Imperial College London


Faculty of EngineeringDepartment of Aeronautics

Professor of Computational Fluid Mechanics



+44 (0)20 7594 5052s.sherwin Website




313BCity and Guilds BuildingSouth Kensington Campus





Biography: Spencer Sherwin is Head of Aerodynamics and Professor of Computational Fluid Mechanics in the Department of Aeronautics and Director of Research Computing Service at Imperial College London. He received his MSE and PhD from the Department of Mechanical and Aerospace Engineering Department at Princeton University. Prior to this he received his BEng from the Department of Aeronautics at Imperial College London.

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Research: Professor Sherwin leads an active research group  specializing in the development and application of parallel high order spectral/hp element methods (Nektar ) for flow around complex geometries with a particular emphasis on vortical and bluff body flows and biomedical modelling of the cardiovascular system. More recently, he has been closely  involved in industrial application of these methods through partnerships with McLaren Racing, Airbus and Rolls Royce. Recently he completed a RAEng/McLaren Racing Fellowship during which spectral/hp element methods were applied to problems of interest of Formula One aerodynamics that promoted the development of wall resolving large eddy simulation capabilities for highly unsteady and separated flows

Other Activities: Currently Professor Sherwin is  Principal Investigator on the EPSRC funded Platform for Research In Simulation Methods. Professor Sherwin is also the academic lead to the Joint PhD programme with the University of São Paulo. 



Winters AR, Moura RC, Mengaldo G, et al., 2018, A comparative study on polynomial dealiasing and split form discontinuous Galerkin schemes for under-resolved turbulence computations, Journal of Computational Physics, Vol:372, ISSN:0021-9991, Pages:1-21

Mengaldo G, Moura RC, Giralda B, et al., 2018, Spatial eigensolution analysis of discontinuous Galerkin schemes with practical insights for under-resolved computations and implicit LES, Computers & Fluids, Vol:169, ISSN:0045-7930, Pages:349-364

Calder M, Craig C, Culley D, et al., 2018, Computational modelling for decision-making: where, why, what, who and how, Royal Society Open Science, Vol:5, ISSN:2054-5703

Mengaldo G, De Grazia D, Moura RC, et al., 2018, Spatial eigensolution analysis of energy-stable flux reconstruction schemes and influence of the numerical flux on accuracy and robustness, Journal of Computational Physics, Vol:358, ISSN:0021-9991, Pages:1-20


Cohen J, Marcon J, Turner M, et al., Simplifying high-order mesh generation for computational scientists, 10th International Workshop on Science Gateways, CEUR Workshop Proceedings, ISSN:1613-0073

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