arXiv subjects

Shruti Tandon

Publications and source records attributed to Shruti Tandon.

15 recordsLinked to original sources

arXiv2026

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↗

arXiv2026

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↗

arXiv2026

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↗

arXiv2026

Resolving the Paradox of Changing El Ni\~no-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↗

arXiv2025

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↗

arXiv2025

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↗

arXiv2025

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↗

arXiv2025

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↗

arXiv2025

Non-monotonic emergence of order from chaos in turbulent thermo-acoustic fluid systems

Self-sustained order can emerge in complex systems due to internal feedback between coupled subsystems. Here, we present our discovery of a non-monotonic emergence of order amidst chaos in a turbulent thermo-acoustic fluid system. Fluctuations play a vital role in determining the dynamical state and transitions in a system. In this work, we use complex networks to encode jumps in amplitude scales owing to fluctuations as links between nodes representing amplitude bins. The number of possible amplitude transitions at a fixed timescale reflects the complexity of dynamics at that timescale. The network entropy quantifies the number of and uncertainty associated with such transitions. Using network entropy, we show that the uncertainty in fluctuations first increases and then decreases as the system transitions from chaos via intermittency to order. The competition between turbulence and nonlinear interactions leads to such non-monotonic emergence of order amidst chaos in turbulent thermo-acoustic fluid systems.

physics.flu-dyn↗

arXiv2024

Cross-variable amplitude-frequency coupling during intermittency in a turbulent thermoacoustic system

We investigate flame-acoustic interactions in a turbulent combustor during the state of intermittency before the onset of thermoacoustic instability using complex networks. Experiments are performed in a turbulent bluff-body stabilized dump combustor where the inlet airflow rate is varied quasi-statically and continuously. We construct a natural visibility graph from the local heat release rate fluctuations at each location. Comparing the average degree during epochs of high and low amplitude acoustic pressure oscillations during the state of intermittency, we detect frequency modulation in local heat release rate signals. Through this approach, we discover unique spatial patterns of cross-variable coupling between the frequency of heat release rate fluctuations and the amplitude of acoustic pressure fluctuations. The frequency of heat release rate lfuctuations increases in regions of flame anchoring owing to high-frequency excitation of the flow and flame during epochs of high-amplitude acoustic pressure dynamics. On the other hand, the frequency of heat release rate fluctuations decreases in regions associated with flame front distortions by large coherent vortices. In experiments with continuously varying airflow rates, the spatial pattern of frequency modulation varies with an increase in the average amplitude of acoustic pressure fluctuations owing to an increase in the epochs of periodic acoustic pressure dynamics and the size of vortices forming in the flow. Dynamic shifts in the location of flame anchoring induce low-frequency fluctuations in heat release rate fluctuations during very high-amplitude intermittent acoustic pressure dynamics. Our approach using conditional natural visibility graphs thus reveals the spatial pattern of amplitude-frequency coupling between the co-evolving flame and the acoustic field dynamics in turbulent reacting flows.

physics.flu-dyn↗

arXiv2024

Strange Nonchaotic Attractor in an Unforced Turbulent Reactive Flow System

We discover strange nonchaotic attractor (SNA) through experiments in an unforced system comprising turbulent reactive flow. While models suggest SNAs are common in dynamical systems, experimental observations are primarily limited to systems with external forcing. We observe SNA prior to the emergence of periodic oscillations from chaotic fluctuations. In complex systems, self-organization can lead to order, and inherent nonlinearity can induce chaos. The occurrence of SNA, which is nonchaotic yet nonperiodic in one such complex system, is intriguing.

nlin.CD↗

arXiv2024

Emergence of order from chaos through a continuous phase transition in a turbulent reactive flow system

