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 the surrogate models, which are applied to two turbo-machinery application case studies.

Evaluation of Surrogate Modelling Methods for Turbo-Machinery Component Design Optimization

BADJAN, GIANLUCA;POLONI, CARLO;
2015-01-01

Abstract

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 the surrogate models, which are applied to two turbo-machinery application case studies.
2015
9783319115412
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/2834266
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