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RISS 인기검색어
- 검색키워드
- 저자 : Fenfen Xiong (검색결과 2 건)
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A double weighted stochastic response surface method for reliability analysis
Fenfen Xiong 대한기계학회 2012 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.26 No.8
The weighted stochastic response surface method (WSRSM) has been demonstrated to be effective in improving the accuracy of the estimation of statistical moments and probability of failure (PoF) upon the stochastic response surface method (SRSM). However, it has been noticed that the weighting method in WSRSM may have little and sometimes negative impact on PoF estimation especially in the cases of low PoF. To mitigate this issue, a new double weighted SRSM (DWSRSM) is proposed that the weights of sample points are determined based on their importance not only to regression but also to PoF estimation. Specifically, relatively larger weights are assigned to points closer to the failure surface, which significantly accounts for the accuracy of PoF estimation. Comparative studies show that DWSRSM outperforms WSRSM producing more accurate PoF estimation without incurring additional function evaluations. An application of DWSRSM to the rocket design further demonstrates its effectiveness for PoF estimation.
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A moment-matching robust collaborative optimization method
Fenfen Xiong,Gaorong Sun,Ying Xiong,Shuxing Yang 대한기계학회 2014 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.28 No.4
Robust collaborative optimization (RCO) is a widely used approach to design multidisciplinary system under uncertainty. In most ofthe existing RCO frameworks, the mean of the state variable is considered as auxiliary design variable and the implicit uncertainty propagationmethod is employed for estimating their uncertainties (interval or standard deviation), which are then used to calculate uncertaintiesin the ending performances. However, as repeated calculation of the global sensitivity equations (GSE) is demanded during the optimizationprocess of the existing approaches, it is typically very cumbersome or even impossible to obtain GSE for many practical engineeringproblems due to the non-smoothness and discontinuity of the black-box-type analysis models. To address this issue, a new RCOmethod is proposed in this paper, in which the standard deviation of the state variable is introduced as auxiliary design variable in additionto the mean. Accordingly, interdisciplinary compatibility constraint on the standard deviation of state variable is added to enhancethe design compatibility between various disciplines. The effectiveness of the proposed method is demonstrated through two mathematicalexamples. The results generated by the conventional robust all-in-one (RAIO) approach are used as benchmarks for comparison. Ourstudy shows that the optimal solutions produced by the proposed RCO method are highly close to those of RAIO while exhibiting goodinterdisciplinary compatibility.
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