As the Reynolds number is increased, a laminar fluid flow becomes turbulent, and the range of time and length scales associated with the flow increases. Yet, in a turbulent reactive flow system, as we increase the Reynolds number, we observe the emergence of a single dominant time scale in the acoustic pressure fluctuations, as indicated by its loss of multifractality. Such emergence of order from chaos is intriguing and has hardly been studied. We perform experiments in a turbulent reactive flow system consisting of flame, acoustic, and hydrodynamic subsystems interacting nonlinearly. We study the evolution of short-time correlated dynamics between the acoustic field and the flame in the spatiotemporal domain of the system. The order parameter, defined as the fraction of the correlated dynamics, increases gradually from zero to one. We find that the susceptibility of the order parameter, correlation length, and correlation time diverge at a critical point between chaos and order. Our results show that the observed emergence of order from chaos is a continuous phase transition. Moreover, we provide experimental evidence that the critical exponents characterizing this transition fall in the universality class of directed percolation. Our study demonstrates how a real-world complex, non-equilibrium turbulent reactive flow system exhibits universal behavior near a critical point.

physics.flu-dyn↗

arXiv2023

Universality of oscillatory instabilities in fluid mechanical systems

Oscillatory instability (OI) emerges amidst turbulent states in experiments in various turbulent fluid and thermo-fluid systems such as aero-acoustic, thermoacoustic and aeroelastic systems. For the time series of the relevant dynamic variable at the onset of the OI, universal scaling behavior have been discovered in experiments via the Hurst exponent and certain spectral measures. By means of a center manifold reduction, the spatiotemporal dynamics of these real systems can be mapped to a complex Ginzburg-Landau equation with a linear global coupling (GCGLE). In this work, we show that the GCGLE is able to capture the universal behavior of the route to OI, elucidating it as a transition from defect to phase turbulence mediated by the global coupling.

physics.flu-dyn↗

arXiv2023

Multilayer network analysis to study complex inter-subsystem interactions in a turbulent thermoacoustic system

Thermoacoustic systems are complex systems where the interactions between the hydrodynamic, acoustic and heat release rate fluctuations lead to diverse dynamics such as chaos, intermittency, and ordered dynamics. Such complex interactions cause catastrophically high-amplitude acoustic pressure oscillations and the emergence of order in the spatio-temporal dynamics, referred to as thermoacoustic instability. In this work, we use multilayer networks to study the spatial pattern of inter-subsystem interactions between the vorticity dynamics and thermoacoustic power generated due to acoustically-coupled combustion in a bluff-body stabilized turbulent dump combustor. We construct a two-layered network where the layers represent the thermoacoustic power and vorticity fields. The inter-layer links are determined using cross-variable short-window correlations between vorticity and thermoacoustic power fluctuations at any two locations in the flow field. Analyzing the topology of inter-layer networks, using network properties such as degree correlations and link-rank distributions, helps us infer the spatial inhomogeneities in inter-subsystem interactions and unravel the fluid mechanical processes involved during different dynamical states. We show that, during chaotic dynamics, interactions between subsystems are non-localized and spread throughout the flow field of the combustor. During the state of thermoacoustic instability (order), we find that intense interactions occur in between regions of coherent vortex shedding and thermoacoustic power generation and we understand that these processes are strongly and locally coupled. Moreover, we discover that such dense inter-layer connections emerge in spatial pockets in the dump plane of the combustor during the state of intermittency much prior to the onset of order.

physics.flu-dyn↗

arXiv2022

Cluster formation in particle-laden flows is a continuous phase transition

Studying particle-laden flows is essential to understand diverse physical processes such as rain formation in clouds, pathogen transmission, and pollutant dispersal. Distinct clustering patterns are formed in such flows with particles of different inertia (characterized by Stokes number St). For the first time, we use complex networks to study the spatiotemporal dynamics in such flows. We simulate particles in a 2D Taylor-Green flow and show that the network measures characterize both the local and global clustering properties. As particles cluster into specific patterns from a randomly distributed initial condition, we observe an emergence of a giant component in the derived network through a continuous phase transition. Further, the phase transition time is identified to be related to the Stokes number through a power law for St < 0.25 and an exponential function for St in the range 0.25 to 1. Our findings provide novel insights into the clustering phenomena in particle-laden flows.

physics.flu-dyn↗