This paper proposes a statistical probability approach to evaluate the quality loss function of electromagnetic design problems, which is expressed in terms of the first two statistical moments, mean and variance. A univariate dimension reduction method is employed to calculate the statistical moments of a performance function and their sensitivities accurately, and efficiently. Finally, the method is integrated into the robust design optimization algorithm. The proposed method explores an optimum design with statistical information so that it can provide a more accurate solution than other robust design methods. The proposed method is tested with a mathematical model and a blushless DC motor, and its numerical accuracy and efficiency are examined by comparison with existing methods.

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This paper proposes an efficient reliability-based optimization method for designing a superconducting magnetic energy system in presence of uncertainty. To evaluate the probability of failure of constraints, samplingbased reliability analysis method is employed, where Monte Carlo simulation is incorporated into dynamic Kriging models. Its main feature is to drastically reduce the numbers of iterative designs and computer simulations during the optimization process without sacrificing the accuracy of reliability analysis. Through comparison with existing methods, the validity of the proposed method is examined with the TEAM Workshop Problem 22.

This paper proposes a sequential optimization methodology for designing an electrothermal polysilicon actuator in the presence of a fabrication tolerance. In the proposed method, a deterministic optimum is first sought from an initial design, and then a reliability-based robust design is obtained launching at the deterministic point. This serial design strategy can enhance numerical efficiency through minimizing the use of computationally expensive reliability-based design optimization. To effectively perform the robust design of very complex multiphysics problems, elaborate surrogate models based on the local window concept are exploited comprehensively. The proposed method is applied to an electrothermal polysilicon actuator with seven design random variables, and then three different nominal designs are examined in terms of maximum deflection, consumed power, and confidence level.

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