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Failure domain analysis and uncertainty quantification using surrogate models for steam explosion in a Nordic type BWR
KTH, School of Engineering Sciences (SCI), Physics, Nuclear Engineering.
KTH, School of Engineering Sciences (SCI), Physics, Nuclear Engineering.ORCID iD: 0000-0001-8216-9376
KTH, School of Engineering Sciences (SCI), Physics, Nuclear Engineering.
2019 (English)In: Nuclear Engineering and Design, ISSN 0029-5493, E-ISSN 1872-759X, Vol. 343, p. 63-75Article in journal (Refereed) Published
Abstract [en]

Sever accident mitigation strategy adopted in Nordic Boiling Water Reactors (BWRs) employs a deep water pool below the reactor vessel to fragment and quench core melt and provide long term cooling of the debris. One of the risks associated with this strategy is early containment failure due to ex-vessel steam explosion. Assessment of the risk of steam explosion is subject to significant (i) epistemic uncertainties in modelling and (ii) aleatory uncertainties in scenarios of melt release. For quantification of the uncertainties and the risk a full model (FM) based on TEXAS-V code and a computationally efficient surrogate model (SM) have been previously developed. FM is used to provide a database of solutions that is used for development of a SM, while SM is used in extensive sensitivity and uncertainty analysis. In this work, we compare the risk of containment failure with non-reinforced and reinforced hatch door for metallic and oxidic melt release scenarios. We quantify the error of SM in the approximation of the FM and assess the effect of the approximation uncertainty on risk assessment. We analyze the results and suggest a simplified approach for decision making considering predicted failure probabilities, expected costs, and scenario frequencies.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE SA , 2019. Vol. 343, p. 63-75
Keywords [en]
Severe accident, Artificial neural networks, Aleatory and epistemic uncertainties, Surrogate model uncertainty
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:kth:diva-244081DOI: 10.1016/j.nucengdes.2018.12.013ISI: 000456923500007Scopus ID: 2-s2.0-85059446676OAI: oai:DiVA.org:kth-244081DiVA, id: diva2:1289925
Note

QC 20190219

Available from: 2019-02-19 Created: 2019-02-19 Last updated: 2019-02-19Bibliographically approved

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Galushin, Sergey

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