Welcome to the Computational Turbulence Laboratory (CTLab) at KU!
Our Vision
We study turbulent flows over complex surfaces in both low and high Mach number regimes. Our expertise encompasses Computational Fluid Dynamics, High-Performance Computing, and Artificial Intelligence. By developing high-order numerical schemes and employing state-of-the-art theoretical and data-driven methods, we aim to understand and predict the aero-thermo-acoustic characteristics of turbulent flows over complex surfaces, such as wall roughness and porous media. Our work advances supersonic aerothermodynamics, clean energy harvesting, and naval engineering.
Supersonic Aerothermodynamics
We study high-Mach number boundary layers over rough walls to examine the impact of shock-boundary layer-roughness interactions on the turbulence field and heat transfer. The end goal is to shed light on roughness-induced early transition to turbulence and surface ablation, and devise turbulence models to predict roughness effects in extreme compressible regimes.
Below is a video of periodic turbulent channel flow over a rough surface at Mach 1.5 — the first visualization of such flow simulations.
Clean Energy Harvesting
Our work contributes to the optimization of wind turbine blades by understanding surface corrosion-induced roughness and mitigating its effects through passive turbulence control using porous materials. We explored porous media applications in flow control, noise reduction, delaying transition to turbulence and stabilizing vortex shedding around bluff-bodies.
AI-Driven Naval Applications
Our pioneering work, as demonstrated by our publication in Aghaei-Jouybari, et. al. (2021), J. Fluid Mech. 912, A8, represents the very first attempt to predict the roughness equivalent sandgrain height (ks) using Machine Learning techniques. ks is a key hydrodynamic parameter that characterizes both the hydrodynamic drag and the underwater radiated noise level. Generating a comprehensive DNS database, consisting of 45 surfaces (24 of them shown below), we designed a neural network that predicts ks with less than 10% error — a significant improvement compared to the existing correlations.
Director
Dr. Mostafa Aghaei Jouybari,
Assistant Professor,
Aerospace Engineering,
University of Kansas.
mostafa@ku.edu
Full Profile
JOIN US
We always welcome motivated students to join our group. Please send your CV to Dr. Aghaei-Jouybari at mostafa@ku.edu if you are interested in turbulent flows!
Latest News
CTLab has been awarded a $1.125M by the Kansas NASA EPSCoR Program (KNEP) for the project “Aerothermal Assessment of Hypersonic Flows over Ablated Surfaces and Woven TPS”. Led by Dr. Aghaei-Jouybari as the Principal Investigator, the project will advance high-fidelity modeling of hypersonic aero-chemo-thermodynamics over ablated and woven thermal protection...
As Director of the Computational Turbulence Laboratory (CTLab), Dr. Aghaei-Jouybari leads an interdisciplinary research program focused on supersonic and hypersonic aero-chemo-thermodynamics, high-fidelity simulations of turbulent flows over complex geometries, and the integration of machine learning and AI for predictive modeling and flow control. ...