atw 2018-10

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atw Vol. 63 (2018) | Issue 10 ı October 532 OPERATION AND NEW BUILD Diagnosis & Prognosis Tool for Severe Accidents in European Nuclear Power Plants Juan C. de la Rosa Blul, Miodrag Stručić, Patricia Pla and Luca Ammirabile 1 Introduction and contents If an accident occurs, European Union (EU) Member States (MS) can count on their own emergency plans, an early warning system called ECURIE (European Community Urgent Radiological Information Exchange) [1] and the EU-wide network for prompt dissemination of radiological data EURDEP (EUropean Radioactivity Data Exchange Platform) [2]. Internationally, the International Atomic Energy Agency (IAEA) has developed the web portal USIE (Unified System for Information Exchange in Incidents and Emergencies) [3] for Contact Points of States Parties to the Convention on Early Notification of a Nuclear Accident [4] and the Convention on Assistance in Case of a Nuclear Accident or Radiological Emergency [5], as well as for IAEA Member States to exchange urgent information during nuclear and radiological incidents and emergencies. In an event of nuclear crisis caused by an accident at a nuclear power plant, the Joint Research Centre (JRC) of the European Commission (EC) will collect and assess in a coherent way all available information which is of interest for the EU and will provide on request expert advice and assistance to the EU Institutions and to EU MS through recognized channels. In line with this goal, one of the key issues in the field of nuclear emergency response is to establish the areas where different mitigating actions to reduce the radiological impact on the inhabitants are to be applied. To achieve this fundamental objective, an accurate prediction of the radiological source term released from the nuclear installation becomes fundamental. These source terms highly depend on the specifics of the installation and the accident sequence. This article presents the development of a plant-specific, accidentspecific mechanistic tool to predict the released source term characterisation and to diagnose the different | | Fig. 1. Uncertainty sources applied to the ERDP tool. quantities of interest and main events along the nuclear accident sequence. The severe accident integral system code to perform the simulations is MAAP 5.04 [6]. 2 Introduction to the uncertainty sources In the field of diagnosis and prediction through the use of modelling tools, three types of uncertainty sources are identified: aleatory, epistemic and user effects. To make an accurate estimate of the outcome of a nuclear severe accident, these three sources of uncertainty must be duly treated. 2.1 Aleatory uncertainty Certain variables are subject to stochastic variation, whether because of their random nature or because they constitute process outcomes depending on many different inputs [7]. The accident sequence evolution, defined as the initial event followed by a set of limited working systems together with their necessary human actions, can be considered as a fundamental aleatory uncertainty source. This source of uncertainty cannot be reduced in the analysis. The most suitable approach to quantify the level of uncertainty is by calculating the relative frequencies of occurrence associated to each of the possible relevant sequences. Whenever the necessary information need to be applied to the underlying probabilistic analysis to compute this sorted list of frequencies is not available, the level of uncertainty will not be able to be further reduced. 2.2 Epistemic uncertainty Due to a limited knowledge in the analysed phenomena, an epistemic related type of uncertainty is introduced into the models [8]. Contrary to the aleatory uncertainty, the sources of epistemic uncertainty can be further reduced once the knowledge gaps are bridged, e.g. by new experiments improving the corresponding state of the art. In this respect, several sound international efforts to orient research in the area of severe accidents are currently being undertaken. In order to implement and quantify the epistemic uncertainty in modelling accident sequences, several robust and widely used methodologies have been put in place [9, 10]. However, all the existing methodologies fall short when applied to the field of severe accidents mainly due to a non-comprehensive experimental database against which validating the models. A more limited though reasonable and useful approach to cope with this type of uncertainties consist of identifying the sources of uncertainties, whether model-type and param etertype, assigning probabilistic distribution functions and perform the uncertainty propagation through Operation and New Build Diagnosis & Prognosis Tool for Severe Accidents in European Nuclear Power Plants ı Juan C. de la Rosa Blul, Miodrag Stručić, Patricia Pla and Luca Ammirabile
