Imperial College London

Professor Mehdi Vahdati

Faculty of EngineeringDepartment of Mechanical Engineering

Principal Research Fellow



+44 (0)20 7594 7073m.vahdati




606City and Guilds BuildingSouth Kensington Campus





Mehdi is the Principal Research Fellow in the Thermo-Fluids Division. He has over 30 years of experience in developing numerical models for aerodynamics and aeroelasticity.  His research topics include:

  • Development of CFD algorithms for internal and external flows
  • Development of numerical aeroelasticity (FSI) models
  • Flutter modelling of gas turbine components (Fan, Compressor, Turbine and Seals)
  • Forced response modelling in gas turbines
  • Stall and surge modelling
  • Aeroelastic behaviour of wind turbines
  • Turbulence modelling using Machine Learning
  • Applications of Machine Learning in turbomachinery
  • Aerodynamic and aeroacoustics modelling of drones (UAVs)

One of the main reasons for turbomachinery failure is vibration caused by Fluid-Solid Interaction (FSI). His research has shown that through efficient and accurate numerical modelling, the mitigating causes of engine failures can be identified, which leads to substantial increase in safety and reliability, major costs savings and provides energy-efficient, environmentally friendly benefits. He was a member of Roll-Royce VUTC from 1993-2020 and Head of Aeroelasticity from 2015-2020.  He is the pioneering author of CFD aeroelasticity Code AU3D. In 2001, Mehdi was awarded by Rolls-Royce plc the title of Rolls-Royce Research Fellow at Imperial College, in recognition for his contributions to the company.

He has published extensively in journals and peer reviewed conferences and has won numerous best paper awards.  He is an Associate Editor of the Journal of Turbomachinery (ASME).



He X, Zhu M, Xia K, et al., 2023, Validation and verification of RANS solvers for TUDa-GLR-OpenStage transonic axial compressor, Journal of the Global Power and Propulsion Society, Vol:7, Pages:13-29

He X, Zhao F, Vahdati M, 2022, A Turbo-Oriented Data-Driven Modification to the Spalart-Allmaras Turbulence Model, Journal of Turbomachinery-transactions of the Asme, Vol:144, ISSN:0889-504X

He X, Tan J, Rigas G, et al., 2022, On the explainability of machine-learning-assisted turbulence modeling for transonic flows, International Journal of Heat and Fluid Flow, Vol:97, ISSN:0142-727X

Zeng H, Zheng X, Vahdati M, 2022, A Method of Stall and Surge Prediction in Axial Compressors Based on Three-Dimensional Body-Force Model, Journal of Engineering for Gas Turbines and Power-transactions of the Asme, Vol:144, ISSN:0742-4795


Suriyanarayanan V, Rendu Q, Vahdati M, et al., 2022, Effect of Manufacturing Tolerance in Flow Past a Compressor Blade, ASME, ISSN:0889-504X

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