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Prediction of In-Vessel Debris Bed Properties in BWR Severe Accident Scenarios using MELCOR and Neural Networks
KTH, School of Engineering Sciences (SCI), Physics, Nuclear Power Safety.
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: 0000-0002-0683-9136
2017 (English)In: Annals of Nuclear Energy, ISSN 0306-4549, E-ISSN 1873-2100Article in journal (Refereed) Submitted
Abstract [en]

Severe accident management strategy in Nordic boiling water reactors (BWRs) employs ex-vessel corium debris coolability. In-vessel core degradation and relocation provide initial conditions for further accident progression. Characteristics of debris bed in the reactor vessel lower plenum affect corium interactions with the vessel structures, vessel failure and melt ejection mode. Melt release from the vessel determines conditions for ex-vessel accident progression and potential threats to containment integrity, i.e. steam explosion, debris bed formation and coolability. Outcomes of core relocation depend on the interplay between (i) accident scenarios, e.g. timing and characteristics of failure and recovery of safety systems and (ii) accident phenomena. Uncertainty analysis is necessary for comprehensive risk assessment. However, computational efficiency of system analysis codes such as MELCOR is one of the big obstacles. Another problem is that code predictions can be quite sensitive to small variations of the input parameters and numerical discretization, due to the highly non-linear feedbacks and discrete thresholds employed in the code models.

 

The goal of this work is to develop a computationally efficient surrogate model (SM) for prediction of main characteristics of corium debris in the vessel lower plenum of a Nordic BWR. Station black out scenario with a delayed power recovery is considered. The SM has been developed using artificial neural networks (ANNs). The networks were trained with a database of MELCOR solutions. The effect of the noisy data in the full model (FM) database was addressed by introducing scenario classification (grouping) according to the ranges of the output parameters. SMs using different number of scenario groups with/without weighting between predictions of different ANNs were compared. The obtained SM can be used for failure domain and failure probability analysis in the risk assessment framework for Nordic BWRs.

Place, publisher, year, edition, pages
2017.
Keyword [en]
Core relocation; boiling water reactor; MELCOR; surrogate model; artificial neural network.
National Category
Other Engineering and Technologies not elsewhere specified
Identifiers
URN: urn:nbn:se:kth:diva-202955OAI: oai:DiVA.org:kth-202955DiVA: diva2:1079542
Note

QC 20170309

Available from: 2017-03-08 Created: 2017-03-08 Last updated: 2017-03-09Bibliographically approved
In thesis
1. Input Calibration, Code Validation and Surrogate Model Development for Analysis of Two-phase Circulation Instability and Core Relocation Phenomena
Open this publication in new window or tab >>Input Calibration, Code Validation and Surrogate Model Development for Analysis of Two-phase Circulation Instability and Core Relocation Phenomena
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Code validation and uncertainty quantification are important tasks in nuclear reactor safety analysis. Code users have to deal with large number of uncertain parameters, complex multi-physics, multi-dimensional and multi-scale phenomena. In order to make results of analysis more robust, it is important to develop and employ procedures for guiding user choices in quantification of the uncertainties.

 

The work aims to further develop approaches and procedures for system analysis code validation and application to practical problems of safety analysis. The work is divided into two parts.

 

The first part presents validation of two reactor system thermal-hydraulic (STH) codes RELAP5 and TRACE for prediction of two-phase circulation flow instability.

 

The goals of the first part are to: (a) develop and apply efficient methods for input calibration and STH code validation against unsteady flow experiments with two-phase circulation flow instability, and (b) examine the codes capability to predict instantaneous thermal hydraulic parameters and flow regimes during the transients.

 

Two approaches have been developed: a non-automated procedure based on separate treatment of uncertain input parameters (UIPs) and an automated method using genetic algorithm. Multiple measured parameters and system response quantities (SRQs) are employed in both calibration of uncertain parameters in the code input deck and validation of RELAP5 and TRACE codes. The effect of improvement in RELAP5 flow regime identification on code prediction of thermal-hydraulic parameters has been studied.

 

Result of the code validations demonstrates that RELAP5 and TRACE can reproduce qualitative behaviour of two-phase flow instability. However, both codes misidentified instantaneous flow regimes, and it was not possible to predict simultaneously experimental values of oscillation period and maximum inlet flow rate. The outcome suggests importance of simultaneous consideration of multiple SRQs and different test regimes for quantitative code validation.

 

The second part of this work addresses core degradation and relocation to the lower head of a boiling water reactor (BWR). Properties of the debris in the lower head provide initial conditions for vessel failure, melt release and ex-vessel accident progression.

 

The goals of the second part are to: (a) obtain a representative database of MELCOR solutions for characteristics of debris in the reactor lower plenum for different accident scenarios, and (b) develop a computationally efficient surrogate model (SM) that can be used in extensive uncertainty analysis for prediction of the debris bed characteristics.

 

MELCOR code coupled with genetic algorithm, random and grid sampling methods was used to generate a database of the full model solutions and to investigate in-vessel corium debris relocation in a Nordic BWR. Artificial neural networks (ANNs) with classification (grouping) of scenarios have been used for development of the SM in order to address the issue of chaotic response of the full model especially in the transition region.

