overview of source term modeling research in sweden and of ... · iaea technical meeting on source...

IAEA Technical Meeting on Source Term

Evaluation for Severe Accidents

October 21-23, 2013, Vienna, Austria

Overview of source term modeling research in

Sweden and of computerized tool RASTEP for

fast, online accident diagnosis and source term

prediction

Wiktor Frid

Swedish Radiation Safety Authority

Michael Knochenhauer

Lloyd’s Register Consulting

Outline

• Objectives

• BWR ex-vessel issues and concerns

• Highlights of research on development of Risk

Oriented Accident Analysis Methodology

(ROAAM) for Nordic BWRs

• Computerized tool RASTEP for rapid accident

diagnostic and source term prediction

W. Frid/M. Knochenhauer IAEA TM 2013 2

SSM´s research objectives

• To reduce uncertainties in accident progression, in

particular with regard to ex-vessel phenomena and

risk for early containment failure

• To improve knowledge about iodine chemistry in

containment, in particular with respect to organic

iodine

• To support improvements of accident management

strategies

• To improve effectiveness and reliability of source

term prediction in emergency situations


Outline

• Objectives








Nordic BWR Severe Accident Mitigation

• Severe accident mitigation strategy in Nordic BWRs:– Flooding of lower drywell with

water from wetwell

– Core melt is expected to fragment, quench and form a coolable debris bed in a deep (7-12m) pool of water

• Melt arrest and accident termination provided by ex-vessel debris bed coolability

BWR Severe Accident Mitigation: Concerns

• What is the likelihood of:

– Formation of no-coolable debris

bed?

– Containment damage by steam

explosion?

– Early release due to

containment failure

• Melt release from the vessel

determine the answers to both

questions.

Outline

• Objectives








Why ROAAM?

• The issues of ex-vessel coolability and steam explosion in

Nordic BWRs are intractable for only probabilistic or

only deterministic analysis approach.

• Mainly because there are complex interactions and

feedbacks between:

– Scenarios of accident progression, and

– Deterministic phenomenological processes.

• Risk Oriented Accident Analysis Methodology (ROAAM) is considered as an adequate tool for addressing

such issues

ROAAM Project Goal• To develop risk oriented accident analysis

frameworks for quantifying conditional threats to containment integrity for a Nordic type BWR reference plant design.

– However, it is possible that ROAAM application to Risk Assessment might be insufficient to resolve the issues

• given complexity of interplay between scenarios and phenomenology in present design and severe accident management strategy.

– In this case, ROAAM can help to devise effective Risk Management strategy (changes in the design and operational procedures) to resolve the issues in a robust and final way.

ROAAM Project Structure

• RES: Risk Evaluation and Synthesis

• MEM: Melt Ejection Mode

• DECO: Debris Coolability Map

• SEIM: Steam Explosion Impact Map

MEM RES

DECO

SEIM

RES: ROAAM Approach to Nordic BWRs

1. Identification of the key physics. – Define an optimal approach to compare loads vs. fragilities for:

• Vessel-melt interaction and Melt ejection mode,

• Debris bed formation and coolability,

• Steam explosion impact on containment functions.

2. Definition of probabilistic frameworks.– Models for quantifying loads, fragilities, and probabilities of failure in MEM,

DECO and SEIM,

– Define involved uncertainties and how to bound them in the quantification.

3. Quantification of loads.– Quantify loads with the intent of enveloping uncertainties in MEM, DECO

and SEIM.

– Evidences of models verification/validation status.

4. Quantification of fragilities.– Failure criteria to provide a solid conservative quantification of failure

incipience and gross failure.

5. Quantification of failure probabilities.– Transpose loads against fragilities.

ROAAM Frameworks (Topmost Level)

• Top level of ROAAM frameworks for Nordic BWRs.– PDF = probability density function.

– CR = Causal relationship (deterministic model).

