a characteristic of the speech production system (alike other biological systems such as the blood circulatory system, heart, brain, etc.) with many interacting subsystems involved. A complex network or graph representation of the time-series corresponding to speech data can provide a deeper understanding of the hidden patterns in the data [39]. Precisely, the complex network can give a visual representation of the underlying dynamics through its connection pattern. ...

The nonlinear interaction of the subsystems of speech production system brings in dynamical transitions from unvoiced to voiced speech and vice versa during continuous speech production. The characterization or detection of the dynamic variation underlying the production of voiced and unvoiced speech is a fundamental step to many speech technology applications. In the present work, we study the complex behavior in the dynamics of voiced and unvoiced speech production using the framework of complex networks for the first time. The time-series corresponding to speech utterance is converted into complex network or graph using recurrence network approach to study the dynamical transitions. We find that the resultant network topology resembles the structure of the attractor representing the dynamics of the speech production system. We demonstrate the presence of either scale-free or random nature of the fluctuations from the recurrence network corresponding to unvoiced speech. Further, we show the absence of scale-free nature in the recurrence network corresponding to voiced speech. The transitions from unvoiced to voiced speech are then captured as variations of the network measures such as average clustering coefficient and characteristic path length. The performance comparison results show that the complex network measures estimated from recurrence networks provide comparable accuracy in detecting voiced/unvoiced speech with that of the state-of-the-art methods.

We investigate the physics of intermittent dynamics that presages the onset of impending combustion instability and blowout in a turbulent lifted jet flame combustor. We observe that the transition from combustion noise to combustion instability happens via intermittency while varying the relative location of the burner inside a confinement as a bifurcation parameter. For further change in burner location past the condition of combustion instability, we again observe the intermittency before the flame blows out. We show that the coupled interaction of flow, flame dynamics and combustion chamber acoustics is the physical reason for the occurrence of intermittency prior to combustion instability. In contrast, intermittent dynamics that presages blowout occurs due to the interplay between flame blowout precursor events and the driving of high-amplitude oscillations as the flame propagates towards the burner.

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 mea-sures, recently developed in the complex network theory, could by means of this new approach characterize time series from a new point of view.
From time series to complex networks: The visibility graph. Available from: https://www.researchgate.net/publication/301232812_From_time_series_to_complex_networks_The_visibility_graph [accessed Jun 29, 2017].

Historically, stoichiometric combustion has tended to be regarded as the norm, and this is approximately so for fuel jet flames. Another reason is that slightly richer than stoichiometric mixtures give maximum power in gasoline engines. With growing concern about pollutant emissions, including NOx arising from the high-temperature three-way catalytic converters utilized such temperatures to oxidize unburned hydrocarbons. But increasing concerns about NOx emissions led to lower-temperature, lean combustion, burners and engines. The advantages of this are not confined to reductions in emissions, as their thermodynamic properties give increased engine efficiencies. This chapter presents necessary fundamental aspects of flame propagation, flame quenching, flameless reaction, and the different modes of autoignition.

The identification of flow pattern is a basic and important issue in multiphase systems. Because of the complexity of phase interaction in gas-liquid two-phase flow, it is difficult to discern its flow pattern objectively. In this paper, we make a systematic study on the vertical upward gas-liquid two-phase flow using complex network. Three unique network construction methods are proposed to build three types of networks, i.e., flow pattern complex network (FPCN), fluid dynamic complex network (FDCN), and fluid structure complex network (FSCN). Through detecting the community structure of FPCN by the community-detection algorithm based on K -mean clustering, useful and interesting results are found which can be used for identifying five vertical upward gas-liquid two-phase flow patterns. To investigate the dynamic characteristics of gas-liquid two-phase flow, we construct 50 FDCNs under different flow conditions, and find that the power-law exponent and the network information entropy, which are sensitive to the flow pattern transition, can both characterize the nonlinear dynamics of gas-liquid two-phase flow. Furthermore, we construct FSCN and demonstrate how network statistic can be used to reveal the fluid structure of gas-liquid two-phase flow. In this paper, from a different perspective, we not only introduce complex network theory to the study of gas-liquid two-phase flow but also indicate that complex network may be a powerful tool for exploring nonlinear time series in practice.

Recently, M. Murugesan and R. I. Sujith ("Combustion Noise is Scale-Free: Transition from Scale-Free to Order at the Onset of Thermoacoustic Instability," Journal of Fluid Mechanics, Vol. 32, June 2015, pp. 225-245) showed that the transition from combustion noise to thermoacoustic instability can be represented as a change from a scale-free to a regular structure in the topology of complex networks. These topological changes of the complex networks during this transition can be quantified by calculating the network properties. In this paper, the variation of network properties, namely, clustering coefficient, characteristic path length, network diameter, and global efficiency is presented as the system dynamics undergoes transition from combustion noise to thermoacoustic instability. These network properties capture the change in system dynamics well before the rise in pressure amplitude levels in the combustors. These network properties can be used as early warning signals to detect the onset of impending thermoacoustic instabilities. ©2015 by Meenatchidevi Murugesan and R. I. Sujith. Published by the American Institute of Aeronautics and Astronautics, Inc.

Clean, sustainable energy systems are a preeminent issue of our time. Most projections indicate that combustion-based energy conversion systems will continue to be the predominant approach for the majority of our energy usage. Unsteady combustor issues pose the key challenges associated with the development of clean, high-efficiency combustion systems such as those used for power generation, heating, or propulsion applications. This comprehensive study is unique in that it treats this subject in a systematic manner. Although this book focuses on unsteady combusting flows, it places particular emphasis on the system dynamics that occur at the intersection of the combustion, fluid mechanics, and acoustic disciplines. Individuals with a background in fluid mechanics and combustion will find this book to be an incomparable study that synthesizes these fields into a coherent understanding of the intrinsically unsteady processes in combustors.

Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mech-anisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.