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R. I. Sujith

Publications and source records attributed to R. I. Sujith.

At least 19 recordsLinked to original sources

Identifying the structure of dynamical transitions in logistic map

Nonlinear dynamical systems manifest rich variety of dynamical states and transitions driven by fluctuations. To understand the pattern of fluctuations during dynamical transitions, we investigate the structural features of chaos to order transition in logistic map. We determine fluctuations as amplitude jumps and encode them onto a complex network where nodes represent amplitude levels and links represent transitions between distinct amplitude bins. We discover that global network measures identify points of period doubling, regimes of periodicity and chaos, including interior crises events. Using local network measures, we also unravel novel peculiar parabolic-shaped patterns in the orbit diagram that we show are reminiscent of the distribution of stable and unstable periodic points in the bifurcation diagram.

nlin.CD

A Graph Neural Network Framework for Characterizing Rainfall Variability Regimes across India

The Indian Summer Monsoon shows significant spatial variation. While prior work primarily focused on forecasting rainfall amounts, little attention has been given to how consistently a location's seasonal rainfall trajectory repeats from year to year. We introduce a graph-based machine learning framework to classify locations across India by this inter-annual consistency. Using 2001 to 2022 GSMaP ISRO data (excluding 2012), we constructed graphs for 29,026 grid points where nodes represent individual years and edges denote cosine similarity. A Graph Convolutional Network classified locations as either consistent or erratic with 96.8% accuracy. Applied to the Indian landmass, the model successfully identified the Western Ghats, Northeast India, and parts of central India as consistent regions. This classification was rigorously validated through statistical testing and temporal stability analysis, showing 93.6% agreement across two independent timeframes. Crucially, the results reveal a previously unreported coupling: regions with higher rainfall volumes are also the most temporally repeatable year-to-year, demonstrating an emergent, spatially coherent structure.

physics.ao-ph

Metamorphosis of transition between states of limit cycle oscillations in aeroacoustic system

Dynamical systems undergoing transition to oscillatory state exhibit change in the nature of the transition from supercritical to subcritical Hopf bifurcation or vice versa upon variation of a secondary parameter. This phenomenon is referred to as change of criticality. Many real-world systems undergo transition to oscillatory state that do not fit in the framework of Hopf bifurcation, and hence the change of criticality. We perform experiments on a ducted turbulent aeroacoustic flow constrained by two orifices separated at a distance apart. We vary the Reynolds number (Re), a bifurcation parameter causing a transition between various limit cycles. We change the distance between the orifices as the secondary parameter. We discover that turbulent aeroacoustic flows exhibit a metamorphosis of the transition from continuous to abrupt through a canard explosion, a bifurcation unique for its continuous yet rapid nature. We observe two distinct abrupt bifurcations, differing in their dynamical states associated with the transition. Understanding this metamorphosis from continuous to abrupt aids in developing low-cost control and preventive strategies for systems undergoing a route to oscillatory instabilities.

physics.flu-dyn

A Deep Learning Earth System Model Simulation of Indian Monsoon Intraseasonal and Interannual Variability

With the data-driven artificial intelligence/machine learning (AI/ML) models having demonstrated their ability to extend the prediction horizon of large-scale weather at a fraction of computational cost of numerical weather prediction models, a pertinent question is, could these models do the same for sub-seasonal to seasonal (S2S) prediction? A key challenge in developing a S2S prediction system is the requirement for a coupled ocean-atmosphere Earth system emulator that can stably simulate the observed intraseasonal and interannual variability with fidelity. In the rapidly evolving field of AI/ML weather models, such a deep learning 3D ocean-atmosphere coupled model has become available, called SamudrACE. With our interest in developing an AI/ML S2S model for Indian monsoon, here we examine the extent to which SamudrACE faithfully simulates Indian monsoon intraseasonal and interannual variability. Compared to observation, we found biases in SamudrACE's simulation of monsoon intraseasonal and interannual variability. Our systematic documentation and analyses of these biases provide a useful benchmark for improving not only SamudrACE but also coupled emulators in general and could fast track the development of a deep learning 3D global S2S prediction system.

physics.ao-ph

Resolving the Paradox of Changing El Niño-Monsoon Relation through Synchronization of Chaotic Oscillators

