Robust 3-D Shape Optimization of Electromagnetic Devices by Combining Sensitivity Analysis and Adaptive Geometric Parameterization

Yao, Yingying; Ryu, Jae Seop; Koh, Chang Seop; Xie, Dexin

doi:10.1109/tmag.2004.824578

Cited by 3 publications

(3 citation statements)

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“…Graphically, one of these three positions would be the solution for the optimization problem in (9) under a certain criterion of variance given as V crit . Another two ways to obtain variance: Taylor series expanded based method and Monte Carlo Simulation are employed to calculate the response variances centered at positions (1, 1), (1,3) and (3,2), and the results are compared with Sobol'-based method in Table 1. It can be seen that results from Sobol'-based method are close to those from MCS, which are considered accurate with a relatively large sample size (i.e., 100,000).…”

Section: Analytic Functionmentioning

confidence: 99%

“…A number of robust optimization methods have been proposed to be applied to electromagnetic devices [2][3][4][5][6]. A large part of the optimization approaches use the gradient index (GI) of parameters to represent the robustness due to its simplicity and low computational cost [7].…”

Section: Introductionmentioning

confidence: 99%

“…A large part of the optimization approaches use the gradient index (GI) of parameters to represent the robustness due to its simplicity and low computational cost [7]. GI methods based on the sensitivity analysis (SA), especially the first-order SA, are extensively reported to evaluate the robustness of a design [2,3]. In order to provide more accurate results under uncertainty, higher order (i.e., secondorder) sensitivity analysis is employed in Ref.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Robust Optimization Method Utilizing the Variance Decomposition Method for Electromagnetic Devices

Wang¹,

Li²,

Chen³

2014

Journal of Magnetics

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Uncertainties in loads, materials and manufacturing quality must be considered during electromagnetic devices design. This paper presents an effective methodology for robust optimization design based on the variance decomposition in order to keep higher accuracy of the robustness prediction. Sobol' theory is employed to estimate the response variance under some specific tolerance in design variables. Then, an optimal design is obtained by adding a criterion of response variance upon typical optimization problems as a constraint of the optimization. The main contribution of this paper is that the proposed method applies the variance decomposition to obtain a more accurate variance of the response, as well save the computational cost. The performance and robustness of the proposed algorithms are investigated through a numerical experiment with both an analytic function and the TEAM 22 problem.

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