Machine Learning Optimisation for Drag Reduction in a Turbulent Boundary Layer

The need to reduce the skin-friction drag of aerodynamic vehicles is of paramount importance. Just a 3% reduction in the skin-friction of a long-range commercial aircraft would save £1.2m in jet fuel per year per aircraft and prevent the annual release of 3,000 tonnes of carbon dioxide. However, achieving net skin-friction drag reduction of wall-turbulent flows is notoriously difficult, even at low Reynolds numbers. Despite decades of research, frustrating performance penalties have prevented the development of any functional and economical system for full-scale applications. Any practical turbulent skin friction reduction control strategy must be able to rapidly and autonomously optimise the aerodynamic surface with minimal power input, have far-reaching effects downstream of control, and must be reactive to changes in flow speed. Enabling such a strategy would mark a new era in turbulence control and is a major objective of this PhD project.
During this project, a new type of machine learning paradigm will be developed, which has the ability to optimise different control inputs rapidly, autonomously, adaptively and simultaneously with only a few numerical simulations. This new capability will be exploited to minimise the turbulent surface friction for a flat plate with minimal power input. The propose control strategy is based on wall blowing, which is a very effective method to reduce shear stress and skin-friction drag (up to 70% drag-reduction have been reported in the literature). However, the energy expenditure can be high, leading to net energy saving as low as 5%. The proposed approach will open up the opportunity to rapidly optimise all existing types of actuation based control strategies as well as discover new types of actuation techniques and drag reduction mechanisms to generate significant levels of turbulent skin friction reduction.
This ambitious goal will be achieved by developing and implementing a novel machine learning paradigm based on Bayesian optimisation, a global optimisation technique that only requires few simulations to be performed, and is yet to be exploited in the field of fluid dynamics.