The design of products and processes makes increasing use of computer simulations for the prediction of its performance. These computer simulations are considerably cheaper than their physical equivalent. Finding the optimal design has therefore become a possibility. One approach for finding the optimal design using computer simulations is the meta-model approach, which approximates the behaviour of the computer simulation outcome using a limited number of time-consuming computer simulations. This thesis contains four main contributions, which are illustrated by industrial cases. First, a method is presented for the construction of an experimental design for computer simulations when the design space is restricted by many (nonlinear) constraints. The second contribution is a new approach for the approximation of the simulation outcome. This approximation method is particularly useful when the simulation model outcome reacts highly nonlinear to its inputs. Third, the meta-model based approach is extended to a robust optimization framework. Using this framework, many uncertainties can be taken into account, including uncertainty on the simulation model outcome. The fourth main contribution is the extension of the approach for use in integral design of many parts of complex systems.
|Qualification||Doctor of Philosophy|
|Award date||22 Nov 2006|
|Place of Publication||Tilburg|
|Publication status||Published - 2006|