For over a century, the relationship between Indian summer monsoon rainfall and El Nino-Southern Oscillation has been the foundation of 'long-range' prediction of Indian monsoon. This relation is estimated from correlations between Indian summer monsoon rainfall and a Pacific sea surface temperature-based index of El Nino-Southern Oscillation. However, a prominent multi-decadal variability in the correlation raises doubts on the realism of El Nino-Monsoon relation and stability of Indian monsoon predictability. Previous studies discussed that Pacific-based El Nino-Southern Oscillation indices do not represent El Nino's global influence completely, making their correlation with Indian monsoon unreliable. To address this limitation, a Global El Nino-Southern Oscillation framework based on the depth of the 20 degree Celsius isotherm is developed, integrating subsurface signal from all three tropical ocean basins and maximizing Indian monsoon teleconnections. Contrary to previous findings, the 20 degree Celsius isotherm-based Global El Nino-Southern Oscillation exhibits a strong and stable correlation (greater than 0.8) with Indian monsoon at an 18-month lead. Through a re-examination of the El Nino-Monsoon relationship with the superior Global El Nino-Southern Oscillation predictor, we show that the true relationship is independent of global warming and stationary in time. We discover that the stationarity in the El Nino-Monsoon relationship emerges as a natural consequence of chaotic synchronization between Indian summer monsoon rainfall and 20 degree Celsius isotherm at an 18-month lead. Such synchronization between chaotic climate variables provides a new physical basis for climate predictability beyond the conventional deterministic limit set by chaos. Our findings provide the foundation and renewed confidence in long range climate prediction.

physics.ao-ph

Why is Seasonal Climate Predictable Beyond the Limit of Deterministic Predictability set by Chaos?

The Earth's climate is an ensemble of interacting, spatially extended oscillatory media ('climate systems') whose slow-changing averages coexist with chaotic, high-frequency weather fluctuations in quasi-equilibrium. The limit of deterministic predictability (LDP) for any climate system is determined by its fastest-growing errors. However, recent findings show that the Indian Summer Monsoon Rainfall (ISMR) can be predicted up to 18 months in advance-far beyond its LDP. Using a model of two interacting oscillatory media, we show that this extended predictability arises from lag synchronization between ISMR and its predictor, the Global El Nino-Southern Oscillation (G-ENSO), to which it is strongly coupled. We introduce complex order parameters representing the internal dynamics of the two climate systems. Their spatiotemporal evolution is governed by coupled Complex Ginzburg-Landau Equations, producing aperiodic yet strongly correlated time series at long lead times. Our findings have far-reaching consequences in advancing seasonal prediction across climate systems.

physics.ao-ph

Prediction and Predictability of the Wet-Season Rainfall over Southeast India

The challenge in predicting sub-regional climate within the Indian monsoon region is exacerbated by its increasing variability in a warming world. While exploring the seasonal predictability of rainfall over the state of Tamil Nadu in southeast India, we identify an overall increase in the monthly rainfall and its variability in recent years due to an increase in surface temperature, water vapour and moisture convergence. We attribute the increasing excess rainfall to a long-term reduction in convective inhibition. We further find an increasing trend in the length of the rainy season due to an earlier onset and a delayed withdrawal of the large-scale monsoon over the southeastern and southwestern regions of southern peninsular India, respectively. Further, the simultaneous (0- month lead) predictability of the primary wet-season (October-December, OND) rainfall over Tamil Nadu is dominated by sea surface temperature (SST) anomalies in the North Indian Ocean. However, a global tropical SST climate network reveals a high potential predictability and potential to realize significant forecast skill at a lead time of up to 10 months. The long-lead predictability arises from SST and rainfall interactions across the tropical Indo-Pacific and equatorial Atlantic regions. Our findings provide a robust data-driven methodology for skillful seasonal rainfall prediction over Tamil Nadu, despite the increasing rainfall variability.

physics.ao-ph

Early warning signals for primary and secondary bifurcation to oscillatory instabilities

In several natural and engineering systems, changes in control parameters can trigger bifurcations that lead to sustained or growing periodic oscillations, indicating the onset of oscillatory instabilities. Such emergent behaviour often results from positive feedback between interacting subsystems, resulting in large-amplitude oscillations that can be detrimental. Several precursors are available to provide early warning of an impending oscillatory instability. In reality, practical systems may exhibit different sequences of bifurcations, including a primary bifurcation to an oscillatory state that may be either continuous or abrupt, followed by an abrupt secondary bifurcation, and further transitions beyond the secondary bifurcation. Existing precursors for oscillatory instabilities typically forewarn the onset of the primary bifurcation to an oscillatory state and tend to saturate once the system enters the oscillatory regime. Notably, primary bifurcations often involve lower amplitudes compared to the more severe states after secondary bifurcation. In this study, we propose a methodology based on spectral visibility graphs to get forewarning for both primary and secondary bifurcations. The method inherently captures the evolution of the harmonic content of the signal relative to other frequency components. The approach employs tuning of a single sensitivity parameter to detect different sequences of bifurcation. We demonstrate the usefulness of our method for thermo-acoustic and aero-acoustic instabilities in multiple engineering systems involving turbulent flow and reactions. Our methodology can help systems prepare in advance or even avoid undesirable transitions. Tuning the sensitivity parameter allows adaptive, risk-based warnings, ensuring high performance without tipping into undesirable regimes.

physics.flu-dyn

A complex network approach to characterize clustering of events in irregular time series

In complex systems, events occur at irregular intervals that inherently encode the underlying dynamics of the system. Analyzing the temporal clustering of these events reveals critical insights into the non-random patterns and the temporal evolution. Existing techniques can effectively quantify the overall clustering tendency of events using global statistical measures. However, these macroscopic approaches leave a critical gap, as they do not attempt to investigate the dynamics of individual clusters. Analyzing individual clusters is essential, as it helps comprehend the local interactions that actively drive the system dynamics, which may be obscured by global averaging, while simultaneously revealing the time scales involved. To address these limitations, we propose a complex network-based framework for analyzing clustering of events occurring at irregular intervals. The framework establishes connections using arrival times, transforming the time series into a network. Network properties are then used to quantify the clustering. Further, a community detection algorithm is used to identify individual clusters in time series. We illustrate the method by applying it to standard arrival processes, such as the Poisson process and the Markov-modulated Poisson process. To further demonstrate its scope, we apply the method to two diverse systems: the time series of droplet arrivals in turbulent flows and the R-R intervals in electrocardiogram (ECG) signals.

physics.data-an

Shock-induced tipping in a thermoacoustic system

Tipping refers to the transition of a system from one state to another. In this study, we focus on shock-induced tipping, which occurs due to a sudden and large disturbance in a control parameter, which is referred to as the shock. This shock drives the system from one dynamical state to another. We present the first experimental demonstration of shock-induced tipping using a prototypical thermoacoustic system, the horizontal Rijke tube. In a thermoacoustic system, unsteady heat release and sound waves interact through positive feedback, leading to self-sustained, high-amplitude oscillations known as limit cycles. The system transitions from a quiescent state to a state of self-sustained oscillations when a shock is introduced in the power supplied to the heat source (an electrically heated grid). This shock is created by abruptly increasing the voltage supplied to the grid, which takes the system into a bistable region. To explain the underlying mechanism linking the shock in the supplied power to the observed tipping behaviour, we model the system by modifying the governing equations of the Rijke tube to incorporate the heat transfer properties of the grid. We demonstrate that the shock in the supplied power manifests as a shock in the grid temperature, causing the system to fall into the basin of attraction of an alternate stable state. The tipping event depends on the magnitude of the shock and the temperature of the grid. Understanding the mechanisms underlying shock-induced tipping is crucial for developing systems with improved safety and reliability.

physics.flu-dyn

Turbulence is ineffective in causing raindrop growth in polluted clouds

Aerosol-cloud interactions represent the largest uncertainty in climate-change assessment, and while cloud turbulence is considered crucial for droplet growth, its precise role remains unclear. Our laboratory-controlled studies show that turbulence does not always enhance collision and coalescence; instead, its influence emerges only when droplets have a sufficiently broad size distribution. The dissipative-scale droplet behaviour underscores the importance of improved parameterisations to accurately model cloud microphysics.

physics.ao-ph

Large-scale patterns of small-scale vorticity interactions foster moist convection during cyclogenesis

