http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Ziyan Ren,Siying He,Liping Zhang,Yanli Qi,Chang Seop Koh 한국자기학회 2020 Journal of Magnetics Vol.25 No.3
Recently, many design problems in the field of electrical engineering tend to be more complex, which are characterized by large scale in size, strong nonlinearity for performance analysis, and multi-dimensional design parameters. Therefore, it is not easy to seek for optimum effectively by traditional optimization algorithms. In order to solve optimal design of complex practical problems, in this paper, a novel hybrid optimization algorithm based on the differential evolution algorithm and the black hole theory is proposed and investigated. The differential evolution (DE) algorithm owns good diversity and flexibility, while the black-hole based optimization algorithm (BHBO) possesses faster convergence. In addition, these two algorithms have simple structures. The proposed algorithm with better merits combination may guarantee better convergence and stronger robustness than its independent counterparts of DE and BHBO. The searching performance is deeply investigated through numerical experiments on benchmark functions and practical electromagnetic applications.
Ren, Ziyan,Zhang, Dianhai,Koh, Chang Seop Emerald Group Publishing Limited 2013 Compel Vol.33 No.1
<B>Purpose</B> - The purpose of this paper is to propose a multi-objective optimization algorithm, which can improve both the performance robustness and the constraint feasibility when the uncertainty in design variables is considered. <B>Design/methodology/approach</B> - Multi-objective robust optimization by gradient index combined with the reliability-based design optimization (RBDO). <B>Findings</B> - It is shown that searching for the optimal design of the TEAM problem 22, which can minimize the magnetic stray field by keeping the target system energy (180?MJ) and improve the feasibility of superconductivity constraint (quenching condition), is possible by using the proposed method. <B>Originality/value</B> - RBDO method applied to the electromagnetic problem cooperated with the design sensitivity analysis by the finite element method.
Ziyan Ren,Doojong Um,Chang-Seop Koh 한국자기학회 2014 Journal of Magnetics Vol.19 No.3
In this paper, we suggest reliability as a metric to evaluate the robustness of a design for the optimal design of electromagnetic devices, with respect to constraints under the uncertainties in design variables. For fast numerical efficiency, we applied the sensitivity-assisted Monte Carlo simulation (S-MCS) method to perform reliability calculation. Furthermore, we incorporated the S-MCS with single-objective and multi-objective particle swarm optimization algorithms to achieve reliability-based optimal designs, undertaking probabilistic constraint and multi-objective optimization approaches, respectively. We validated the performance of the developed optimization algorithms through application to the optimal design of a superconducting magnetic energy storage system.
Ziyan Ren,Hyunjin Cho,Junmo Yeon,Chang-Seop Koh IEEE 2015 IEEE transactions on magnetics Vol.51 No.3
<P>Due to the existence of randomness and fuzziness in engineering problems, the reliable performance prediction is very complex. In addition, in the early design stage of a new product, the insufficient information on uncertainty further aggravates the difficulty of performance prediction and robustness evaluation. This paper suggests a new reliability analysis method using possibility theory to evaluate constraint feasibility even with insufficient uncertainty data in design variables. In order to save the computing time, two strategies of approximating performance constraints are proposed: 1) design sensitivity-based method and 2) dynamic Kriging-based method. Through applications to analytic example and engineering problem, the proposed methods are investigated and discussed.</P>
Ziyan Ren,Dianhai Zhang,Chang Seop Koh 대한전기학회 2013 Journal of Electrical Engineering & Technology Vol.8 No.2
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.
Numerically Efficient Algorithm for Reliability-Based Robust Optimal Design of TEAM Problem 22
Ziyan Ren,Chanhyuk Park,Chang-Seop Koh IEEE 2014 IEEE transactions on magnetics Vol.50 No.2
<P>The robust optimization and reliability-based optimization have been proven effective to deal with uncertainties in design variables. However, there are scarcely any publications about comparison of robust and reliable designs, not to mention the combination of robustness and reliability in the electrical engineering. In this paper, the optimal design of superconducting magnetic energy storage system is taken as an example to comparatively investigate robust and reliable designs. Furthermore, the performance robustness and constraint feasibility are integrated into a single optimization model - reliability-based robust design optimization (RBRDO). The proposed RBRDO formulation yields results that provide new alternatives to the designer.</P>
Ziyan Ren,Minh-Trien Pham,Minho Song,Dong-Hun Kim,Chang Seop Koh IEEE 2011 IEEE transactions on magnetics Vol.47 No.5
<P>An effective methodology for a robust global optimization of electromagnetic devices is developed based on the gradient index and multi-objective optimization method. The method transforms a given optimization problem into a multi-objective optimization one by adding another optimization target for minimizing the gradient index. The performance and robustness of the obtained optimal designs from the proposed algorithm are investigated through a numerical experiment with the TEAM Workshop Problem 22.</P>
Ren, Ziyan,Zhang, Dianhai,Koh, Chang Seop The Korean Institute of Electrical Engineers 2013 Journal of Electrical Engineering & Technology Vol.8 No.2
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.
Ren, Ziyan,Koh, Chang-Seop The Korean Institute of Electrical Engineers 2013 Journal of Electrical Engineering & Technology Vol.8 No.4
In the reliability-based design optimization of electromagnetic devices, the accurate and efficient reliability assessment method is very essential. The first-order sensitivity-assisted Monte Carlo Simulation is proposed in the former research. In order to improve its accuracy for wide application, in this paper, the second-order sensitivity analysis is presented by using the hybrid direct differentiation-adjoint variable method incorporated with the finite element method. By combining the second-order sensitivity with the Monte Carlo Simulation method, the second-order sensitivity-assisted Monte Carlo Simulation algorithm is proposed to implement reliability calculation. Through application to one superconductor magnetic energy storage system, its accuracy is validated by comparing calculation results with other methods.