Reliability-Based Design Optimization of a Superconducting Magnetic Energy Storage System (SMES) Utilizing Reliability Index Approach

Jeung, Gi-Woo; Kim, Dong-Wook; Sung, Young Hwa; Kim, Heung-Geun; Kim, Dong-Hun

doi:10.4283/jmag.2012.17.1.046

Cited by 15 publications

(24 citation statements)

References 6 publications

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“…2 [10,11]. For solving (5), the Hasofer-Lind and Rackwitz-Fiessler (HL-RF) method is employed here because of its simplicity and efficiency [11][12][13].…”

Section: Reliability Analysis Using Riamentioning

confidence: 99%

“…3. Such a double-loop optimisation structure inherently demands very high computational cost because the sub-optimisation problem of (5) has to be solved for each probabilistic constraint function given at every design point [11][12][13][14][15].…”

Section: Implementation Of Rbdo Based On Riamentioning

confidence: 99%

“…Generally, DDO is performed by two representative methodologies: one is the sampling-based approach, such as evolution strategy, genetic algorithm, particle swarm and so on; the other is the sensitivity-based approach employing design sensitivity values. Unlike DDO, not enough intensive research on the issue of uncertainty in EM designs has been conducted so far [1][2][3][4][5][6][7][8][9][10][11]. Probabilistic design optimisation can be classified into two approaches: robust design optimisation (RDO) and reliability-based design optimisation (RBDO).…”

Section: Introductionmentioning

confidence: 99%

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Efficient strategies for reliability‐based design optimisation of a superconducting magnetic energy storage system‐based on reliability index approach

Kim¹,

Jeung²,

Choi

et al. 2013

IET Science, Measurement & Technology

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This study presents an enriched reliability index approach for reliability-based design optimisation of electromagnetic devices in the presence of variability in design variables. To substantially improve computational efficiency, two new optimisation strategies consisting of a constraint indicator condition and a successive optimum search technique are incorporated into the existing method based on the reliability index approach. The constraint indicator decides whether reliability analysis is needed for each intermediate design during the reliability-based optimisation process. Employing the successive optimum search technique, a deterministic optimum is first sought from an initial design and then a probabilistic optimum is obtained by launching at the deterministic optimum. The proposed method is tested with a coil design problem for a superconducting magnetic energy storage system, and is thoroughly investigated by comparing the results with the conventional method based on the reliability index approach and the Monte Carlo simulation in terms of computational efficiency and accuracy. Finally, the results show that the method leads to a significantly decreasing computational cost.

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“…2 [10,11]. For solving (5), the Hasofer-Lind and Rackwitz-Fiessler (HL-RF) method is employed here because of its simplicity and efficiency [11][12][13].…”

Section: Reliability Analysis Using Riamentioning

confidence: 99%

Section: Implementation Of Rbdo Based On Riamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient strategies for reliability‐based design optimisation of a superconducting magnetic energy storage system‐based on reliability index approach

Kim¹,

Jeung²,

Choi

et al. 2013

IET Science, Measurement & Technology

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show abstract

“…So the efficiency is very low. There are mainly two strategies to improve the efficiency of the RBMDO: (1) improve the reliability analysis efficiency, such as reliability index approach (RIA) 41 and PMA 42 and (2) change the architecture of RBMDO.…”

Section: Rbmdomentioning

confidence: 99%

Hybrid reliability-based multidisciplinary design optimization with random and interval variables

Yang

Yue

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part O:

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This article presents a procedure for reliability-based multidisciplinary design optimization with both random and interval variables. The sign of performance functions is predicted by the Kriging model which is constructed by the so-called learning function in the region of interest. The Monte Carlo simulation with the Kriging model is performed to evaluate the failure probability. The sample methods for the random variables, interval variables, and design variables are discussed in detail. The multidisciplinary feasible and collaborative optimization architectures are provided with the proposed method. The method is demonstrated with three examples.

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“…To implement such reliability analysis, an additional optimization problem, of which the goal is to find the most probable failure point (MPP) in the normal design space, has to be solved. Consequently, it is revealed that RBDO has a doubleloop optimization structure [6]- [8]: to find an optimum of the objective function as a main loop, and to satisfy the given probabilities of constraints as a sub-loop. In spite of the benefit of RBDO, such a double-loop strategy inevitably demands an extremely high computational cost.…”

Section: Introductionmentioning

confidence: 99%

A Single-Loop Strategy for Efficient Reliability-Based Electromagnetic Design Optimization

Kim

Choi

et al. 2015

IEEE Trans. Magn.

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This paper proposes a single-loop optimization strategy for efficient reliability-based electromagnetic design in the presence of uncertainty. Unlike conventional reliability-based optimization methods, optimization and reliability assessment are totally decoupled from each other during the overall design process. To achieve the desired design feasibility, constraint boundaries of an original design problem are shifted to feasible directions determined by the reliability information of a previous design. After all, the proposed single-loop method for reliability-based optimization performs a serial design of deterministic optimization with shifting constraints and reliability analysis. The proposed method is tested with a mathematical problem and the TEAM Workshop Problem 22, and its numerical accuracy and efficiency is examined by comparison with existing methods.

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Reliability-Based Design Optimization of a Superconducting Magnetic Energy Storage System (SMES) Utilizing Reliability Index Approach

Cited by 15 publications

References 6 publications

Efficient strategies for reliability‐based design optimisation of a superconducting magnetic energy storage system‐based on reliability index approach

Efficient strategies for reliability‐based design optimisation of a superconducting magnetic energy storage system‐based on reliability index approach

Hybrid reliability-based multidisciplinary design optimization with random and interval variables

A Single-Loop Strategy for Efficient Reliability-Based Electromagnetic Design Optimization

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