The formation and intensification of a tropical cyclone is a complex phenomenon involving several feedback interactions between momentum and energetics of the storm, and across multiple spatio-temporal scales. Background vorticity interactions in the turbulent atmosphere play a crucial role in the formation of cyclones. How these vorticity interactions lead to convective organization and sustain a disastrous cyclonic vortex amidst a turbulent atmosphere remains elusive. Moreover, what processes distinguish depressions that develop into a cyclone from those that do not? Here, we investigate the role of small-scale vorticity interactions in the background flow in sustaining large-scale organization during the emergence of a cyclone. We construct time-varying complex networks where geographical locations are nodes and connections between nodes represent short-time vorticity correlations. Only those nodes are connected that are in spatial proximity corresponding to sub-meso length scales. Each network is constructed for 29 hours of data; consecutive networks are separated by three hours, thus revealing the evolution of local coherence in vorticity dynamics. We discover that small-scale vorticity interactions manifest as large-scale emergent patterns. Further, we establish that organized moist convection is significantly correlated to regions of locally coherent vorticity dynamics during the intensification of a depression that forms a cyclone; however, such correlations are not sustained during non-developing cases. Using modal analysis of time-evolving network connectivity, we show that these large-scale patterns are essentially large-scale modes of propagation of coherence in small-scale vorticity dynamics. We explain that such propagation is facilitated by moisture feedback at small-scales and self-organized patterns at large-scales.

physics.ao-ph

Modeling the influence of interactions on different variables in a turbulent thermoacoustic system

Turbulent reacting flows confined to ducts are plagued by thermoacoustic instability, a state in which a positive feedback between flow, flame and acoustic perturbations leads to the emergence of catastrophically high-amplitude oscillatory dynamics in the sound and global heat release rate fluctuations. Modeling the interdependence between local interactions and the global emergence of order in such spatially extended complex systems is exacting. Here, we present a novel reduced-order model to capture the influence of the local interactions on the variables exhibiting global emergence of order in a turbulent reacting flow system. We represent each variable that exhibits global oscillatory instability as an oscillator with a cubic nonlinearity. The oscillator is driven by a forcing term that represents the holistic influence of the inter-subsystem interactions on the global behavior. The forcing term essentially couples the local interactions and the globally emergent dynamics in the model. Further, the influence of the inter-subsystem interactions on the behavior of each subsystem is different. Therefore, we use different forcing terms for each variable inspired by the physical interactions in the system. The nonlinear oscillators representing the acoustic and the heat release rate oscillations are hence forced using Wiener and Markov-modulated Poisson processes, respectively. Using this approach, we are able to reproduce (i) the multifractal characteristics of acoustic pressure fluctuations during chaotic dynamics, (ii) the loss of multifractality through the experimentally observed scaling law behavior during the transition from chaos to order and (iii) the emergence of periodicity and bifurcation in heat release rate dynamics.

physics.flu-dyn

Periodic modulation of the space-filling nature of the turbulent flame leads to spiky heat release oscillations

Spiky oscillations are characterized by slow-fast dynamics and are observed in excitable media such as neuronal membranes and cardiac cells. In a turbulent reactive flow system, we observe that the heat release rate exhibits self-sustained periodic, spiky oscillations in synchrony with the sinusoidal periodic acoustic pressure oscillations. These self-sustained oscillations are a consequence of thermoacoustic instability, which arises due to a positive feedback between the acoustic and the heat release rate fields. One of the primary mechanisms for fluctuations in the heat release rate is the modulation in the topology of the flame, a thin interface separating the reactants and products. In this work, we explore the dynamics of the space-filling nature of the flame, quantified by its fractal dimension in relation to the spiky heat release rate oscillations in a turbulent reactive flow system. We discover that the periodic oscillatory dynamics in the space-filling nature of the flame lead to periodic, spiky heat release rate oscillations. Based on this result, we show that the spiky oscillations in the heat release rate can be approximated as $e^{\mathrm{sin}(ωt)}$ during the dynamical state of generalized synchronization between the heat release rate and the acoustic pressure oscillations in a turbulent reactive flow system. In synchronization theory, generalized synchronization is characterized by an emergent functional relationship, $Φ$, between the interacting subsystems. We unravel this emergent functional relation between the heat release rate and the acoustic pressure oscillations in a turbulent reactive flow system. It is intriguing that the dynamics of a far-from-equilibrium complex system can be represented by such simple mathematical relations.

