Causal structures of turbulent skin-friction drag in wall-bounded turbulent flows

TL;DR

This paper applies a novel causal inference method, LKIF, to turbulent channel flow data to uncover the causal structures behind skin-friction drag, revealing insights into the physical processes involved.

Contribution

It introduces the LKIF causal inference method to turbulence research, providing a more explicit understanding of causality in TSD generation compared to correlation analysis.

Findings

Causal structures are linked to streamwise streaks and rolls near extreme events.

Positive causal structures increase TSD entropy, while negative ones decrease it.

LKIF reveals clearer causal relationships than traditional correlation methods.

Abstract

Understanding the mechanism of turbulent skin-friction drag (TSD) generation is of fundamental and practical importance for designing effective drag reduction strategies. However, many previous studies adopted correlation analysis to reveal the causal map between turbulent motions and TSD generation, an approach that is potentially risky as correlation does not necessarily imply causation. In this study, a novel causal inference method called Liang-Kleeman information flow (LKIF) is utilized for the first time to identify the velocity-induced causal structures related to TSD generation in a turbulent channel flow. The statistical properties of the causal structures are comprehensively investigated. The positive and negative causal structures, defined by their signs and respectively associated with an increase and decrease in TSD information entropy, promote and suppress the generation of extreme TSD. Particularly, we find that the underlying physics of causal structures is essentially associated with the processes of streamwise streaks and rolls approaching or receding from the extreme events. Results indicate that the physics-informed LKIF framework can reveal a more explicit and interpretable causal relationship than correlation analysis.