The expense of high fidelity computational fluid dynamics, in terms of time and amount of computing resources required, excludes such methods from the early stages of aircraft design. It is only in the early, conceptual, stage of aircraft development where a wide range of designs are considered and global, rather than local, optimisation can play a key role. This thesis deals with methods which may allow high cost computer simulations to be used within a global optimisation design process. The first half of the thesis concentrates on the use of surrogate modeling of the optimisation design space, which allows cheap approximations to be used in lieu of expensive computer simulations. The process is automated and present statistical methods are modified to accommodate problems associated with the simulation of fluid flow and uncertainty within an automated system. The re-interpolation of a regression model of noisy data is presented as a method of improving convergence towards a global optimum. The second half of the thesis develops methods of using partially converged computational fluid dynamics simulations within a surrogate modelling optimisation process. Significant time savings are made possible by reducing computational effort directed at producing a surrogate for regions of poor designs and concentrating resources on modelling regions of promising designs.