physics.flu-dyn

Vortical interactions in turbulent thermoacoustic systems

This study examines the dynamics of vortical interactions and their implications for mitigating thermoacoustic instability in a turbulent combustor. The regions of intense vortical interactions are identified as vortical communities in the network space of weighted directed vortical networks constructed from two-dimensional experimental velocity data. One can expect vortical interactions in the combustor to be strongest near the moment of vortex shedding, as the shed vortices gradually weaken due to dissipation while convecting downstream. However, we show that, during the state of thermoacoustic instability, there is a non-trivial consistent phase lag of approximately 52 degrees between the shedding of the coherent structures from the backward-facing step and the time instant when the vortical interactions attain their local maximum value. We explain this phase lag by investigating the correlation between acoustic pressure fluctuations, spatio-temporal dynamics of coherent structures, and vortical interactions in the reaction field of the combustor. We also show the aperiodic variation of vortical interactions during the states of combustion noise and aperiodic epochs of intermittency. Furthermore, the spatio-temporal evolution of pairs of vortical communities with the maximum inter-community interactions provides insight into explaining the critical regions detected in the reaction field during the states of intermittency and thermoacoustic instability, also identified in previous studies. We further show that the most efficient suppression of thermoacoustic instability via air microjet injection is achieved when steady air jets are introduced to disrupt the maximum inter-community interactions present during the state of thermoacoustic instability.

physics.flu-dyn

Universal self-similarity of hierarchical communities formed through a general self-organizing principle

Emergence of self-similarity in hierarchical community structures is ubiquitous in complex systems. Yet, there is a dearth of universal quantification and general principles describing the formation of such structures. Here, we discover universality in scaling laws describing self-similar hierarchical community structure in multiple real-world networks including biological, infrastructural, and social networks. We replicate these scaling relations using a phenomenological model, where nodes with higher similarity in their properties have greater probability of forming a connection. A large difference in their properties forces two nodes into different communities. Smaller communities are formed owing to further differences in node properties within a larger community. We discover that the general self-organizing principle is in agreement with Hakens principle; nodes self-organize into groups such that the diversity or differences between properties of nodes in the same community is minimized at each scale and the organizational entropy decreases with increasing complexity of the organized structure.

physics.soc-ph

A network-theoretic approach for characterizing Mack-mode instability in high-speed boundary layers

Here we present a network theory-based approach to investigate the Mack-mode instability signature found in high-speed schlieren data from a Mach 6 laminar boundary layer flow over a $7^\circ$ cone. The data contain instability wave packets in the form of coherent rope-like structures which exhibit intermittency. The intermittency implies that conventional Fourier techniques are not particularly well suited for analysis. Network analysis, which is well known for handling episodic spatio-temporal data in a variety of complex systems, provides an alternate and more suitable framework. Techniques from time-varying spatial proximity networks are applied to the present data. The connected components in the network topology reveal lines of constant phase for coherent wave packets associated with the instability, and localized regions of high schlieren light intensity for intermittent laminar or turbulent flow states. The orientation angle of the connected network components is found to be a suitable metric for identifying components associated with the Mack-mode instability, and that enables detailed characterization of the wavelength and propagation speeds of the instability wave packets. Beyond the characterization exercise, network analysis can provide a powerful framework for understanding the fundamental nature of intermittency and its role in the laminar-to-turbulent flow transition process.

physics.flu-dyn

Climatic Phase Transitions Unravel the Onset and Withdrawal of Indian Monsoon

The livelihood and food security of more than a billion people depend on the Indian monsoon (IM). Yet, a universal definition of the large-scale season and progress of IM is missing. Even though IM is a planetary-scale convectively coupled system arising largely from seasonal migration of the Intertropical Convergence Zone (ITCZ), the definitions of its onset and progression are based on local weather observations, making them practically inutile due to the detection of bogus onsets. Using climate networks, we show that small-scale clusters of locally defined rainfall onsets coalesce through two abrupt climatic phase transitions defining large-scale monsoon onsets over Northeast India and the Indian peninsula, respectively. These abrupt transitions are interspersed with continuous growth of clusters. Breaking the conventional wisdom that IM starts from southern peninsula and expands northward and westward, we unveil that IM starts from Northeast India and expands westward and northward, covering the entire country. We show that the large-scale monsoon onset over the Indian peninsula is critically dependent on the characteristics of monsoon onset over Northeast India. Unlike existing definitions, a rapid and consistent northward propagation of rainfall establishing the ITCZ manifests after our network-based onset dates. Thus, our definition captures the IM onset better than the existing definitions.

physics.ao-ph