TEKNOLOGIAN TUTKIMUSKESKUS VTT OY

TURMETTurvallisuusperustelun metodiikansystematisointi

KYT2018 Puoliväliseminaari 7.4.2017Suvi Karvonen

227.10.2017 2

TURMET – Systemizing Safety Case Methodology

§ Safety Case is the basis and documentation of the safety of thechosen repository design§ In practice Safety Case often evolves through several iterations

and in tandem with the design, instead of guiding it§ Many components of the Safety Case commonly aren’t derived in

a systematic manner when they perhaps could be§ The original idea of TURMET was to answer this question of how

to create a Safety Case “from scratch”§ Most focus was on using mathematical analysis to create a tool for

scenario analysis and selection

327.10.2017 3

TURMET: A Change in Scope

§ The funding for most KYT projects was significanly cut for thefirst two years of the programme, including TURMET: theoriginal scope of the project became impossible to achieve§ Feedback from the programme had consistently been that the

most interesting part of the project was our scenario analysismodel§ It was decided to focus on scenario analysing while the

oroginally planned systemic approach to safety casemethodology had to be cut§ As a result TURMET has been focusing on scenario analysis in

2016-2017

427.10.2017 4

TURMET – Systemizing Safety Case Methodology

§ Website: http://www.vtt.fi/sites/turmet§ 2015 – Literature reviews & concept model

§ Literature Review on Safety Case & Scenario Analysis§ Scenario Analysis and the Conceptual Model

§ 2016 – more focus on Scenario Analysis§ Development of the scenario model§ Publication to be submitted to Risk Analysis

§ 2017§ Finishing scenario model§ Expert judgement tools

§ 2018§ Analysis of scenario model results§ Safety Case methodology

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Literature Review on Safety Case

§ Topics§ Safety Case definition, requirements and purpose§ Examples of Safety Cases§ Practical experiences of Safety Case through interviews and

published reports§ Authors: Suvi Karvonen and Jutta Peura, VTT

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Literature review on Scenario Analysis

§ Scenario Analysis for theSafety Assessment of NuclearWaste Repositories§ Purposes and structure of the

review:§ Key characteristics of

Scenario Analysis§ Challenges in Scenario

Analysis§ Publication submitted to Risk

Analysis, under review

727.10.2017 7

A model for Scenario Analysis

§ A model for Scenario Analysis is under development, in order toaddress the following challenges:§ Build a System Model of the disposal system, in which to display

all FEPs and their interactions§ Ensure comprehensiveness - due to computation times,

laboratory costs etc. it is not practicable to analyze allscenarios - which scenarios to analyze for still coming toconclusions on safety§ Characterize epistemic uncertainties – e.g. giving Kd a

range or distribution instead of a single value in uncertaincases

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System Model

§ An Influence diagram displaysall FEPs and their interactions

§ A Bayesian network is themathematical model embeddedinto the Influence diagram

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Bayesian network: FEPs

§ FEPs are modeled as stochastic variables assuming discretestates§ Safety is assessed at the radiological consequence at the end of the

Bayesian network (e.g. Radionuclide discharge)

FEP u.m. StatesIce sheet km 0 0.5 2

Saline water upconing - None Medium Intense

Fracture displacement cm 0 5 300

Corrodants concentration g/l 0 35 70

Near-field flow l/y 0 100 1,000

Buffer flow l/y 0 100 1,000

Canister breach mm 0 1 950

Radionuclide discharge - Negligible Below limit Above limit

FEP u.m. StatesIce sheet km 0 0.5 2

Saline water upconing - None Medium Intense

Fracture displacement cm 0 5 300

Corrodants concentration g/l 0 35 70

Near-field flow l/y 0 100 1,000

Buffer flow l/y 0 100 1,000

Canister breach mm 0 1 950

Radionuclide discharge - Negligible Below limit Above limit

Radiologicalconsequence

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§ Interactions are modeled as conditional probabilities between aFEP and its predecessors§ Conditional probability tables (CPTs)

Fracturedisplacement

[cm]

Near-fieldflow[l/y]

Chlorideconcentration

[mm]

Buffer flow [l/y]

0 100 1,000

0 0 0 0.64 0.32 0.045 0 0 0.25 0.50 0.25

300 0 0 0.04 0.32 0.64. . . . . . . . . . . . . . . . . .0 0 35 0.93 0.06 05 0 35 0.66 0.31 0.03

