Explainable deep learning reveals the physical mechanisms behind the turbulent kinetic energy equation
Francisco Alc\'antara-\'Avila, Andr\'es Cremades, Sergio Hoyas, Ricardo Vinuesa

TL;DR
This paper employs explainable deep learning to uncover the physical structures and mechanisms responsible for turbulent kinetic energy transport in channel flow, highlighting hierarchical organization near the wall and its breakdown in the outer layer.
Contribution
The study introduces an explainable deep learning approach using SHAP to identify and analyze turbulence structures, revealing hierarchical organization and dissipation's dominant role.
Findings
Near-wall structures are mainly associated with sweep events.
Hierarchical organization of turbulence exists in the viscous layer.
Classical coherent structures do not fully explain energy budget mechanisms.
Abstract
In this work, we investigate the physical mechanisms governing turbulent kinetic energy transport using explainable deep learning (XDL). An XDL model based on SHapley Additive exPlanations (SHAP) is used to identify and percolate high-importance structures for the evolution of the turbulent kinetic energy budget terms of a turbulent channel flow at a friction Reynolds number of . The results show that the important structures are predominantly located in the near-wall region and are more frequently associated with sweep-type events. In the viscous layer, the SHAP structures relevant for production and viscous diffusion are almost entirely contained within those relevant for dissipation, revealing a clear hierarchical organization of near-wall turbulence. In the outer layer, this hierarchical organization breaks down and only velocity-pressure-gradient correlation and…
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Taxonomy
TopicsModel Reduction and Neural Networks · Fluid Dynamics and Turbulent Flows · Lattice Boltzmann Simulation Studies
