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Bayesian optimization analysis of containment-venting operation in a Boiling Water Reactor severe accident

BWR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 during severe accidents, by which the uncontrollable release of fission products can be avoided. The authors seek to develop an optimization approach, from a simulation-based perspective, to the venting operations by using an integrated severe accident code, THALES2/KICHE. The effectiveness of containment venting strategies needs to be verified via numerical simulations based on various settings of venting conditions. The number of iterations, however, needs to be controlled for cumbersome computational burden of integrated codes. Bayesian optimization is an efficient global optimization approach. By using Gaussian process regression, a surrogate model of the "black-box" code is constructed. It can be updated simultaneously whenever new simulation results are acquired. With predictions via the surrogate model, upcoming locations of the most probable optimum can be revealed. The sampling procedure is adaptive. The number of code queries is largely reduced for the optimum finding, compared with pure random searches. One typical severe accident scenario of a boiling water reactor is chosen as an example. The research demonstrates the applicability of the Bayesian optimization approach to the design and establishment of containment-venting strategies during severe accidents.

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