research

fundamental fluid dynamics

themes | aerospace | energy

The main objective of this research theme is to investigate the scaling of dissipation and as it relates to various canonical turbulent shear flows. This information is then used to develop and improve turbulence models that are used in the industrial sector. Below is a brief summary of active research topics being investigated within the group.


Vortex Dynamics

Turbulent flows, found in a wide array of engineering and natural flow phenomena, are composed of a wide range of coherent vortical structures varying in both size and energy. The complex interaction between these vortical structures has a significant role on the dynamics of the flow field as well as to the generation of drag forces, sound and the spread of pollution. Vortex rings and vortex pairs are two archetypal coherent vortical structures which can be found in the wakes of bluff bodies as well as jets. As a result of their intense coherence, they are known to last for considerable downstream distance which can create major engineering challenges. In this research, we investigate the formation, dynamics, and decay of the vortex structures. Ultimately, we are interested in understanding the key mechanisms that lead to their breakdown and loss of coherence, which would have implications for turbulence modelling and engineering applications.


Student(s): Anushka Goyal*; Raphaƫl Limbourg
Funding: Natural Sciences and Engineering Research Council (NSERC); Fonds de Recherche Quebec, Nature et Technologies (FRQNT)


Turbulent flow-field estimation

Different flow fields are characterised by a multitude of scales; for example atmospheric turbulence has very large scales, on the order of several metres, while boundary layer flows have scales on the order or millimetres. Each flow exhibits unique characteristics to make them identifiable. In nature, birds and fish can identify different types of flow. A frigatebird can identify updrafts and take advantage of their flow properties to glide for weeks without needing to flap its wings. Fish swimming in the wake of an object can take advantage of its vortex shedding to swim in paths which reduce their energy expenditure. This is likely learned from the experiences of each animal; with limited sensory inputs, these animals can identify flows and determine efficient paths to travel within them. Since these animals have learned these properties over time, it should be possible to train a neural network to identify flows as well. This research explores this ideas and looks at ways in which we can identify the type of flow we are in, and then predict (over a finite time) what will happen next.


Student(s): Dylan Caverly*
Funding: Natural Sciences and Engineering Research Council (NSERC)