Imperial College London

DrSylvainLaizet

Faculty of EngineeringDepartment of Aeronautics

Reader in Computational Fluid Mechanics
 
 
 
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Contact

 

+44 (0)20 7594 5045s.laizet Website

 
 
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Location

 

339City and Guilds BuildingSouth Kensington Campus

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Summary

 

Summary

I am a reader in the Department of Aeronautics at Imperial College London (ICL). I hold a PhD and a Habilitation à Diriger des Recherches from the University of Poitiers in France in the field of Computational Fluid Dynamics (CFD) applied to turbulence. 

Understanding turbulent flows and how to use them in various engineering applications is the motivation behind my research. With my collaborators at Imperial College, in France and in Brazil, we develop high-order finite-difference highly-scalable flow solvers dedicated to turbulent flows. 


https://github.com/xcompact3d

https://www.turbulencesimulation.com/


Within the turbulence simulation group, we are currently investigating wake-to-wake interaction in wind farms, Bayesian optimisation techniques for drag reduction and energy saving, active control solutions of free shear flows, immersed boundary methods for moving objects, neural networks applied to computational fluid dynamics and particle-laden gravity currents.

Publications

Journals

Voet L, Ahlfeld R, Gaymann A, et al., A hybrid approach combining DNS and RANS simulations to quantify uncertainties in turbulence modelling, Applied Mathematical Modelling: Simulation and Computation for Engineering and Environmental Systems, ISSN:0307-904X

Bartholomew P, Deskos G, Frantz R, et al., 2020, Xcompact3D: An open-source framework for solving turbulence problems on a Cartesian mesh, Softwarex, Vol:12, ISSN:2352-7110

Hamzehloo A, Lusher D, Laizet S, et al., 2020, On the performance of WENO/TENO schemes to resolve turbulence in DNS/LES of high-speed compressible flows, International Journal for Numerical Methods in Fluids, ISSN:0271-2091

Xiao H, Wu J-L, Laizet S, et al., 2020, Flows over periodic hills of parameterized geometries: a dataset for data-driven turbulence modeling from direct simulations, Computers and Fluids, Vol:200, ISSN:0045-7930

Deskos G, Laizet S, Palacios R, 2020, WInc3D: A novel framework for turbulence-resolving simulations of wind farm wake interactions, Wind Energy, Vol:23, ISSN:1095-4244, Pages:779-794

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