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IDPSA APPROACH TO ASSESS THE POTENTIAL OF A THERMALLY INDUCED STEAM

GENERATOR TUBE RUPTURE

Martina Kloos, Joerg Peschke, GRS

PSA 2017, Pittsburgh, PA September 24-28, 2017

Introduction

M. Kloos: IDPSA approach to assess the potential of a thermally induced steam generator tube rupture

Primary analysis aim Detailed investigation of the behavior of steam generator (SG)

tubes during a high pressure core melt event.

Particular analysis aims Quantification of the influence of aleatory uncertainties and

estimation of the conditional likelihoods for • a thermally induced SG tube rupture • a break of the main coolant pipe (hot leg) • a break of the pressurizer surge line

Quantification of the epistemic uncertainties of the likelihoods.

Comparison of the occurrence times of the damage states.

Identification of the prime implicants for the damage states.

Introduction


Analysis is performed with the tool MCDET. MCDET integrates advanced techniques for Monte Carlo (MC) and Dynamic Event

Tree (DET) simulation to handle the influence of uncertainties. These techniques are applied in combination with

• a computer code for system dynamics/accident simulation − ATHLET-CD (Analysis of THermal-hydraulics of LEaks and Transients - Core Degr.)

• the Crew Module of MCDET for human action sequence simulation Post-processing modules of MCDET provide well-founded probabilistic

assessments combined with epistemic uncertainty quantification.

Scenario and aleatory uncertainties


High pressure scenario is initiated by a SBO. Batteries provide DC power supply to all battery supported functions.

Secondary side: Since on-site power plus D1- and D2-nets are not available, EOP ‘Sec. side

bleed and feed’ must be executed by the crew. Aleatory uncertainty: Performance of human actions • Manual opening of main steam relief valves (SRVs) is performed. • Feed water injection is not performed.

− Focus on sequences with high primary-to-secondary side differential pressure and dry SGs.

As long as the SRVs are not manually opened, they are demanded to open and to close for partial blow-down of the SGs. • Valves operate as intended.

− Focus on sequences with early elevated primary-to-secondary side differential pressure.

Scenario and aleatory uncertainties


Primary side: Due to volume expansion of the coolant, the pressurizer level increases and the

pressurizer valves are demanded to open and to close for pressure limitation. • Aleatory uncertainty: Performance of the three valves PRV, SV1 and SV2

EOP ‘Prim. side bleed & feed’ must be executed, when the prim. side temperature and the differential pressure containment / reactor building exceed specific levels. • Prim. side depressurization is not initiated. • Focus on sequences with high primary side pressure and high temperature

over a longer time period.

Accumulators can inject their coolant inventory, only if all pressurizer valves stuck open during pressure limitation. • Aleatory uncertainty: Performance of source and additional isolation valves of

the accumulators

Additional aleatory uncertainty: • Degree of degradation of the SG tubes

Consideration of aleatory uncertainties


DET simulation is applied to handle the aleatory uncertainties of most discrete parameters.

Result of each MC simulation run is a DET constructed with the values sampled for the aleatory variables handled by MC simulation

Special combination of MC & DET simulation:

MC simulation applied to handle the aleatory uncertainties of all continuous and selected discrete parameters.

Aleatory uncertainty of the performance of human actions for SGD

- Model -


Crew Module of MCDET applied • to implement a time-dependent model of human actions • to simulate time-dependent sequences

Considered human tasks: • deactivation of the automatic functions of the reactor protection system • preparation of the electricity supply of the bleed bus bar • opening of the main steam relief valves

Aleatory uncertainties • outcome (success/failure?) of simple human actions for accomplishing the tasks

− failure probabilities from ASEP method • execution times of simple human actions

− uniform distributions from expert judgment


- Model -


Aleatory uncertainties were considered by the combination of MC & DET simulation realized by the tandem MCDET/Crew Module.

Distribution functions of the time periods needed to accomplish the tasks RPS and BB.

Distribution function of the time period needed to accomplish task SGD.



- Influence on accident sequence simulation -

DET

1 time

2 time

. .

. .

. .

