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

Modelling of Dielectric Barrier Discharge Plasma Actuators 

Active control of free-shear flows

Turbulence-resolving simulations of gravity currents



Collaborations

Imperial College London, UK
J. Christos Vassilicos
-study of fractal-generated turbulence

Matthew Piggott, Rafael Palacios

-study of wind farms and wake-to-wake interaction 

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

Andrew Wynn
-optimisation techniques applied to active flow control

PETROBRAS/PUCRS, Porto Alegre, Brazil
Jorge Silvestrini
-study of gravity currents

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

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

Nicolas Benard
-design of plasma actuator models

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



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