 

The core relocation analysis shows that there are two main groups of scenarios: with relatively small (<20 tons) and large (>100 tons) amounts of total relocated debris in the reactor lower plenum. The domains are separated by transition regions, in which small variation of the input can result in large changes in the final mass of debris.  SMs using multiple ANNs with/without weighting between different groups effectively filter out the noise and provide a better prediction of the output cumulative distribution function, but increase the mean squared error compared to a single ANN.

Abstract [sv]

Validering av datorkoder och kvantifiering av osäkerhetsfaktorer är viktiga delar vid säkerhetsanalys av kärnkraftsreaktorer. Datorkodanvändaren måste hantera ett stort antal osäkra parametrar vid beskrivningen av fysikaliska fenomen i flera dimensioner från mikro- till makroskala. För att göra analysresultaten mer robusta, är det viktigt att utveckla och tillämpa rutiner för att vägleda användaren vid kvantifiering av osäkerheter.Detta arbete syftar till att vidareutveckla metoder och förfaranden för validering av systemkoder och deras tillämpning på praktiska problem i säkerhetsanalysen. Arbetet delas in i två delar.Första delen presenterar validering av de termohydrauliska systemkoderna (STH) RELAP5 och TRACE vid analys av tvåfasinstabilitet i cirkulationsflödet.Målen för den första delen är att: (a) utveckla och tillämpa effektiva metoder för kalibrering av indatafiler och validering av STH mot flödesexperiment med tvåfas cirkulationsflödeinstabilitet och (b) granska datorkodernas förmåga att förutsäga momentana termohydrauliska parametrar och flödesregimer under transienta förlopp.Två metoder har utvecklats: en icke-automatisk procedur baserad på separat hantering av osäkra indataparametrar (UIPs) och en automatiserad metod som använder genetisk algoritm. Ett flertal uppmätta parametrar och systemresponser (SRQs) används i både kalibrering av osäkra parametrar i indatafilen och validering av RELAP5 och TRACE. Resultatet av modifikationer i hur RELAP5 identifierar olika flödesregimer, och särskilt hur detta påverkar datorkodens prediktioner av termohydrauliska parametrar, har studerats.Resultatet av valideringen visar att RELAP5 och TRACE kan återge det kvalitativa beteende av två-fas flödets instabilitet. Däremot kan ingen av koderna korrekt identifiera den momentana flödesregimen, det var därför ej möjligt att förutsäga experimentella värden på svängningsperiod och maximal inloppsflödeshastighet samtidigt. Resultatet belyser betydelsen av samtidig behandling av flera SRQs liksom olika experimentella flödesregimer för kvantitativ kodvalidering.Den andra delen av detta arbete behandlar härdnedbrytning och omfördelning till reaktortankens nedre plenumdel i en kokarvatten reaktor (BWR). Egenskaper hos härdrester i nedre plenum ger inledande förutsättningar för reaktortanksgenomsmältning, hur smältan rinner ut ur reaktortanken och händelseförloppet i reaktorinneslutningen.Målen i den andra delen är att: (a) erhålla en representativ databas över koden MELCOR:s analysresultat för egenskaperna hos härdrester i nedre plenum under olika händelseförlopp, och (b) utveckla en beräkningseffektiv surrogatsmodell som kan användas i omfattande osäkerhetsanalyser för att förutsäga partikelbäddsegenskaper.MELCOR, kopplad till en genetisk algoritm med slumpmässigt urval användes för att generera en databas av analysresultat med tillämpning på smältans omfördelning i reaktortanken i en Nordisk BWR.Analysen av hur härden omfördelas visar att det finns två huvudgrupper av scenarier: med relativt liten (<20 ton) och stor (> 100 ton) total mängd omfördelade härdrester i nedre plenum. Dessa domäner är åtskilda av övergångsregioner, där små variationer i indata kan resultera i stora ändringar i den slutliga partikelmassan. Flergrupps artificiella neurala nätverk med klassificering av händelseförloppet har använts för utvecklingen av en surrogatmodell för att hantera problemet med kaotiska resultat av den fullständiga modellen, särskilt i övergångsregionen.

Place, publisher, year, edition, pages
Stockholm: Kungliga Tekniska högskolan, 2017. 86 p.
Series
TRITA-FYS, ISSN 0280-316X ; 2017:03
Keyword
Reactor system code, input calibration, code validation, surrogate model, two-phase circulation flow instability, in-vessel core relocation, genetic algorithm, artificial neural network, Reaktorsystemkod, indata kalibrering, kodvalidering, surrogatmodell, två-fas cirkulationsflöde, instabilitet, härdrelokering i reaktortanken, genetisk algoritm, artificiella neurala nätverk
National Category
Other Physics Topics
Research subject
Physics
Identifiers
urn:nbn:se:kth:diva-202957 (URN)978-91-7729-265-4 (ISBN)
Public defence
2017-02-17, FA32, Roslagstullsbacken 21, Huvudbyggnaden, våningsplan 3, AlbaNova, Stockholm, 13:00
Opponent
Supervisors
Note

QC 20170309

Available from: 2017-03-09 Created: 2017-03-08 Last updated: 2017-03-10Bibliographically approved

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