atw Vol. 63 (2018) | Issue 10 ı October simulation calculations [11], even though the lack of a complete model validation will prevent a fully accurate uncertainty quantification application. 2.3 User effects Many relevant documents are found in the open literature addressing the influence that users in charge of performing accident calculations have on the results [11, 12]. In a nutshell, the user effects constitute the dependency of the results on the responsible person upon the decisions taken on the selection of the models, building up of the model of the plant, i.e. nodalization, initial and boundary conditions, and interpretation of the results. The most recommended treatment of this kind of uncertainty consists of following certain rules usually known as code user best practices [13] by which a high quality input deck of the plant is implemented and the appropriate selections according to the simulated scenario are made. 3 Treatment of the uncertainty sources The Figure 1 depicts in a schematic way the fundamental sources of uncertainty as described above and adapted to the ERDP (Emergency Response Diagnosis and Prognosis) tool. 3.1 U1 – Uncertainty of the model of the plant This is the added uncertainty stemming exclusively from the fact that the model of the plant does not fully reflect the real plant configuration but only approximately. The so-called surrogate model of the plant is made up by modifying a comprehensive reference model of the same design with the available plant-specific information found in the open literature. The main sources of information are the followings: • IAEA Power Reactor Information System [14]; • Main Characteristics of Nuclear Power Plants in the European Union and Candidate Countries [15]; • Nuclear Engineering International Handbook (2013 and 2016) [16, 17]; • National Stress Tests reports [18]; • Literature papers. The reference models of the plant are those available with the MAAP 5.04 code (see Table 1), [6]. It is important to note that U1 is the only uncertainty source among the Pressurized Water Reactor Types Westinghouse Large-Dry Containment Westinghouse Ice-Condenser Containment Babcock & Wilcox Large-Dry Containment VVER440 (213 and 230) VVER1000 | | Tab. 1. Available Reference NPP Designs. three brought up specifically by this ERDP tool, whereas the other two sources are also present even in case of accounting for a comprehensive set of as-built information of the plant. 3.2 U2 – Uncertainty of the accident sequence In the absence of plant-specific PRA models, the entire spectrum of relevant accident sequences can be covered by considering an equally comprehensive set of conditions affecting the severe accident evolution from the time of core damage until the onset of radiological consequences. If relevant for the rest of the accident evolution, these conditions should be taken as the grouping criteria to classify the accident sequences featuring core damage into Plant Damage States (PDS) [19]. A PDS is a category of scenario characterised by representing a specific evolution of the damaged core, Reactor Cooling System (RCS) and containment. Within Level 2 PRA, the high-number of accident sequences leading to core damage are classified into different PDSs through the Logic Tree, which is just a set of criteria standing for those aspects of the accident scenario relevant for its subsequent evolution. On the other hand, an accident sequence can be considered according to the following relationship: Accident Sequence = IE + BCs + Mitigating Systems • The Initiating Event (IE) can be a Loss Of Coolant Accident (LOCA), Station Black-Out (SBO), loss of Main Feed Water (MFW), Steam Generator Tube Rupture (SGTR), reactivity insertion, Main Steam Line Break (MSLB), Interfacing System Loss Of Coolant Accident (ISLOCA), etc. • The Boundary Conditions (BCs) here deal with the imposed status of the Defence in Depth (DiD) barriers, e.g. containment isolation system, RCS break, etc. Boiling Water Reactor Types MARK I BWR/4 MARK II BWR/5 MARK III BWR/6 Oskarshamn I, II, III / Forsmark III Ringhals I Forsmark I/II, TVO I/II • The available systems to cope with the severe accident can be standard by design or back-fitted. The system will impact the accident evolution through the availability, characterisation and timing. In order to identify the IE and the boundary conditions, we can look at the standard PDS grouping criteria [19]. When removing the criteria related to mitigating systems availability, the following three remain: 1) RCS pressure before core damage; 2) Reactor Pressure Vessel (RPV) integrity; 3) Containment bypass. Therefore, and except for the different combinations of available mitigating systems, the PDSs will be covered by considering the different states of the above three categories of conditions. As for the mitigating systems, their availability will be treated as an additional source of epistemic uncertainty upon the critical safety functions to be fulfilled summarized in the following three categories: 1) Core cooling 2) Containment cooling 3) Mitigation of Radioactive releases 3.3 U3 – Uncertainty of the severe accident phenomena Two different categories of inputs are here considered: 1) Epistemic uncertainty, 1.1) Stemming from a lack of knowledge. In this case, as discussed above, a complete set of relevant uncertainty parameters, models and key driving phenomena should be identified and quantified according to the state of the art; 1.2) Stemming from the lack of relevant plant-specific as-built information impacting on the sequence evolution. In this case, and whenever applicable, conservative values should be implemented. As an instance, the reactor cavity chemical composition will significantly affect the OPERATION AND NEW BUILD 533 Diagnosis & Prognosis Tool for Severe Accidents in European Nuclear Power Plants Operation and New Build ı Juan C. de la Rosa Blul, Miodrag Stručić, Patricia Pla and Luca Ammirabile
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