RPS & BB finished

criteria for SG depressurization

opening of steamrelief valves

Criteria for SG depressurization:- inlet temperature RPV > 310°C- pressurizer water level < 9.5 m- multiple activation of the pressurizer valves

criteria for SG depressurization

opening ofsteam relief valves

RPS & BB finished

Aleatory uncertainty of the performance of pressurizer valves


Model: Geometric distribution for the failure cycle of each pressurizer valve.

1-nsoscscsosc

th

)]p -)(1p -[(1)] p -(1 p +[p =

cycle) n at P(failure

⋅

psc = 5.83E-3 pso = 3.50E-3

Aleatory uncertainty of the performance of pressurizer valves



No CCF No early sc failure No medium sc failure Late so failure of PRV

SV

Late sc failure of PRV

SV

Medium sc failure of PRV

SV

Early sc-failure of PRV

SV(2 out of 3)-CCF No early sc-failure No medium sc-failure Late so-failure of valve

Late sc-failure of valve

Medium sc-failure of valve not involved in the CCF

Early sc-failure of valve not involved in the CCF(3 out of 3)-CCF

Time

Aleatory uncertainty of the degree of SG tube degradation


Model: Distribution derived from a Markov chain considering 5 degradation classes:

≤ 20 % 20 - 40 % 40 - 60 % 60 - 80 % 80 - 100 % Distribution is conditional on the number N of operation years of a tube after

its last test:

π(N) = (π1(N), π2(N), …, π5(N)) = π(0) ∙ PN

π(0) = (1, 0, 0, 0, 0) Aleatory uncertainty of N is quantified by Discrete Uniform distr.

Aleatory uncertainty of the degree of SG tube degradation



DET

1

2

...time

5000 s

Degr. 0 - 20 %: d201Prob. : 1. - p1

Degr. 20 - 70 %: d701Prob. : p1

Degr. 0 - 20 %: d202Prob. : 1. - p2

Deg. 20 - 70 %: d702Prob. : p2

Epistemic uncertainties


Independent failure-on-demand and CCF probabilities (Beta distr.) Human error probabilities (Beta distr.) Transition probabilities used in Markov chain model (Uniform distr.) 22 parameters of the applied computer code: No. Parameter Name Distribution Type

1 time delay RESA signal Uniform 2 correction factor decay heat Uniform 3 maximum value of steam pressure Polygonal Line 4 additional change of set value of maximum steam pressure Uniform 5 contraction value of steam discharge Polygonal Line 6 pressure loss in nozzle Polygonal Line 7 correction factor for opening cross section of pressurizer relief valve Uniform 8 correction factor for opening cross section of pressurizer safety valves Uniform 9 correction factor for opening cross section of main steam safety valves Uniform 10 heat conductivity of UO2 Uniform 11 heat conductivity of ZR Uniform 12 heat conductivity of ZRO2 Uniform 13 heat capacity of UO2 Uniform 14 heat capacity of ZR Uniform 15 total mass threshold to relocation Uniform 16 ceramic mass threshold to relocation Uniform 17 model selection for IDAM Discrete 18 relocation velocity of metallic melt Uniform 19 relocation velocity of ceramic melt Uniform 20 selection of zirconium oxidation model Discrete 21 melt temperature of UO2 Uniform 22 melt temperature of metallic zircaloy Uniform

Influence of epistemic uncertainties referring to model parameters


Pressure RPV Head [Pa]

Consideration of epistemic uncertainties


MC simulation: Each set of values sampled for the epistemic variables is used as input to the

method applied to handle the influence of aleatory uncertainties. Values of the epistemic variables are considered in the ‘outer loop’ whereas those

of the aleatory variables are considered in the ‘inner loop’ of the simulation runs. − m DETs for each out of n sets of epistemic variable values

Conclusions and outlook


Using MCDET for an IDPSA allows for considering many aspects of aleatory & epistemic uncertainty.

Special combination of MC & DET simulation is applied to consider the influence of uncertainties on • the sequence of human actions for sec. side depressurization

− MCDET/Crew Module • the accident sequence initiated by the SBO

− MCDET/ATHLET-CD First test runs are performed currently

• to check whether new MCDET version generates the input data of all simulation runs

• to check whether ATHLET-CD can handle the input data provided by MCDET • to get an overview on the computation time in order to fix the final number of

epistemic runs and DETs per epistemic run We expect that the results of the actual analysis will be available at the end of this

year and can be presented next year.

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