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Design Sensitivity Method for Sampling-Based RBDO With Varying Standard Deviation
Cho, Hyunkyoo,Choi, K. K.,Lee, Ikjin,Lamb, David ASME International 2016 Journal of Mechanical Design Vol. No.
<P>Conventional reliability-based design optimization (RBDO) uses the mean of input random variable as its design variable; and the standard deviation (STD) of the random variable is a fixed constant. However, the constant STD may not correctly represent certain RBDO problems well, especially when a specified tolerance of the input random variable is present as a percentage of the mean value. For this kind of design problem, the STD of the input random variable should vary as the corresponding design variable changes. In this paper, a method to calculate the design sensitivity of the probability of failure for RBDO with varying STD is developed. For sampling-based RBDO, which uses Monte Carlo simulation (MCS) for reliability analysis, the design sensitivity of the probability of failure is derived using a first-order score function. The score function contains the effect of the change in the STD in addition to the change in the mean. As copulas are used for the design sensitivity, correlated input random variables also can be used for RBDO with varying STD. Moreover, the design sensitivity can be calculated efficiently during the evaluation of the probability of failure. Using a mathematical example, the accuracy and efficiency of the developed design sensitivity method are verified. The RBDO result for mathematical and physical problems indicates that the developed method provides accurate design sensitivity in the optimization process.</P>
Kang, Byungsu,Kim, Chang-Eob,Cho, Hyunkyoo,Choi, K. K.,Kim, Dong-Hun IEEE 2018 IEEE transactions on magnetics Vol.54 No.3
<P>A hybrid reliability analysis method is proposed to yield very accurate failure rate calculation of a performance function when dealing with highly nonlinear electromagnetic systems in the presence of uncertainties. To achieve this goal, the first-order reliability method called reliability index approach is first conducted for searching a most probable failure point (MPP) at a given design. However, its result may have significant errors especially for nonlinear or multi-dimensional performance functions. To overcome the drawback, the univariate dimension reduction method is additionally executed at the obtained MPP, and then the probability of failure of a performance function is recalculated through additively decomposing an n-dimensional function into n 1-D functions. A mathematical example and TEAM workshop problem 22 are provided to demonstrate numerical efficiency and accuracy of the proposed method by comparison with the existing reliability methods.</P>
Kang, Byungsu,Kim, Dong-Wook,Cho, Hyunkyoo,Choi, K. K.,Kim, Dong-Hun IEEE 2017 IEEE transactions on magnetics Vol.53 No.6
<P>This paper proposes an efficient and stable reliability analysis method for reliability-based electromagnetic design problems with non-normal probability distributions of input parameters. The reliability analysis strongly depends on distribution types of random variables since nonlinear transformations between an original random space and a standard normal random space cause additional nonlinearity into the reliability assessment of probabilistic constraint functions. That can lead to numerical inaccuracy and instability in the reliability-based design process, or may fail to have a solution to the probabilistic constraint assessment. To overcome these difficulties, a hybrid mean-value method is introduced to seeking a most probable failure point in the performance measure approach, which is one of the first-order reliability analysis methods. The proposed method is tested with a mathematical model and a loudspeaker design, of which random variables are assumed to follow five different probability distributions case by case.</P>
자동차 브레이크 패드 마모량 측정센서 브라켓의 다이나믹크리깅 대리모델 기반 설계최적화
정준영,유정주,변경석,조현규,Jun-Yeong Jeong,Jung Joo Yoo,Kyung Seok Byun,Hyunkyoo Cho 한국전산구조공학회 2024 한국전산구조공학회논문집 Vol.37 No.2
This paper introduces an optimized design for a sensor bracket used to measure the wear amount of an automobile brake pad, based on a dynamic kriging surrogate model. During testing, the temperature of the brake pad can increase beyond 600℃, which often causes sensor malfunction. Therefore, it is essential to optimize the shape of the sensor bracket to minimize heat transfer. To reduce the computational cost of the optimization, the heat-transfer simulation is replaced by a dynamic kriging surrogate model. Dynamic kriging utilizes the best combination of correlation and basis functions and constructs an accurate surrogate model. Following optimization, the temperature of the sensor position decreases by 7.57%. The results from the surrogate model under optimum conditions are verified by a heat-transfer simulation, and the design optimization using a surrogate model is found to be effective.