Grad Seminar: Unsupervised learning for coherent structure identification in turbulent channel flow

Abstract

Coherent structures (CS), i.e., regions of flowing fluid that exhibit significant spatio-temporal coherence, have long been observed in turbulent fluid flow. These CS offer an opportunity to gain insights on fluid behaviour by bypassing the non-linear complexities associated with turbulent flows. Historically, the identification of CS in turbulent flows has involved using manual thresholds to label regions of interest. This work takes steps towards pruning human subjectivity from the CS detection process, where an unsupervised learning framework is used to automatically organize regions of simulated fluid flow into distinct clusters.

Presenter

John Lyne, MASc candidate in Systems Design Engineering 

Attending this seminar will count towards the graduate student seminar attendance milestone!