scholarly journals Optimal Design of a BLDC Motor Considering Three-Dimensional Structures Using the Response Surface Methodology

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 461
Author(s):  
Seong-Tae Jo ◽  
Hyo-Seob Shin ◽  
Young-Geun Lee ◽  
Ji-Hun Lee ◽  
Jang-Young Choi

In this paper, the optimal design of a brushless direct current motor with a three-dimensional (3D) structure using the response surface methodology (RSM) is presented. There were two optimization goals: reduction of the cogging torque and maintenance of the back electromotive force to prevent performance degradation. For motors with a 3D structure, a 3D finite element method analysis is essential, though it requires considerable computation time. Therefore, to reduce the optimal design time, the 3D structure was placed on the 2D plane. Thereafter, a 2D corrected model was applied to the RSM. For the validity of the technique, the analysis results of the initial 3D model, 2D model, and 2D corrected model were compared, and the results of the optimal design 3D model, 2D corrected model, and experiment were compared.

2006 ◽  
Vol 42 (4) ◽  
pp. 1219-1222 ◽  
Author(s):  
Sung-Il Kim ◽  
Jung-Pyo Hong ◽  
Young-Kyoun Kim ◽  
Hyuk Nam ◽  
Han-Ik Cho

Author(s):  
Xingzhi Hu ◽  
Yanhui Duan ◽  
Ruili Wang ◽  
Xiao Liang ◽  
Jiangtao Chen

Abstract The popular use of response surface methodology (RSM) accelerates the solutions of parameter identification and response analysis issues. However, accurate RSM models subject to aleatory and epistemic uncertainties are still challenging to construct, especially for multidimensional inputs, which is widely existed in real-world problems. In this study, an adaptive interval response surface methodology (AIRSM) based on extended active subspaces is proposed for mixed random and interval uncertainties. Based on the idea of subspace dimension reduction, extended active subspaces are given for mixed uncertainties, and interval active variable representation is derived for the construction of AIRSM. A weighted response surface strategy is introduced and tested for predicting the accurate boundary. Moreover, an interval dynamic correlation index is defined, and significance check and cross validation are reformulated in active subspaces to evaluate the AIRSM. The effectiveness of AIRSM is demonstrated on two test examples: three-dimensional nonlinear function and speed reducer design. They both possess a dominant one-dimensional active subspace with small estimation error, and the accuracy of AIRSM is verified by comparing with full-dimensional Monte Carlo simulates, thus providing a potential template for tackling high-dimensional problems involving mixed aleatory and interval uncertainties.


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