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Bayesian optimization analysis of containment venting operation in a BWR severe accident

BWRのシビアアクシデント時における格納容器ベント操作に対するベイズ的最適化解析

Zheng, X.; 石川 淳; 杉山 智之; 丸山 結

Zheng, X.; Ishikawa, Jun; Sugiyama, Tomoyuki; Maruyama, Yu

Containment venting is one of essential measures to protect the integrity of the final barrier of a nuclear reactor, by which the uncontrollable release of fission products can be avoided. The authors seek to develop an optimization approach to the planning of containment-venting operations by using THALES2/KICHE. Factors that control the activation of the venting system, for example, containment pressure, amount of fission products within the containment and pH value in the suppression chamber water pool, will affect radiological consequences. The effectiveness of containment venting strategies needs to be confirmed through numerical simulations. The number of iterations, however, needs to be controlled for cumbersome computational burden of severe accident codes. Bayesian optimization is a computationally efficient global optimization approach to find desired solutions. With the use of Gaussian process regression, a surrogate model of the "black-box" code is constructed. According to the predictions through the surrogate model, the upcoming location of the most probable optimum can be revealed. The number of code queries is largely reduced for the optimum finding, compared with simpler methods such as pure random search. The research demonstrates the applicability of the Bayesian optimization approach to the design and establishment of containment-venting strategies under BWR severe accident conditions.

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