Uncovering dynamically critical regions in near-wall turbulence using 3D Convolutional Neural Networks

Understand

Near-wall regions in wall-bounded turbulent flows experience intermittent ejection of slow-moving fluid packets away from the wall and sweeps of faster moving fluid towards the wall.

• These extreme events play a central role in regulating the energy budget of the boundary layer, and are analyzed here with the help of a three-dimensional (3D) Convolutional Neural Network (CNN).
• A CNN is trained on Direct Numerical Simulation data from a periodic channel flow to deduce the intensity of such extreme events, and more importantly, to reveal contiguous three-dimensional salient structures in the flow that are determined autonomously by the network to be critical to the formation and evolution of ejection events.
• These salient regions, reconstructed using a multilayer Gradient-weighted Class Activation Mapping (GradCAM) technique proposed here, correlate well with bursting streaks and coherent fluid packets being ejected away from the wall.