Surrogate models are used to approximate complex problems in order to reduce the final cost of the design process. This study has evaluated the potential for employing surrogate modelling methods in turbo-machinery component design optimization. Specifically four types of surrogate models are assessed and compared, namely: neural networks, Radial Basis Function (RBF) Networks, polynomial models and Kriging models. Guidelines and automated setting procedures are proposed to set surrogate models, which are applied to two turbo-machinery application studies.
|Title of host publication
|Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences.
|David Greiner, Blas Galván, Jacques Périaux, Nicolas Gauger, Kyriakos C. Giannakoglou, Gabriel Winter
|Number of pages
|Published - 31 Jan 2015
|Computational Methods in Applied Sciences
- Surrogate models
- Neural networks