Design and analysis of variable fidelity multi-objective experiments
Seminar Room 1, Newton Institute
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.