Are extreme dissipation events predictable in turbulent fluid flows?

TL;DR

This paper develops a method to predict extreme dissipation events in turbulent flows by identifying characteristic precursors using a combined dynamics-statistics approach, demonstrating robustness across different Reynolds numbers.

Contribution

It introduces a novel precursor-based prediction method for extreme dissipation events in turbulence, leveraging attractor statistics and flow alignment analysis.

Findings

Precursor states effectively predict extreme dissipation events.

The method is robust across different Reynolds numbers.

High likelihood regions in state space are key to prediction.

Abstract

We derive precursors of extreme dissipation events in a turbulent channel flow. Using a recently developed method that combines dynamics and statistics for the underlying attractor, we extract a characteristic state that precedes laminarization events that subsequently lead to extreme dissipation episodes. Our approach utilizes coarse statistical information for the turbulent attractor, in the form of second order statistics, to identify high-likelihood regions in the state space. We then search within this high probability manifold for the state that leads to the most finite-time growth of the flow kinetic energy. This state has both high probability of occurrence and leads to extreme values of dissipation. We use the alignment between a given turbulent state and this critical state as a precursor for extreme events and demonstrate its favorable properties for prediction of extreme dissipation events. Finally, we analyze the physical relevance of the derived precursor and show its robust character for different Reynolds numbers. Overall, we find that our choice of precursor works well at the Reynolds number it is computed at and at higher Reynolds number flows with similar extreme events.