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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2017.1
- 수록면
- 434 - 441 (8page)
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초록· 키워드
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Containment venting is one of several essential measures to protect the integrity of thefinal barrier of a nuclear reactor during severe accidents, by which the uncontrollablerelease of fission products can be avoided. The authors seek to develop an optimizationapproach to venting operations, from a simulation-based perspective, using an integratedsevere accident code, THALES2/KICHE. The effectiveness of the containment-ventingstrategies needs to be verified via numerical simulations based on various settings of theventing conditions. The number of iterations, however, needs to be controlled to avoidcumbersome computational burden of integrated codes. Bayesian optimization is an efficientglobal optimization approach. By using a Gaussian process regression, a surrogatemodel of the “black-box” code is constructed. It can be updated simultaneously whenevernew simulation results are acquired. With predictions via the surrogate model, upcominglocations of the most probable optimum can be revealed. The sampling procedure isadaptive. Compared with the case of pure random searches, the number of code queries islargely reduced for the optimum finding. One typical severe accident scenario of a boilingwater reactor is chosen as an example. The research demonstrates the applicability of theBayesian optimization approach to the design and establishment of containment-ventingstrategies during severe accidents.
목차
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참고문헌 (19)
참고문헌 신청
A. O'Hagan / 1999 / Uncertainty analysis and other inference tools for complex computer codes / Proceedings of the Sixth Valencia International Meeting : 503 ~ 524
B. Shahriari / 2016 / Taking the human out of the loop : a review of Bayesian Optimization / Proc. IEEE 104 : 48 ~ 175
B.R. Sehgal / 2012 / Nuclear safety in light water reactors: severe accident phenomenology / Academic Press, Elsevier
C. E. Rasmussen / 2006 / Gaussian processes for machine learning / The MIT Press
E. Brochu / 2009 / A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning
B. Shahriari / 2016 / Taking the human out of the loop : a review of Bayesian Optimization / Proc. IEEE 104 : 48 ~ 175
B.R. Sehgal / 2012 / Nuclear safety in light water reactors: severe accident phenomenology / Academic Press, Elsevier
C. E. Rasmussen / 2006 / Gaussian processes for machine learning / The MIT Press
E. Brochu / 2009 / A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning
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