Complex-network modeling of reversal events in two-dimensional turbulent thermal convection

Spontaneous flow reversals in buoyancy-driven flows are ubiquitous in many fields of science and engineering, often characterized by violent, intermittent occurrences. In this study, we present a complex-network-based reduced-order model to analyse intermittent events in turbulent flows, using temporal and spatial snapshot data. This framework combines elements of dynamical system theory with network science. We demonstrate its utility by applying it to data sequences from intermittent flow reversal events in two-dimensional thermal convection. This approach has proven robust in detecting and quantifying structures and predicting reversals. Additionally, it provides a perspective on the physical mechanisms underlying flow reversals through cluster evolution. This purely data-driven methodology shows the potential to enhance our understanding, prediction and control of turbulent flows and complex systems.