MALEDRAG - Machine Learning Optimization for Drag Reduction in a Turbulent Boundary Layer

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Image: Imperial College London

The need to reduce the skin-friction drag of aerodynamic vehicles is of paramount importance. Nominally 50% of the total energy consumption of an aircraft or high-speed train is due to skin-friction drag. Reducing skin-friction drag reduces fuel consumption and transport emissions, leading to vast economic savings and wider health and environmental benefits. In this project, wall-normal blowing is combined with a Bayesian Optimisation framework in order to find the optimal parameters to generate net energy savings over a turbulent boundary layer. It is found that wall-normal blowing with amplitudes of less than 1% of the freestream velocity of the boundary layer can generate a drag reduction of up to 80% with up to 5% of energy saving.

Read the complete user research report at the Gauss Centre for Supercomputing.

Principal Investigator

Sylvain Laizet

Imperial College London, United Kingdom