. . . . . . . . . . . . . . . . . .5 1,000 70 0.25 0.50 0.25

300 1,000 70 0.04 0.32 0.64

Bayesian network: interactions

( )lg

Bshearyl

B ClqcmdqP 35,0,5100 ====

Sem

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ated

data

1127.10.2017 11

Bayesian network: scenarios

§ A scenario is a combination of FEP states

§ Specifically, a combination of states of allFEPs but the radiological consequence

§ Each scenario results in a differentprobability distribution over the states of theradiological consequence

§ The probability of large radiologicalconsequences can be calculated byaccounting for all the scenarios

§ Safety can be assessed by comparing thisprobability against a maximum acceptablelevel

( ) ( )[ ] ( )[ ]å ÕÎ Î

-- ×=Ss

s ssAk

rrlimitabove

kks

rlimitabove

vcvcpk

E.g. the probability ofunacceptable radionuclide

discharge is 49%

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Challenges in modeling the interactions

§ How to obtain the conditional probabilities, that is, how to fill in theconditional probability tables?§ Historical data, but also:

Experts’ beliefs+ Can be elicited in reasonable

times− Involve epistemic uncertainties

Radionuclide-transport code(model verification still ongoing)

Simulations+ Provide more precise predictions (initial

assumption: determinisitc)− Are resource-intensive (time, costs)

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Experts’ beliefs

§ For each FEP, multiple experts (e.g. three)§ Each expert provides his/her conditional probability table about the

FEP at hand§ For aggregating the beliefs, weight zik can be assigned to expert k at

FEP i§ Values of the weights should be fixed such that the estimated

probability of large consequences is the most conservative§ This is done through multilinear optimization (see next slide)

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Experts’ beliefs

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Simulations

§ Each simulation provides a deterministic prediction§ Before any simulation is run, the conditional probability table is empty and the

probability of unacceptable radionuclide discharges is interval-valued§ Simulations are resource-intensive, calling for prioritization of simulations§ This is done through an iterative algorithm, selecting the next simulation

until a conclusive probability interval is obtained

Near-fieldflow [l/y]

Bufferflow [l/y]

Canisterbreach [mm]

Radionuclide discharge [-]

Negligible Belowlimit

Abovelimit

0 0 0 1 0 010 0 0 1 0 0

1,000 0 0 1 0 00 10 0 1 0 0

. . . . . . . . . . . . . . . . . .1,000 10 950 0 0 1

0 1,000 950 0 1 010 1,000 950 0 0 1

1,000 1,000 950 0 0 1

Sim

ulat

ions

Near-fieldflow [l/y]

Bufferflow [l/y]

Canisterbreach [mm]

Radionuclide discharge [-]

Negligible Belowlimit

Abovelimit

0 0 0 - - -10 0 0 - - -

1,000 0 0 - - -0 10 0 - - -

. . . . . . . . . . . . . . . . . .1,000 10 950 - - -

0 1,000 950 - - -10 1,000 950 - - -

1,000 1,000 950 - - -

0 1 2 3 40

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Threshold

0 1 2 3 40

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Threshold

Non-conclusive and conclusive interval-valuedprobabilities of unacceptable radionuclide discharges

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§ The bounds of the interval are conservative estimates

Near-fieldflow [l/y]

Bufferflow [l/y]

Canisterbreach [mm]

Radionuclide discharge [-]

Negligible Belowlimit

Abovelimit

0 0 1 - - -10 0 1 - - -

1,000 0 1 - - -0 10 1 - - -

10 10 1 - - -1,000 10 1 - - -

0 1,000 1 - - -10 1,000 1 - - -

1,000 1,000 1 - - -0 0 950 - - -

10 0 950 - - -1,000 0 950 - - -

0 10 950 - - -10 10 950 - - -

1,000 10 950 - - -0 1,000 950 - - -

10 1,000 950 - - -1,000 1,000 950 - - -

Simulations

0 1 2 3 40

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Iterations (number of simulations run)

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Iterations (number of simulations run)

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Iterations (number of simulations run)

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Interval-valued probability of unacceptableradionuclide discharges

1727.10.2017 17

Next steps 2017-2018 for TURMET

§ Scenario Modeling (2017-)§ More realistic numbers for physical interactions and parameters§ Implementation of time-dependence§ Post-processing to identify the causes of unacceptable radionuclide

releases§ Visualization and analysis of results

§ Safety Case Methodology (2018-)§ Implementing the scenario analysis results into safety case methodology

framework

1827.10.2017 18

Contact Information

§ Suvi Karvonen; suvi.karvonen@vtt.fi§ Edoardo Tosoni; edoardo.tosoni@aalto.fi§ Prof. Ahti Salo; ahti.salo@aalto.fi

http://www.vtt.fi/sites/turmet

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