Axisymmetric hub-endwall profile optimization for a transonic fan to improve aerodynamic performance based on an integrated design optimization method

Cheng, Yan; Zhao, Yin; Shen, Xiuli; Guo, Fushui; Wu, Yi-Liang

doi:10.1007/s00158-019-02266-y

Cited by 2 publications

(1 citation statement)

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“…As a result, a large number of metamodels have been proposed, of which several types have gained wide acceptance in various applications. They are polynomial response surface (PRS) [12][13][14], support vector regression (SVR) [15][16][17], radial basis functions (RBF) [18,19], extended radial basis functions (E-RBF) [20], moving least squares (MLS) [21], artificial neural networks (ANN) [22,23], multivariate adaptive regressive splines (MARS) [24] and Kriging (KRG) [25,26]. These different metamodels give us more options for different tasks.…”

Section: Introductionmentioning

confidence: 99%

Ensemble of Regression-Type and Interpolation-Type Metamodels

et al. 2020

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Metamodels have become increasingly popular in the field of energy sources because of their significant advantages in reducing the computational cost of time-consuming tasks. Lacking the prior knowledge of actual physical systems, it may be difficult to find an appropriate metamodel in advance for a new task. A favorite way of overcoming this difficulty is to construct an ensemble metamodel by assembling two or more individual metamodels. Motivated by the existing works, a novel metamodeling approach for building the ensemble metamodels is proposed in this paper. By thoroughly exploring the characteristics of regression-type and interpolation-type metamodels, some useful information is extracted from the feedback of the regression-type metamodels to further improve the functional fitting capability of the ensemble metamodels. Four types of ensemble metamodels were constructed by choosing four individual metamodels. Common benchmark problems are chosen to compare the performance of the individual and ensemble metamodels. The results show that the proposed metamodeling approach reduces the risk of selecting the worst individual metamodel and improves the accuracy of the used individual metamodels.

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