DAE

Abstract

At the core of any design process is the need to predict performance and vary designs accordingly. Performance prediction may come in many forms, from back-of-envelope through high fidelity simulations to physical testing. Such experiments may be one- or two-dimensional simplifications and may include all or some environmental factors. Traditional practice is to increase the fidelity and expense of the experiments as the design progresses, superseding previous low-fidelity results. However, by employing a surrogate modelling approach, all results can contribute to the design process. This talk presents the use of nested space filling experimental designs and a co-Kriging based multi-objective expected improvement criterion to select pareto optimal solutions. The method is applied to the design of an unmanned air vehicle wing and the rear wing of a race-car.