Vortex Identification Using Machine Learning

Many fluid flows are dominated by the dynamics of vortex formation and convection. Examples of practical importance include flows over aircraft wings/wind turbine blades and environmental flows. Knowledge of vortex parameters such as position, radius, circulation, and convective velocity are needed to understand and predict the influence of vortices on flow development. Although a vortex is intuitively understood as a region of fluid with a coherent rotational motion, there is no universally accepted method of defining and identifying vortices in a fluid flow. Furthermore, reliable vortex identification in turbulent flows is made difficult by the presence of random velocity and pressure field fluctuations. Previous work on vortex identification has attempted to overcome these challenges through the application of machine learning techniques to identify and track vortices in experimental data and numerical simulations. The goal of this project is to develop a robust method for vortex identification, quantification, and tracking based on machine learning techniques.

Faculty Supervisor:

Serhiy Yarusevych

Student:

Partner:

Taras Shevchenko National University of Kyiv

Discipline:

Engineering

Sector:

Sustainability & the Environment; Green/Alternative Energy; Aerospace

University:

University of Waterloo

Program: