TURBULENCE SIMULATION GROUP IMPERIAL COLLEGE LONDON
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Research

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Research Activities:

Development and use of a suite of high-order finite-difference highly-scalable flow solvers

Machine Learning for turbulence modelling

Optimization techniques based on Bayesian optimisation for drag reduction  and energy savings in turbulent boundary layers

Immersed Boundary Methods for moving objects

Wake prediction for wind turbines, design and control of wind farms

Quantum computing for Computational Fluid Dynamics
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Active control of free-shear flows

Turbulence-resolving simulations of gravity currents



Collaborations

-Imperial College London, UK
Rafael Palacios
-Wind farms and wake-to-wake interactions 

Francesco Montomoli, Björn W. Schuller
-Uncertainty quantification, machine learning

Andrew Wynn
-Optimisation techniques applied to active flow control

George Rigas
-Active flow control for bluff bodies

-STFC Daresbury
Charles Moulinec, Jian Fan, Stefano Rolfo
-GPU programming
-Particle tracking


PETROBRAS/PUCRS, Porto Alegre, Brazil
Jorge Silvestrini
-Gravity currents

NREL, US
Georgios Deskos
-Wind farms and wake-to-wake interactions 

University of Poitiers, France
Eric Lamballais, Florent Margnat, Véronique Fortuné, 

-Development of Implicit Large-Eddy Simulation techniques
-Development of tools for acoustic prediction

Virginia Tech, US
Heng Xiao
-Machine learning for turbulence modelling

IRSTEA/INRIA, University of Rennes, France
Etienne Memin, Dominique Heitz
-Development of Large-Eddy Simulation models
-Particle tracking



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