publications

2024

1. Realizability-Informed Machine Learning for Turbulence Anisotropy Mappings

   Ryley McConkey, Eugene Yee, and Fue-Sang Lien

   arXiv, 2024
2. Turbo-RANS: straightforward and efficient Bayesian optimization of turbulence model coefficients

   Ryley McConkey, Nikhila Kalia, Eugene Yee, and 1 more author

   International Journal of Numerical Methods for Heat and Fluid Flow, 2024

2023

1. On the Generalizability of Machine-Learning-Assisted Anisotropy Mappings for Predictive Turbulence Modelling

   Ryley McConkey, Eugene Yee, and Fue-Sang Lien

   International Journal of Computational Fluid Dynamics, Aug 2023
2. Numerical investigation of noise suppression and amplification in forced oscillations of single and tandem cylinders in high Reynolds number turbulent flows

   Zhi Cheng, Ryley McConkey, Eugene Yee, and 1 more author

   Applied Mathematical Modelling, Aug 2023

   Abs

   The noise generated by a three-dimensional turbulent wake behind an oscillating single or tandem cylinder(s) is studied using detached eddy simulation and the Ffowcs Williams-Hawkings (FW-H) method. This hybrid methodology is validated using some experimental data of a turbulent flow past a single or tandem stationary cylinder(s) and some numerical data (obtained from a direct numerical simulation) of a laminar flow past tandem oscillating cylinders. For an oscillating cylinder at a fixed Reynolds number Re=120,000, the sound energy is suppressed compared to that of a stationary cylinder provided the value of oscillation frequency approaches 57% of the original vortex-shedding frequency of the stationary cylinder. This noise reduction arises from the structural oscillations suppressing the noise generated by the vortex shedding from the stationary cylinder—the additional source of noise stemming from the structural oscillations is too small to compensate for the mechanism responsible for the noise suppression. The noise generated by the cylinder motion includes contributions from the loading and thickness noise, whereas the noise generated by vortex shedding from the stationary cylinder only includes the loading noise. The flow past tandem stationary cylinders exhibits a larger number of spectral peaks in the sound pressure power spectrum and the peaks occur at a lower frequency compared to the flow past a single cylinder. The drag force fluctuations result in the propagation of the sound pressure in the in-line direction, whereas the lift force fluctuations result in the propagation of the sound pressure in the transverse direction. Five different scenarios of stationary and/or oscillating tandem cylinders are studied. The second and third invariants of the velocity gradient tensor are used to visualize the distinct vortex structures in the wake for the five different scenarios of tandem cylinders. A single cylinder oscillating in the transverse direction exhibits a larger sound pressure level (SPL) than the streamwise oscillation of the same cylinder. In contrast, the streamwise (in-line) oscillation of one or the other cylinder in a tandem array results in a larger SPL than the transverse oscillation of one or the other cylinder. Finally, the SPL is more sensitive to the oscillations of the rear (downstream) cylinder than the front (upstream) cylinder.

2022

1. Deep structured neural networks for turbulence closure modeling

   R. McConkey, E. Yee, and F. S. Lien

   Physics of Fluids, Mar 2022
2. Modelling of Flow-Induced Vibration of Bluff Bodies: A Comprehensive Survey and Future Prospects

   Ying Wu, Zhi Cheng, Ryley McConkey, and 2 more authors

   Energies, Mar 2022

   Abs

   A comprehensive review of modelling techniques for the flow-induced vibration (FIV) of bluff bodies is presented. This phenomenology involves bidirectional fluid–structure interaction (FSI) coupled with non-linear dynamics. In addition to experimental investigations of this phenomenon in wind tunnels and water channels, a number of modelling methodologies have become important in the study of various aspects of the FIV response of bluff bodies. This paper reviews three different approaches for the modelling of FIV phenomenology. Firstly, we consider the mathematical (semi-analytical) modelling of various types of FIV responses: namely, vortex-induced vibration (VIV), galloping, and combined VIV-galloping. Secondly, the conventional numerical modelling of FIV phenomenology involving various computational fluid dynamics (CFD) methodologies is described, namely: direct numerical simulation (DNS), large-eddy simulation (LES), detached-eddy simulation (DES), and Reynolds-averaged Navier–Stokes (RANS) modelling. Emergent machine learning (ML) approaches based on the data-driven methods to model FIV phenomenology are also reviewed (e.g., reduced-order modelling and application of deep neural networks). Following on from this survey of different modelling approaches to address the FIV problem, the application of these approaches to a fluid energy harvesting problem is described in order to highlight these various modelling techniques for the prediction of FIV phenomenon for this problem. Finally, the critical challenges and future directions for conventional and data-driven approaches are discussed. So, in summary, we review the key prevailing trends in the modelling and prediction of the full spectrum of FIV phenomena (e.g., VIV, galloping, VIV-galloping), provide a discussion of the current state of the field, present the current capabilities and limitations and recommend future work to address these limitations (knowledge gaps).

2021

1. A curated dataset for data-driven turbulence modelling

   Ryley McConkey, Eugene Yee, and Fue-Sang Lien

   Scientific Data, Mar 2021
2. Deep learning-based turbulence closure with improved optimal eddy viscosity prediction

   Ryley McConkey, Eugene Yee, and Fue-Sang Lien

   In Proceedings of the 29th Annual Conference of the Computational Fluid Dynamics Society of Canada, Mar 2021