Structural Design for CF/GF Hybrid wind turbine blade using Multi-Objective Genetic Algorithm and Kriging Model Response Surface Method

2007 ◽  
Author(s):  
Akira Todoroki ◽  
Yuki Kawakami
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbine ◽  
Response Surface ◽  
Turbine Blade ◽  
Response Surface Method ◽  
Structural Design ◽  
Kriging Model ◽  
Surface Method ◽  
Multi Objective Genetic Algorithm ◽  
Model Response

scholarly journals Sinking Velocity Impact-Analysis for the Carrier-Based Aircraft Using the Response Surface Method-Based Improved Kriging Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Xiao-Feng Xue ◽  
Yuan-Zhuo Wang ◽  
Cheng Lu ◽  
Zhang Yun-Peng
Keyword(s):  
Genetic Algorithm ◽  
Response Surface ◽  
Response Surface Method ◽  
Relative Error ◽  
Impact Analysis ◽  
Kriging Model ◽  
Surface Method ◽  
Approach Velocity ◽  
The Impact ◽  
Sinking Velocity

The deck landing sinking velocity of carrier-based aircraft is affected by carrier attitude, sea condition, aircraft performance, etc. Its impact analysis is a complex nonlinear problem, and there even is some contradictory phenomenon that when the approach velocity increases, the sinking velocity decreases under certain circumstances. Aiming at exploring the impact of the various related deck landing parameters on sinking velocity for carrier-based aircraft in the actual environment, response surface method-based improved Kriging algorithm (IK-RSM) is proposed based on genetic algorithm and Kriging model. Based on the deck landing measured data of the F/A-18A aircraft in the actual operating environment, the impact degree of the 15 deck landing parameters on the sinking velocity is explored, respectively, by using the partial correlation analysis of multivariate statistical theory and the IK-RSM. It can be found that the 4 parameters are strongly correlated with the sinking velocity; that is, the aircraft glide angle and deck pitch angle are highly correlated with the sinking velocity; next, the approach velocity and the engaging velocity are moderately correlated with the sinking velocity. The 4 parameters above could be used to establish the impact analysis model of the sinking velocity. The genetic algorithm is applied to the correction coefficients optimization of the IK-RSM’s kernel functions, and the IK-RSM of the F/A-18A aircraft sinking velocity is formed. Compared with the Kriging model and the empirical formula, the sinking velocity prediction accuracy indexes of IK-RSM are the best; for example, the determination coefficient is 0.981, the mean relative error is 1.813%, and the maximum relative error is 6.771%. Furthermore, based on the sinking velocity IK-RSM and the sensitivity analysis method proposed, we have explained the reason for the contradictory phenomenon that when the approach velocity increases, the sinking velocity decreases at some samples. It could provide certain technical support for the flight attitude control related to the sinking velocity during the actual flight of carrier-based aircraft.

An optimal sub-structure for a TLP-type wind turbine based on neuro-response surface method

2015 ◽  
Vol 20 (4) ◽  
pp. 604-616 ◽  
Author(s):  
Jae-chul Lee ◽  
Sung-chul Shin ◽  
Soo-young Kim ◽  
Andreas Kraus ◽  
Jeong-eun Lee
Keyword(s):  
Wind Turbine ◽  
Response Surface ◽  
Response Surface Method ◽  
Surface Method
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