Managing Uncertainties in Electromagnetic Design Problems With Robust Optimization

Steiner, Gerald; Weber, Andreas R.; Magele, Christian

doi:10.1109/tmag.2004.824556

Cited by 72 publications

(53 citation statements)

References 13 publications

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“…With SSQ, the measure of dispersion is classified into six sigma (standard deviation) levels [7]; the optimization problem can be reformulated as min μ f and σ f (5) where σ f is the standard deviation indicating the intensity of variation due to the uncertainty of variables and μ f the mean value defining the average performance within the uncertain range. The two parameters, σ f and μ f , for the test function (2) are shown in Figs. 7 and 8, respectively.…”

Section: Ssq Methodsmentioning

confidence: 99%

Six Sigma Quality Approach to Robust Optimization

Xiao

Rotaru

et al. 2015

IEEE Trans. Magn.

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In electromagnetic design, uncertainties in design variables are inevitable, thus in addition to pursuing the theoretical optimum of the objective function the evaluation of robustness of the optimum solution is also critical. Several methodologies exist to tackle robust optimization, such as worst case optimization and gradient index; this paper investigates the use of standard deviation and mean value of objective function under uncertainty of variables. A modified Kriging model with the ability of balancing exploration and exploitation is employed to facilitate the objective function prediction. Two TEAM benchmark problems are solved using different methodologies to compare the advantages and disadvantages of different robust optimization approaches.Index Terms-Gradient index (GI), Kriging, six sigma quality (SSQ) approach, worst case optimization (WCO).

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Section: Ssq Methodsmentioning

confidence: 99%

Six Sigma Quality Approach to Robust Optimization

Xiao

Rotaru

et al. 2015

IEEE Trans. Magn.

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“…The optimal design [15], however, is definitely better than [16] because the former gives a better objective value and a higher reliability at the same time. Between the optimal designs [17] and [18], neither of them can be said a superior design because of the confliction between the objective value and the reliability level.…”

Section: B Uncertainty Is Considered In Physical Parametersmentioning

confidence: 99%

A New Sensitivity-Based Reliability Calculation Algorithm in the Optimal Design of Electromagnetic Devices

Ren¹,

Koh²

2013

Journal of Electrical Engineering and Technology

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-A new reliability calculation method is proposed based on design sensitivity analysis by the finite element method for nonlinear performance constraints in the optimal design of electromagnetic devices. In the proposed method, the reliability of a given design is calculated by using the Monte Carlo simulation (MCS) method after approximating a constraint function to a linear one in the confidence interval with the help of its sensitivity information. The validity and numerical efficiency of the proposed sensitivity-assisted MCS method are investigated by comparing its numerical results with those obtained by using the conventional MCS method and the first-order reliability method for analytic functions and the TEAM Workshop Problem 22.

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“…It can be seen that the AV method only needs twice FEM analysis (one time for (8), and one time for (10)) to obtain both performance and gradient information. …”

Section: Design Sensitivity Analysis and Reliability Analysis 31 Desmentioning

confidence: 99%

Multi-Objective Optimization of Electromagnetic Device Based on Design Sensitivity Analysis and Reliability Analysis

Ren¹,

Park²,

Koh³

2013

The Transactions of The Korean Institute of Electrical Engineer

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-In this paper, for constrained optimization problem, one multi-objective optimization algorithm that ensures both performance robustness and constraint feasibility is proposed when uncertainties are involved in design variables. In the proposed algorithm, the gradient index of objective function assisted by design sensitivity with the help of finite element method is applied to evaluate robustness; the reliability calculated by the sensitivity-assisted Monte Carlo simulation method is used to assess the feasibility of constraint function. As a demonstration, the performance and numerical efficiency of the proposed method is investigated through application to the optimal design of TEAM problem 22--a superconducting magnetic energy storage system.

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Managing Uncertainties in Electromagnetic Design Problems With Robust Optimization

Cited by 72 publications

References 13 publications

Six Sigma Quality Approach to Robust Optimization

Six Sigma Quality Approach to Robust Optimization

A New Sensitivity-Based Reliability Calculation Algorithm in the Optimal Design of Electromagnetic Devices

Multi-Objective Optimization of Electromagnetic Device Based on Design Sensitivity Analysis and Reliability Analysis

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