Detection of dynamical regime transitions with lacunarity as a multiscale recurrence quantification measure

We propose lacunarity as a novel recurrence quantification measure and illustrate its efficacy to detect dynamical regime transitions which are exhibited by many complex real-world systems. We carry out a recurrence plot-based analysis for different paradigmatic systems and nonlinear empirical data in order to demonstrate the ability of our method to detect dynamical transitions ranging across different temporal scales. It succeeds to distinguish states of varying dynamical complexity in the presence of noise and non-stationarity, even when the time series is of short length. In contrast to traditional recurrence quantifiers, no specification of minimal line lengths is required and geometric features beyond linear structures in the recurrence plot can be accounted for. This makes lacunarity more broadly applicable as a recurrence quantification measure. Lacunarity is usually interpreted as a measure of heterogeneity or translational invariance of an arbitrary spatial pattern. In application to recurrence plots, it quantifies the degree of heterogeneity in the temporal recurrence patterns at all relevant time scales. We demonstrate the potential of the proposed method when applied to empirical data, namely time series of acoustic pressure fluctuations from a turbulent combustor. Recurrence lacunarity captures both the rich variability in dynamical complexity of acoustic pressure fluctuations and shifting time scales encoded in the recurrence plots. Furthermore, it contributes to a better distinction between stable operation and near blowout states of combustors

Role of transient growth in subcritical transition to thermoacoustic instability in a horizontal Rijke tube

International audienceA theoretical framework has been developed to understand the non-normal nature of the thermoacoustic interaction in an electrically heated horizontal Rijke tube. The ther-moacoustic system considered here includes the dynamics of the chamber acoustic field and the heat source. The eigenmodes of the system are non-orthogonal due to the nonnormal nature of the linearised evolution operator, which leads to the transient growth in the perturbations even for a linearly stable system. The transient growth is measured in a norm, which in general physically represents the energy in the disturbance. The present framework allows one to obtain a norm systematically from the definition of disturbance energy. In the present paper, adjoint optimisation technique is used to obtain the optimum initial condition (suitable for system with large degrees of freedom ~ 104) for maximum transient growth of the norm (disturbance energy). The optimum initial condition thus obtained has significant contributions from the variables governing the dynamics of the heat source. Some interesting flow structures are observed in the optimum initial condition near the heat source. The non-normal nature of the system is shown to be reflected in the reduction in the range of linearisation (threshold energy for the system to be nonlinearly unstable) of the system. © 2010 by S. Mariappan, P. J. Schmid & R. I. Sujith

Investigation of the effect of acoustic oscillations on swirl flow using PIV

39th AIAA Fluid Dynamics Conference

Non-normality of thermoacoustic interactions: an experimental investigation

International audienc

Experimental investigation on preconditioned rate induced tipping in a thermoacoustic system

Many systems found in nature are susceptible to tipping, where they can shift from one stable dynamical state to another. This shift in dynamics can be unfavorable in systems found in various fields ranging from ecology to finance. Hence, it is important to identify the factors that can lead to tipping in a physical system. Tipping can mainly be brought about by a change in parameter or due to the influence of external fluctuations. Further, the rate at which the parameter is varied also determines the final state that the system attains. Here, we show preconditioned rate induced tipping in experiments and in a theoretical model of a thermoacoustic system. We provide a specific initial condition (preconditioning) and vary the parameter at a rate higher than a critical rate to observe tipping. We find that the critical rate is a function of the initial condition. Our study is highly relevant because the parameters that dictate the asymptotic behavior of many physical systems are temporally dynamic

Deep learning for early warning signals of tipping points

Many natural systems exhibit tipping points where slowly changing environmental conditions spark a sudden shift to a new and sometimes very different state. As the tipping point is approached, the dynamics of complex and varied systems simplify down to a limited number of possible "normal forms" that determine qualitative aspects of the new state that lies beyond the tipping point, such as whether it will oscillate or be stable. In several of those forms, indicators like increasing lag-1 autocorrelation and variance provide generic early warning signals (EWS) of the tipping point by detecting how dynamics slow down near the transition. But they do not predict the nature of the new state. Here we develop a deep learning algorithm that provides EWS in systems it was not explicitly trained on, by exploiting information about normal forms and scaling behavior of dynamics near tipping points that are common to many dynamical systems. The algorithm provides EWS in 268 empirical and model time series from ecology, thermoacoustics, climatology, and epidemiology with much greater sensitivity and specificity than generic EWS. It can also predict the normal form that characterizes the oncoming tipping point, thus providing qualitative information on certain aspects of the new state. Such approaches can help humans better prepare for, or avoid, undesirable state transitions. The algorithm also illustrates how a universe of possible models can be mined to recognize naturally occurring tipping points