Probabilistic thresholds of turbulence decay in transitional shear flows

TL;DR

This paper investigates the limits of predicting turbulence decay in shear flows, revealing a universal rate of predictability loss and defining phase-space thresholds that distinguish regions of different decay predictability.

Contribution

It introduces a method to quantify the predictability of turbulence decay and establishes thresholds in phase space, advancing understanding of extreme event forecasting in turbulent flows.

Findings

Predictability degrades at a consistent average rate over time.

Thresholds can separate phase-space regions with different decay predictability.

The predictability loss rate is independent of uncertainty magnitude and event type.

Abstract

Linearly stable shear flows first transition to turbulence in the form of localised patches. At low Reynolds numbers, these turbulent patches tend to suddenly decay, following a memoryless process typical of rare events. How far in advance their decay can be forecasted is still unknown. We perform massive ensembles of simulations of pipe flow and a reduced order model of shear flows (Moehlis et al. 2004) and determine the first moment in time at which decay becomes fully predictable, subject to a given magnitude of the uncertainty on the flow state. By extensively sampling the chaotic sets, we find that, as one goes back in time from the point of inevitable decay, predictability degrades at greatly varying speeds. However, a well-defined (average) rate of predictability loss can be computed. This rate is independent of the uncertainty and also of the type of rare event, i.e. it applies to decay and to other extreme events. We leverage our databases to define thresholds that approximately separate phase-space regions of distinct decay predictability. Our study has implications for the development of predictive models, in particular it sets their theoretical limits. It also opens avenues to study the causes of extreme events in turbulent flows: a state which is predictable to produce an extreme event, it is causal to it from a probabilistic perspective.