An Overview of Westinghouse Realistic Large Break LOCA Evaluation Model

Since the 1988 amendment of the 10 CFR 50.46 rule in 1988, Westinghouse has been developing and applying realistic or best-estimate methods to perform LOCA safety analyses. A realistic analysis requires the execution of various realistic LOCA transient simulations where the effect of both model and input uncertainties are ranged and propagated throughout the transients. The outcome is typically a range of results with associated probabilities. The thermal/hydraulic code is the engine of the methodology but a procedure is developed to assess the code and determine its biases and uncertainties. In addition, inputs to the simulation are also affected by uncertainty and these uncertainties are incorporated into the process. Several approaches have been proposed and applied in the industry in the framework of best-estimate methods. Most of the implementations, including Westinghouse, follow the Code Scaling, Applicability and Uncertainty (CSAU) methodology. Westinghouse methodology is based on the use of the WCOBRA/TRAC thermal-hydraulic code. The paper starts with an overview of the regulations and its interpretation in the context of realistic analysis. The CSAU roadmap is reviewed in the context of its implementation in the Westinghouse evaluation model. An overview of the code (WCOBRA/TRAC) and methodology is provided. Finally, the recent evolution to nonparametric statistics in the current edition of the W methodology is discussed. Sample results of a typical large break LOCA analysis for a PWR are provided

CFENSS-SRS -menetelmä ydinpolttoaineen ja neutroniikan epävarmuusanalyysiin

Statistical uncertainty analysis has received much attention in the past decade. The impacts of both nuclear fuel and nuclear data uncertainties have been studied separately but not as a coupled system. The main research question of this Thesis was to confirm whether the uncertainties of fuel behaviour parameters and the nuclear data can be propagated separately. The secondary goals included comparing various statistical perturbation methods. The computations were performed close to the framework of the OECD/NEA UAM-LWR TMI-1 Pressurized Water Reactor benchmark, and more specifically, its pin cell exercise. A novel CFENSS-SRS (Coupled Fuel Behaviour and Neutronics Stochastic Sampling with Simple Random Sampling) method is presented for the combined uncertainty analysis of nuclear fuel behaviour and neutronics. The method applies the statistical uncertainty analysis to univariate nuclear fuel parameters and correlated neutron cross sections. Truncated normal distribution is used as the objective distribution for drawing samples based on the Principle of Maximum Entropy. Due to practical difficulties in employing the distribution, the distribution parameters are approximated for the nuclear fuel parameters while a normal distribution is applied for the neutron cross sections. Negative values of inherently positive parameters are re-sampled to avoid distorting the distribution to a great extent. The results support the hypothesis that the nuclear fuel parameters and the nuclear data can truly be treated as independent sources of uncertainty. Additionally, it was revealed that the details of the perturbation methodology, such as using relative covariance matrices rather than absolute ones, have a much smaller impact on the output uncertainty than neglecting some of the uncertainty data.Tilastollisen epävarmuus- ja herkkyysanalyysin käyttö on lisääntynyt ydinenergia-alan tutkimuksessa kuluneen vuosikymmenen aikana. Ydinpolttoaineen ja ydinvakiotiedon epävarmuuksien vaikutuksia on tutkittu erikseen, mutta niitä ei ole käsitelty yhtenä kokonaisuutena. Tämän lopputyön tavoitteena oli selvittää, pystytäänkö näitä kahta tärkeää epävarmuuden lähdettä tarkastelemaan erikseen määritettäessä lopputuloksen kokonaisepävarmuutta. Tutkimuksen kuluessa tavoitteena oli myös vertailla erilaisia tilastollisia menetelmiä epävarmuuden liittämiseksi laskuihin. Lopputyössä kehitettiin uusi CFENSS-SRS -menetelmä (engl. Coupled Fuel Behaviour and Neutronics Stochastic Sampling with Simple Random Sampling) yhdistettyyn epävarmuusanalyysiin. Työssä tutkittiin ydinvakiotiedon osalta vain mikroskooppisten vaikutusalojen epävarmuuksien vaikutusta. Maksimientropiaperiaatetta noudattaen tavoitteena oli käyttää katkaisua normaalijakaumaa epävarmojen muuttujien satunnaisarvonnassa. Käytännössä jakaumaa ei kuitenkaan ollut mahdollista soveltaa, sillä tällä hetkellä ei tunneta menetelmää sen parametrien laskemiseksi yleisessä tapauksessa. Ydinpolttoaineen kohdalla jakauman parametreja approksimoitiin normaalijakauman vastaavilla parametreilla, mutta vaikutusalojen osalta turvauduttiin kokonaan normaalijakaumaan. Positiiviseksi tunnettujen muuttujien satunnaisarvontaa toistettiin, kunnes normaalijakaumasta seuraavia negatiivisia arvoja ei ollut. Tulokset tukevat oletusta, että epävarmuuslähteet voidaan käsitellä toisistaan riippumattomina. Menetelmää kehitettäessä puolestaan huomattiin, että kaikkien epävarmuuksien huomiointi on tärkeämpää kuin satunnaisarvonnan yksityiskohtien parantaminen. Tulokset vastasivat kirjallisuudesta löytyviä tuloksia niiltä osin kuin vastaavaa tutkimusta on tehty

Integrated Methodology for Thermal-Hydraulics Uncertainty Analysis (IMTHUA)

This dissertation describes a new integrated uncertainty analysis methodology for "best estimate" thermal hydraulics (TH) codes such as RELAP5. The main thrust of the methodology is to utilize all available types of data and information in an effective way to identify important sources of uncertainty and to assess the magnitude of their impact on the uncertainty of the TH code output measures. The proposed methodology is fully quantitative and uses the Bayesian approach for quantifying the uncertainties in the predictions of TH codes. The methodology also uses the data and information for a more informed and evidence-based ranking and selection of TH phenomena through a modified PIRT method. The modification considers importance of various TH phenomena as well as their uncertainty importance. In identifying and assessing uncertainties, the proposed methodology treats the TH code as a white box, thus explicitly treating internal sub-model uncertainties, and propagation of such model uncertainties through the code structure as well as various input parameters. A The TH code output is further corrected through a Bayesian updating with available experimental data from integrated test facilities. It utilizes the data directly or indirectly related to the code output to account implicitly for missed/screened out sources of uncertainties. The proposed methodology uses an efficient Monte Carlo sampling technique for the propagation of uncertainty using modified Wilks sampling criteria. The methodology is demonstrated on the LOFT facility for 200% cold leg LBLOCA transient scenario