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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 Power Safety.
KTH, School of Engineering Sciences (SCI), Physics, Nuclear Power Safety.ORCID iD: 0000-0001-8216-9376
KTH, School of Engineering Sciences (SCI), Physics, Nuclear Power Safety.ORCID iD: 0000-0002-9123-2944
KTH, School of Engineering Sciences (SCI), Physics, Nuclear Power Safety.ORCID iD: 0000-0002-0683-9136
2017 (English)In: 17th International Topical Meeting on Nuclear Reactor Thermal Hydraulics, NURETH 2017, Association for Computing Machinery, Inc , 2017Conference paper, Published paper (Refereed)
Abstract [en]

Sever accident mitigation strategy adopted in Nordic Boiling Water Reactors (BWRs) employs a deep water pool below the reactor vessel in order to fragment and quench core melt and provide long term cooling of the debris. One of the risk factors associated with this accident management strategy is early failure of the containment due to steam explosion. Assessment of the risk is subject to significant epistemic and aleatory uncertainties in (i) modelling of steam explosion and (ii) scenarios of melt release from the vessel and water pool conditions. High computational efficiency of the models is required for such assessment. A surrogate model (SM) approach has been previously developed using artificial neural network and the database of Texas-V code solutions for steam explosion loads in the Nordic type BWRs. In this paper we extend our surrogate model to allow analysis of steam explosion in relatively shallow water pools (>2 m), address effects of melt emissivity and resolve more accurately variation of pressure in the drywell. We provide detailed comparison of metallic vs oxidic melt release scenarios, incorporate uncertainty of the SM into modelling and analyze the sensitivity of our results to SM uncertainty. We estimate risks of containment failure with non-reinforced and reinforced hatch door and demonstrate the effect of the surrogate model uncertainty on the results. We analyze the results and develop a simplified approach for decision making considering predicted failure probabilities, expected costs and scenario frequencies. 

Place, publisher, year, edition, pages
Association for Computing Machinery, Inc , 2017.
Keywords [en]
Aleatory and epistemic uncertainties, Artificial Neural Networks, Severe accident, Surrogate model uncertainty, Boiling water reactors, Computational efficiency, Explosions, Failure (mechanical), Fuel additives, Hydraulics, Lakes, Neural networks, Nuclear reactor accidents, Reinforcement, Risk assessment, Risk perception, Steam, Aleatory uncertainty, Failure Probability, Mitigation strategy, Shallow water pools, Surrogate model, Uncertainty quantifications, Uncertainty analysis
National Category
Other Civil Engineering
Identifiers
URN: urn:nbn:se:kth:diva-236832Scopus ID: 2-s2.0-85052599683OAI: oai:DiVA.org:kth-236832DiVA, id: diva2:1273749
Conference
17th International Topical Meeting on Nuclear Reactor Thermal Hydraulics, NURETH 2017, 3 September 2017 through 8 September 2017
Funder
Swedish Radiation Safety Authority
Note

QC 20181221

Available from: 2018-12-21 Created: 2018-12-21 Last updated: 2018-12-21Bibliographically approved

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Grishchenko, DmitryGalushin, SergeyBasso, SimoneKudinov, Pavel

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