Bio
Ryley graduated from the University of Alberta in 2019 with a Bachelor's of Science in Mechanical Engineering (co-op). Pursing an interest in turbulence and computational fluid dynamics (CFD), Ryley then began his Master's degree at the University of Waterloo. In his Master's research, Ryley focused on simulating a new type of wind turbine, which uses vortex induced vibration (VIV) to generate energy. Ryley direct transferred to a PhD in 2020, focusing on developing new turbulence models using machine learning techniques. Ryley completed a 6 month visit at the University of Manchester in 2022-2023, where he focused on data-driven turbulence modelling on complex 3D flows.
Ryley has uniquely diverse experience that includes mechanical design, software implementation, and industrial research and development. At MACH32, a medical device startup company, Ryley designed and simulated novel autoinjectors and COVID-19 protection devices. He is a co-author of multiple patents for these designs. Ryley also has worked on automating CFD simulations as a Software Developer at Orbital Stack, a wind engineering startup company. In the Research and Development group (Labs) at RWDI, Ryley developed and implemented machine learning based tools to augment simulations and wind tunnel experiments.
Research interests
- Fluid mechanics
- Turbulence modelling
- Machine learning
Selected publications and presentations
R. McConkey, E. Yee, F. S. Lien, “On the generalizability of data-driven turbulence closure models,” International Journal of Computational Fluid Dynamics (2023).
R. McConkey (presenter), A. Mole, A. Skillen, A. Revell, E. Yee, F. S. Lien, “Machine Learning Augmented Turbulence Modelling for Massively Separated Three-Dimensional Flows”, in Computational Fluids Conference, Cannes, France (2023).
R. McConkey, E. Yee, F. S. Lien, “Deep Structured Neural Networks for Turbulence Closure Modelling,” Physics of Fluids 34, 035110 (2022).
R. McConkey, E. Yee, F.S. Lien “Deep Learning-Based Turbulence Closure with Improved Optimal Eddy Viscosity Prediction,” Proceedings of the 29th Annual Conference of the Computational Fluid Dynamics Society of Canada (2021).
R. McConkey, E. Yee, F. S. Lien, “A curated dataset for data-driven turbulence modelling,” Nature: Scientific Data 8, 1-14 (2021).
Selected github repositories
turbo-RANS: Straightforward tuning of turbulence model constants
dataFoam: Convert OpenFOAM simulations into datasets for machine learning
Dataset: Turbulence modelling using machine learning
Dataset: Vortex shedding in a turbulent wake
