Gabrielli, F.

Sanchez-Espinosa, V. H.

Stakhanova, Anastasia

English

KITopen

KIT – The Research University in the Helmholtz AssociationINSTITUTE for NEUTRON PHYSICS and REACTOR TECHNOLOGY (INR)www.kit.eduWAME project. Development of a real-time system toimprove decision making in severe accident event in nuclearpower plants.Name: Stakhanova AnastasiaTechnical Advisors: Dr. Victor H. Sanchez-Espinosa, Dr.-Ing. Fabrizio GabrielliSupervisor: Prof. Robert Stieglitz(Work is made in cooperation with Framatome GmbH, Erlangen)Start: 01.04.2019   End: 31.03.2022KIT, INR2 9/6/2021Table of contents Reminder (based on slides from 2020):- WAME project. Main Goals- Connection to IAEA CRP I31033- Fast Source Term Calculation (FSTC) Tool U&S Analysis of ASTEC KONVOI simulations:- Uncertain input parameters- Results of MBLOCA scenario Conclusions and next stepsKIT, INR3 9/6/2021Goal of the project: improvement in real-time source term predictionto help technical support center in case of accident at NPP.- SA codes cannot be used in that case, because they are time-consuming- Probably the most common strategy in that case is prepare database with source termvalues and use Bayesian Belief Networks (BBN) to choose appropriate source term- Idea of WAME project is to modify prepared source terms base on real measurements- For that MOCABA algorithm (developed in Framatome) was suggestedConnection to IAEA CRP I31033: Database for MOCABA algorithm should cover asmuch as possible outcomes of simulated process depending on changes in inputdata.- Identify most important parameters for given accident scenario- Set the Probability Density Function (PDF) for each parameters- Sample uncertain input parameters inside given range- Run multiple SA code simulations- Collect simulation results and calculate simple statistics and correlation coefficientsSamplingMultiple simulationsCollect andfilter resultsU&S analysisFSTC tool:- Scheme below is briefly showing work-flow without details- Python language; Windows & Linux- Currently coupled with ASTEC code KIT, INR4 9/6/2021U&S Analysis of ASTEC KONVOI simulations- Forming the final list of uncertain input parameters is iterative process. Initial list, forexample, consists of 40 parameters all with uniform distribution and all from ASTECmodels. Then it was reduced and available information about PDFs was extracted fromliterature. Two parameters governing leakage rate and burn-up were added also. Resultswith these 16 parameters shown further.- For KONVOI simulations number of samples was usually set to 200 or 300- 300 samples run took ~3.5 days on 32 cores- For all simulations Latin Hypercube sampling method was used- Further results only for MBLOCA scenario will be shown (in project simulations forMBLOCA+SBO, SBLOCA and SBLOCA+SBO are planned)- ASTEC2.2b severe accident code is used for simulations- Set of main events happening during accident progression is chosen. For example, “startof fission product release”, “lower head vessel failure”, “first material slump in lowerplenum”, etc.- In general results between any pair of main event could be extracted and analyzed.KIT, INR5 9/6/2021Parameter Best estimated valueProbability density functionMeaningpar1 5.0 Normal STRU ELSA Correction factor for the ratio S/V of the fuel pellets due toroughnesspar2 0.03 Normal STRU ELSA Correction factor for the ratio S/V of the fuel pellets for the limitedsteam accesspar5 1.2E-5 Normal STRU ELSA Geometrical diameter of the grainpar5a 2.0E-6 Triangular Standard deviation of geometrical diameter of the grainpar14 2500.0 Normal VESSEL:INTE Threshold Temperature of the cladding Dislocation [K]par15 2300.0 Normal VESSEL:INTE Threshold Temperature of the oxide layer Dislocation [K]par16 250.0E-4 Normal VESSEL:INTE Threshold thickness of the oxide layer [mm]par31 3.5 Uniform SOPHAEROS:AEROSOLS Particle mean thermal conductivity (J/m/K)par32 840.0 Uniform SOPHAEROS:AEROSOLS Average specific heat (J/kg K) of the aerosolpar33 3000.0 Triangular SOPHAEROS:AEROSOLS particle mean density (kg/m3)par34 1.0E-08 Triangular SOPHAEROS:AEROSOLS:SIZE particle minimum geometrical radius (m)par35 2.0E-05 Triangular SOPHAEROS:AEROSOLS:SIZE particle maximum geometrical radius (m)par36 1.0 Triangular SOPHAEROS:AEROSOLS:SHAPE Shape factor relative to particlecoagulationpar37 1.0 Beta SOPHAEROS:AEROSOLS:SHAPE Shape factor relative to Stokes velocitypar41 1.0 Uniform Coefficient for the leakage rateparBU 164.0 Uniform Effective full power daysUncertain input parametersKIT, INR6 9/6/2021Correlations between input parametersInitially suggested correlations Correlations after applying Iman-Conover method[Please note, that initial correlation matrix could be not suitable for Choletsky decomposition, which is one ofthe steps of Iman-Conover method, and some additional methods should be applied first to the correlationmatrix to make it suitable for decomposition – for example, iterative spectral algorithm, alternatingprojections method or others]• Iman-Conover method: Ronald L. Iman & W. J. Conover (1982) A distribution-free approach to inducing rank correlation among input variables, Communications in Statistics - Simulation and Computation, 11:3, 311-334, DOI: 10.1080/03610918208812265• Implementation of Iman-Conover method: https://stats.stackexchange.com/questions/271686/iman-conover-implementation-for-correlated-randoms-in-python-with-numpy-scipy; https://www.howardrudd.net/how-tos/vba-monte-carlo-risk-analysis-spreadsheet-with-correlation-part-3a/; https://www.howardrudd.net/how-tos/vba-monte-carlo-risk-analysis-spreadsheet-with-correlation-part-3b/; https://www.howardrudd.net/how-tos/vba-monte-carlo-risk-analysis-spreadsheet-with-correlation-part-4/• Methods for correcting non-positive definite matrices: “Correcting Non Positive Definite Correlation Matrices”, Maree, S.C., 2012, Bachelor Thesis, TUDelftKIT, INR7 9/6/2021Results of U&S analysis for MBLOCA scenarioExample of how different could be time of main events occurrence:KIT, INR8 9/6/2021Results of U&S analysis for MBLOCA scenarioSimple statistics for Cs and Kr in containment and environment (as fraction of initial inventory)KIT, INR9 9/6/2021Results of U&S analysis for MBLOCA scenarioExamples of Pearson correlation coefficients for total amount of Kr in environment and Cs aerosols incontainment for two different time points (start of fission product release, ~ end of the process)KIT, INR10 9/6/2021Conclusions and next steps• U&S analysis was performed for ASTEC simulation results of MBLOCAscenario at KONVOI NPP (using generic ASTEC input file for KONVOI + somemodifications)• Analyzing changes of Pearson correlation coefficients in time could provide thebetter understanding – which parameters are important at different stages ofthe process.• Couple sets of simulations for MBLOCA + SBO were made with additionaluncertain input parameters, regarding iodine chemistry. And some of theseparameters will be added to current list of input parameters.• Simulations for SBLOCA and SBLOCA+SBO will be performed.• For QUENCH-08 simulations test with different number of samples and‘consistency’ test were made. The same could be done for KONVOI simulationsalso.

WAME project. Development of a real-time system to improve decision making in severe accident event in nuclear power plants

https://publikationen.bibliothek.kit.edu/1000139027/129769348

WAME project. Development of a real-time system to improve decision making in severe accident event in nuclear power plants

Abstract

Similar works

Full text

Available Versions

KITopen