This is the author accepted manuscript.Optimisation of designs using Computational Fluid Dynamics (CFD) are frequently performed across many fields of
research, such as the optimisation of an aircraft wing to reduce drag, or to increase the efficiency of a heat exchanger.
General optimisation strategies involves altering design variables with a view to improve appropriate objective function(s).
Often the objective function(s) are non-linear and multi-modal, and thus polynomial time algorithms for solving such
problems may not be available. In such cases, applying Evolutionary Algorithms (EAs - a class of stochastic global
optimisation techniques inspired from natural evolution) may locate good solutions within a practical time frame. The
traditional CFD design optimisation process is often based on a ‘trial-and-error type approach. Starting from an initial
geometry, Computational Aided Design changes are introduced manually based on results from a limited number of
design iterations and CFD analyses. The process is usually complex, time-consuming and relies heavily on engineering
experience, thus making the overall design procedure inconsistent, i.e. different ‘best’ solutions are obtained from different
designers. [...]This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) grant (reference number: EP/M017915/1) for the University of Exeter’s College of Engineering, Mathematics, and Physical Sciences
