Application of Response Surface Method using Rapid Screening, Support Vector Machine, and Multiple Regression on the Acidity and Activity of Si−Al−Zr Ternary Oxide

2010 ◽  
Vol 12 (4) ◽  
pp. 435-444 ◽  
Author(s):  
Kohji Omata ◽  
Yuichiro Yamazaki ◽  
Yasukazu Kobayashi ◽  
Muneyoshi Yamada
Keyword(s):  
Support Vector Machine ◽  
Response Surface ◽  
Multiple Regression ◽  
Response Surface Method ◽  
Rapid Screening ◽  
Support Vector ◽  
Ternary Oxide ◽  
Surface Method

Nonlinear Dynamic Probabilistic Analysis for Turbine Casing Radial Deformation Using Extremum Response Surface Method Based on Support Vector Machine

2013 ◽  
Vol 8 (4) ◽  
Author(s):  
Chengwei Fei ◽  
Guangchen Bai
Keyword(s):  
Support Vector Machine ◽  
Response Surface ◽  
Response Surface Method ◽  
Probabilistic Analysis ◽  
Nonlinear Dynamic ◽  
Nonlinear Material ◽  
Support Vector ◽  
Radial Deformation ◽  
Surface Method ◽  
Turbine Casing

To improve the computational efficiency of nonlinear dynamic probabilistic analysis for aeroengine typical components, an extremum response surface method based on the support vector machine (SVM ERSM) was proposed in this paper. The basic principle was introduced and the mathematical model was established for the SVM ERSM. The probabilistic analysis of turbine casing radial deformation was taken as an example to validate the SVM ERSM considering the influences of nonlinear material property and dynamic heat loads. The results of probabilistic analysis imply that the distribution features of random parameters and the major factors are gained for more accurate the design of casing radial deformation. The SVM ERSM offers a feasible and valid method, which possesses high efficiency and high precision in the nonlinear dynamic probabilistic analysis. Moreover, the SVM ERSM is promising to provide an useful insight for casing dynamic optimal design and the blade-tip clearance control of aeroengine high pressure turbine.

Reliability Analysis of Self-Anchored Suspension Bridge by Improved Response Surface Method

2011 ◽  
Vol 90-93 ◽  
pp. 869-873 ◽  
Author(s):  
Xiao Lin Yu ◽  
Quan Sheng Yan
Keyword(s):  
Reliability Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Structural Reliability ◽  
Limit State ◽  
Suspension Bridge ◽  
Support Vector ◽  
Surface Method ◽  
Reliability Method ◽  
Improved Response Surface Method

The response surface method (RSM) developed in recent years is an effective way to solve the structural reliability problems with implicit performance function. In order to improve the computational efficiency and make RSM suitable well to large and complex engineering structures, the reliability analysis method based on uniform design method (UDM) and support vector machine (SVM) was proposed. UDM is adopted to select training data and SVM is used as response surface. Structural reliability index is calculated in combination with the traditional reliability analysis methods (such as, the first-order reliability method (FORM), the second-order reliability method (SORM) or Monte Carlo simulation method (MCSM)). Numerical examples show that sampled with the UDM can greatly reduce the number of samples required for training by SVM model, and a very good approximation of the limit state surface can be obtained to get the failure probability. The reliability analysis of the under serviceability limit-state of a typical self-anchored suspension bridge——Sanchaji Bridge was carried out with the improved response surface method.

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