Coupled interaction between unsteady flame dynamics and acoustic field in a turbulent combustor

Complex system approach to investigate and mitigate thermoacoustic instability in turbulent combustors

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

Early Detection of Thermoacoustic Combustion Instability Using a Methodology Combining Complex Networks and Machine Learning

Abstract: Early detection of thermoacoustic instabilities is of interest to both applied physicists and engineers, to avoid resonance leading to self-destruction of gas-based engines and turbines. This study shows how a combination of complex-network physics and machine learning can be used to detect a precursor of thermoacoustic instabilities, which can help to prevent the onset of a potentially destructive combustion-driven instability.

Spatiotemporal dynamics and early detection of thermoacoustic combustion instability in a model rocket combustor

Abstract: We numerically study the spatiotemporal dynamics and early detection of thermoacoustic combustion instability in a model rocket combustor using the theories of complex networks and synchronization. The turbulence network, which consists of nodes and vertexes in weighted networks between vortices, can characterize the complex spatiotemporal structure of a flow field during thermoacoustic combustion instability. The transfer entropy allows us to identify the driving region of thermoacoustic combustion instability. In addition to the order parameter, a phase parameter newly proposed in this study is useful for capturing the precursor of thermoacoustic combustion instability.

Dynamical systems approach to study thermoacoustic transitions in a liquid rocket combustor

Abstract: Liquid rockets are prone to large amplitude oscillations, commonly referred to as thermoacoustic instability. This phenomenon causes unavoidable developmental setbacks and poses a stern challenge to accomplish the mission objectives. Thermoacoustic instability arises due to the nonlinear interaction between the acoustic and the reactive flow subsystems in the combustion chamber. In this paper, we adopt tools from dynamical systems and complex systems theory to understand the dynamical transitions from a state of stable operation to thermoacoustic instability in a self-excited model multielement liquid rocket combustor based on an oxidizer rich staged combustion cycle. We observe that this transition to thermoacoustic instability occurs through a sequence of bursts of large amplitude periodic oscillations. Furthermore, we show that the acoustic pressure oscillations in the combustor pertain to different dynamical states. In contrast to a simple limit cycle oscillation, we show that the system dynamics switches between period-3 and period-4 oscillations during the state of thermoacoustic instability. We show several measures based on recurrence quantification analysis and multifractal theory, which can diagnose the dynamical transitions occurring in the system. We find that these measures are more robust than the existing measures in distinguishing the dynamical state of a rocket engine. Furthermore, these measures can be used to validate models and computational fluid dynamics simulations, aiming to characterize the performance and stability of rockets.

Analysis of the nonlinear dynamics of inter-cycle combustion variations in an ethanol fumigation-diesel dual-fuel engine

Abstract: The nonlinear dynamics of a combustion system in a modern common-rail dual-fuel engine has been studied. Using nonlinear dynamic data analysis (phase space reconstruction, recurrence plots, recurrence qualification analysis and wavelet analysis), the effect of ethanol fumigation on the dynamic behaviour of a combustion system has been examined at an engine speed of 2000 rpm with engine load rates of 50%, 75% and 100% and ethanol substitutions up to 40% (by energy) in 10% increments for each engine load. The results show that the introduction of ethanol has a significant effect on inter-cycle combustion variation (ICV) and the dynamics of the combustion system for all of the studied engine loads. For pure diesel mode and lower ethanol substitutions, the ICV mainly exhibits multiscale dynamics: strongly periodic and/or intermittent fluctuations. As the ethanol substitution is increased, the combustion process gradually transfers to more persistent low-frequency variations. At different engine loads, we can observe the bands with the strongest spectral power density that persist over the entire 4000 engine cycles. Compared to high engine loads (75% and 100%), the dynamics of the combustion system at a medium engine load (50%) was more sensitive to the introduction of ethanol. At higher ethanol substitutions, the increased ICV and the complexity of the combustion system at the medium load are attributable to the enhanced cooling caused by the excessive ethanol evaporation, while the low-frequency large-scale combustion fluctuations for the higher engine loads are likely caused by cyclic excitation oscillation during the transition of the combustion mode.

The Structure and Function of Complex Networks

Abstract: Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

Recurrence Plots of Dynamical Systems

Abstract: A new graphical tool for measuring the time constancy of dynamical systems is presented and illustrated with typical examples.

Practical method for determining the minimum embedding dimension of a scalar time series

Abstract: A practical method is proposed to determine the minimum embedding dimension from a scalar time series. It has the following advantages: (1) does not contain any subjective parameters except for the time-delay for the embedding; (2) does not strongly depend on how many data points are available; (3) can clearly distinguish deterministic signals from stochastic signals; (4) works well for time series from high-dimensional attractors; (5) is computationally efficient. Several time series are tested to show the above advantages of the method.

Dynamical assessment of physiological systems and states using recurrence plot strategies

Abstract: Physiological systems are best characterized as complex dynamical processes that are continuously subjected to and updated by nonlinear feedforward and feedback inputs. System outputs usually exhibit wide varieties of behaviors due to dynamical interactions between system components, external noise perturbations, and physiological state changes. Complicated interactions occur at a variety of hierarchial levels and involve a number of interacting variables, many of which are unavailable for experimental measurement. In this paper we illustrate how recurrence plots can take single physiological measurements, project them into multidimensional space by embedding procedures, and identify time correlations (recurrences) that are not apparent in the one-dimensional time series. We extend the original description of recurrence plots by computing an array of specific recurrence variables that quantify the deterministic structure and complexity of the plot. We then demonstrate how physiological states can be assessed by making repeated recurrence plot calculations within a window sliding down any physiological dynamic. Unlike other predominant time series techniques, recurrence plot analyses are not limited by data stationarity and size constraints. Pertinent physiological examples from respiratory and skeletal motor systems illustrate the utility of recurrence plots in the diagnosis of nonlinear systems. The methodology is fully applicable to any rhythmical system, whether it be mechanical, electrical, neural, hormonal, chemical, or even spacial.

Recurrence-plot-based measures of complexity and their application to heart-rate-variability data.

Abstract: The knowledge of transitions between regular, laminar or chaotic behaviors is essential to understand the underlying mechanisms behind complex systems. While several linear approaches are often insufficient to describe such processes, there are several nonlinear methods that, however, require rather long time observations. To overcome these difficulties, we propose measures of complexity based on vertical structures in recurrence plots and apply them to the logistic map as well as to heart-rate-variability data. For the logistic map these measures enable us not only to detect transitions between chaotic and periodic states, but also to identify laminar states, i.e., chaos-chaos transitions. The traditional recurrence quantification analysis fails to detect the latter transitions. Applying our measures to the heart-rate-variability data, we are able to detect and quantify the laminar phases before a life-threatening cardiac arrhythmia occurs thereby facilitating a prediction of such an event. Our findings could be of importance for the therapy of malignant cardiac arrhythmias.