Sandpile cascades on oscillator networks: the BTW model meets Kuramoto

TL;DR

This paper explores how integrating oscillator dynamics with the BTW sandpile model on networks reveals new behaviors like cyclic patterns and large cascade events, relevant for complex systems such as power grids and brain networks.

Contribution

It introduces a novel model combining Kuramoto oscillators with the BTW sandpile, uncovering emergent cyclic behaviors and cascade phenomena not seen in traditional models.

Findings

System exhibits long-term synchronization with minimal cascades.

Large cascades, including Dragon King events, occur after buildup phases.

Mean-field theory accurately predicts cascade size distribution and tipping points.

Abstract

Cascading failures abound in complex systems and the BTW sandpile model provides a theoretical underpinning for their analysis. Yet, it does not account for the possibility of nodes having oscillatory dynamics such as in power grids and brain networks. Here we consider a network of Kuramoto oscillators upon which the BTW model is unfolding, enabling us to study how the feedback between the oscillatory and cascading dynamics can lead to new emergent behaviors. We assume that the more out-of-sync a node is with its neighbors the more vulnerable it is and lower its load-carrying capacity accordingly. And when a node topples and sheds load, its oscillatory phase is reset at random. This leads to novel cyclic behavior at an emergent, long timescale. The system spends the bulk of its time in a synchronized state where load builds up with minimal cascades. Yet, eventually the system reaches a tipping point where a large cascade triggers a "cascade of larger cascades," which can be classified as a Dragon King event. The system then undergoes a short transient back to the synchronous, build-up phase. The coupling between capacity and synchronization gives rise to endogenous cascade seeds in addition to the standard exogenous ones, and we show their respective roles. We establish the phenomena from numerical studies and develop the accompanying mean-field theory to locate the tipping point, calculate the load in the system, determine the frequency of the long-time oscillations and find the distribution of cascade sizes during the build-up phase.