Uncertainty-based optimisation techniques provide optimal airfoil de-
signs that are less vulnerable to the presence of uncertainty in the operational
conditions (i.e., Mach number, angle-of-attack, etc.) at which an airfoil is func-
tioning. These uncertainty-based techniques typically require numerous function
evaluations to accurately calculate the statistical measure of the quantity of inter-
est. To render the computational burden down, the design optimisation of the air-
foil is performed by a multi-fidelity surrogate-based technique. The high-fidelity
aerodynamic performance is calculated with a compressible RANS solver using
a fine grid. At the low-fidelity level a coarser grid is used. To obtain accurate drag
predictions despite the lower grid resolution the so-called far-field drag approxi-
mation is employed.
