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

被引：3

作者：

Badjan, Gianluca ^{[1
]}

Poloni, Carlo ^{[2
]}

Pike, Andrew ^{[1
]}

Ince, Nadir ^{[1
]}

机构：

[1] ALSTOM Power Ltd, Rugby CV21 2NH, England

[2] Univ Trieste, I-34127 Trieste, Italy

来源：

ADVANCES IN EVOLUTIONARY AND DETERMINISTIC METHODS FOR DESIGN, OPTIMIZATION AND CONTROL IN ENGINEERING AND SCIENCES | 2015年 / 36卷

关键词：

Surrogate models; Neural networks; Turbo-machinery; NEURAL-NETWORK; ALGORITHM;

D O I：

10.1007/978-3-319-11541-2_13

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：209 / 223

页数：15

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