Multi-objective design optimisation of an airfoil with geometrical uncertainties leveraging multi-fidelity Gaussian process regression

This paper presents the multi-objective optimisation of the MH114 high-lift airfoil. We seek the set of Pareto optimal solutions that maximise the airfoil lift and minimise the drag. The lift and drag forces are considered uncertain due to geometrical uncertainties. The uncer- tainty quantification of the probabilistic aerodynamic force values re- quires a large number of samples. However, the prediction of the aero- dynamic forces is expensive due to the numerical solution of the Navier- Stokes equations. Therefore, a multi-fidelity surrogate assisted approach is employed to combine expensive RANS simulations with cheap poten- tial flow calculations. The multi-fidelity surrogate-based approach allows us to economically optimise the aerodynamic design of the airfoil under uncertainty.