A novel global optimization algorithm and data-mining methods for turbomachinery design

A new multi-objective, multi-disciplinary global optimization strategy is proposed to address the high-dimensional, computationally expensive black box problem (HEB) in turbomachinery design. The strategy consists of an adaptive sampling hybrid optimization algorithm (ASHOA), two data-mining techniq...

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Bibliographic Details
Published inStructural and multidisciplinary optimization Vol. 60; no. 2; pp. 581 - 612
Main Authors Li, Xiaojian, Zhao, Yijia, Liu, Zhengxian
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2019
Springer Nature B.V
Subjects
Adaptive algorithms
Adaptive sampling
Centrifugal compressors
Computational Mathematics and Numerical Analysis
Data mining
Design optimization
Design parameters
Engineering
Engineering Design
Global optimization
Kriging
Metamodels
Multiple objective analysis
Optimization algorithms
Parameterization
Pressure ratio
Research Paper
Self organizing maps
Strategy
Theoretical and Applied Mechanics
Turbomachinery
Variance analysis
Data-mining
Turbomachinery
Adaptive sampling
Hybrid optimization
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Summary:A new multi-objective, multi-disciplinary global optimization strategy is proposed to address the high-dimensional, computationally expensive black box problem (HEB) in turbomachinery design. The strategy consists of an adaptive sampling hybrid optimization algorithm (ASHOA), two data-mining techniques, a 3D blade parameterization method, and the aerodynamic/mechanical codes. Firstly, the ASHOA is established by integrating a novel adaptive sampling Kriging metamodel and a new hybrid optimization method. Secondly, two data-mining methods (analysis of variance (ANOVA) and self-organizing map (SOM)) are applied to set the initial design space and optimization objectives of the transonic centrifugal compressor. A refined design space and objective parameters of the optimization problem are eventually obtained. Finally, the optimization process of a transonic centrifugal compressor is carried out based on the refined design space and objectives using ASHOA. The results show that the search efficiency of the optimization strategy is 2–10 times higher when compared to other excellent optimization algorithms. For the optimized compressor, both isentropic efficiency and total pressure ratio at design condition are improved by 1.61% and 4.13%, respectively, and the maximum stress decreases by 9.68%.
ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-019-02227-5