# Ryley McConkey

Postdoc at Massachusetts Institute of Technology

I 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), I then began a Master’s degree at the University of Waterloo. In my Master’s research, I was focused on simulating a new type of wind turbine which uses vortex induced vibration (VIV) to generate energy. Then, I direct transferred to a PhD in 2020. My PhD was focused on developing new turbulence models using machine learning. I completed a 6 month visit at the University of Manchester in 2022-2023, where I focused on data-driven turbulence modelling on complex 3D flows. After completing my PhD in 2024, I have recently started a Postdoc at MIT in Tess Smidt’s atomic architects group. More to come!

My diverse experience includes mechanical design, software implementation, and industrial research and development. At MACH32, a medical device startup company, I designed and simulated novel autoinjectors and COVID-19 protection devices. I also 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, I developed and implemented machine learning based tools to augment simulations and wind tunnel experiments.

Here is a playlist with my favourite fluid mechanics videos:

## Selected publications

- Realizability-Informed Machine Learning for Turbulence Anisotropy Mappings
*arXiv*, 2024 - Turbo-RANS: straightforward and efficient Bayesian optimization of turbulence model coefficients
*International Journal of Numerical Methods for Heat and Fluid Flow*, 2024 - A curated dataset for data-driven turbulence modelling
*Scientific Data*, 2021 - Deep learning-based turbulence closure with improved optimal eddy viscosity prediction
*In Proceedings of the 29th Annual Conference of the Computational Fluid Dynamics Society of Canada*, 2021 - Deep structured neural networks for turbulence closure modeling
*Physics of Fluids*, Mar 2022 - On the Generalizability of Machine-Learning-Assisted Anisotropy Mappings for Predictive Turbulence Modelling
*International Journal of Computational Fluid Dynamics*, Aug 2023 - Modelling of Flow-Induced Vibration of Bluff Bodies: A Comprehensive Survey and Future Prospects
*Energies*, Aug 2022 - Numerical investigation of noise suppression and amplification in forced oscillations of single and tandem cylinders in high Reynolds number turbulent flows
*Applied Mathematical Modelling*, Aug 2023