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


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Gianluca Badjan , Carlo Poloni , Andrew Pike , Nadir Ince

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. More... »

PAGES

209-223

Book

TITLE

Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences

ISBN

978-3-319-11540-5
978-3-319-11541-2

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-11541-2_13

DOI

http://dx.doi.org/10.1007/978-3-319-11541-2_13

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1021715577


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