Imperial College London

DrSylvainLaizet

Faculty of EngineeringDepartment of Aeronautics

Senior Lecturer
 
 
 
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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 senior lecturer in the Turbulence, Mixing and Flow Control (TMFC) group, 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. Before becoming a lecturer in 2014, I was a Research and Teaching Assistant at the P' Institute in Poitiers and a Research Assistant in the Department of Aeronautics at ICL.

 Understanding turbulent flows and how to use them in various engineering applications is the motivation behind my research. With the help of numerous collaborators, I have developed a suite of high-order finite-difference highly-scalable flow solvers https://github.com/xcompact3d designed for low cost and accurate simulations of turbulent flows. 

At Imperial College, I am currently working on wind farm design, optimization techniques based on Machine Learning, active control of free shear flows, moving Immersed Boundary Methods and particle-laden gravity currents.

Publications

Journals

Schuch FN, Pinto LC, Silvestrini JH, et al., 2018, Three-Dimensional Turbulence-Resolving Simulations of the Plunge Phenomenon in a Tilted Channel, Journal of Geophysical Research-oceans, Vol:123, ISSN:2169-9275, Pages:4820-4832

Chandramouli P, Heitz D, Laizet S, et al., 2018, Coarse large-eddy simulations in a transitional wake flow with flow models under location uncertainty, Computers & Fluids, Vol:168, ISSN:0045-7930, Pages:170-189

Ioannou V, Laizet S, 2018, Numerical investigation of plasma-controlled turbulent jets for mixing enhancement, International Journal of Heat and Fluid Flow, Vol:70, ISSN:0142-727X, Pages:193-205

Margnat F, Ioannou V, Laizet S, 2018, A diagnostic tool for jet noise using a line-source approach and implicit large-eddy simulation data, Comptes Rendus Mecanique, Vol:346, ISSN:1631-0721, Pages:903-918

Conference

ahlfeld, laizet, Geraci G, et al., Multi-Fidelity Uncertainty Quanti cation Using RANS and DNS, CTR Stanford Summer Program

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