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

Gianluca Badjan, Carlos Poloni, Andrew Pike, Nadir Ince

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

    2 Citations (Scopus)


    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.
    Original languageEnglish
    Title of host publicationAdvances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences.
    EditorsDavid Greiner, Blas Galván, Jacques Périaux, Nicolas Gauger, Kyriakos C. Giannakoglou, Gabriel Winter
    Number of pages14
    ISBN (Print)978-3-319-11540-5
    Publication statusPublished - 31 Jan 2015

    Publication series

    NameComputational Methods in Applied Sciences


    • Surrogate models
    • Neural networks
    • Turbo-machinery


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