• Approach takes into account the influence of both– Deterministic phenomena (represented in CRs)

– Accident scenarios (represented in PDFs)

Load Capacity

Risk

Debris Bed Formation and Coolability

In-vessel scenario

Vessel failure mode and

timing

Severe accident

management

(SAM)

Melt mass M

Decay heat power W

Melt jet diameter

Melt superheat

Water pool depth

Pool subcooling

Debris bed

formation

Debris bed

coolability

“Load”

“Capacity”

Probability of dryout

(Capacity < Load)

Debris bed

coolability map

Vessel Failure ModesTwo Main Groups of Failure Modes

1. Vessel Wall Failure due to accelerated creep

2. Vessel Penetrations Failure

i. IGT Failure

ii. CRGT Failure

iii. Pumps

Failure modes are design and scenario specific:

� Corium melt properties� Debris bed heights� SAM strategy implemented

(e.g. CRGTs cooling or late recovery of ECCS)

Outline

• Objectives








Why was the project RASTEP

initiated?• Early source term prediction in connection with severe accidents is

crucial– Utilities predict source terms, and provide predictions to authorities

– SSM, the Swedish Radiation Safety Authority has an important role after a severe accident, involving both communication and technical aspects

– SSM needs in-house source term prediction capability

• Plant PSA:s in Sweden are detailed, full scope and updated on annual basis– Increasingly used for risk informed applications

• Possibility to – Make use of the detailed PSA information for source term prediction

– Build on results from EU project STERPS (Source Term Indicator Based on Plant Status)

• Lead to development of RASTEP (Rapid Source Term Prediction)

Basic features of RASTEP

• RASTEP is to be used in fast, online diagnosis of an event or an accident.

• Shall provide the SSM emergency preparedness organisation with an independent

view of the accident progression and possible off-site consequences.

• Model starting point:

– Scram and failure of “first line of defence” (systems expected to function in case of a

normal disturbance)

• Model end point / results

– Release path & source term & probability

• Compatibility with plant PSA model – both level 1 and 2

• Interface with off-site consequence analysis tools, e.g., LENA or ARGOS

– Simple user interface

– Plant specific models for all Swedish NPP:s.

NB: RASTEP software is in status ”pre-alpha version” – examples and user interface illustrations

are preliminary.

RASTEP Architecture

• Bayesian Belief Network (reflects plant status, PSA)

• Source term module (Spreadsheet, MAAP)

– A set of precalculatedsource terms fitted to BBN end states

• Interfaces with

– User (input and results)

– BBN software (Netica)

– PSA information (manual or automatic)

– Plant information (manual or on-line)

– Offsite consequence software (LENA or ARGOS)

Measurement

(pressure,

temperature...)

Measurement

(pressure,

temperature...)

Observables

Conclusion from

multiple observables

Deduction

Status of core / RV /

containment etc.

(priority given to

observables)

Continued

development

Conclusion

Basic assumption

(default in case of no

observables)

Basic assumption

(default in case of no

observables)

PSA based

information

Measurement

(pressure,

temperature...)

... Several steps

Source term

General structure of the BBN model

Sample from Oskarshamn 3 BBN –

Residual Heat Removal

Sub-networks – Reflects accident

progression• Initiating Event

• Core Cooling

• Residual Heat Removal

• Fuel Status

• Reactor Pressure Vessel

(RPV) Status

• Containment Status

• Reactor Building Status

• Turbine Building Status

• Source Term

Development of plant BBN

model

• Definition of the physical source term volumes

to be considered (e.g. Fuel, RCPB)

• Fission product transport and release routes

• Mapping of severe accident management

systems and actions

• Key plant systems

• Observable plant state parameters

• Physical phenomena

Fission product transport routes /

release paths

Severe Accident Mitigation

Systems – BWR (O3)

Current status

• RASTEP model for Oskarshamn 3 finalized.

• First V&V round finished ; verification includes:

– The correct source term is selected (based on MAAP calculations)

– Probabilities of intermediary and end states are in accordance with

PSA

– The representation of Low Probability/High Consequence events is

credible

• Full-scope models under finalisation for Oskarshamn 2 (ASEA-

Atom BWR) and Ringhals 3 (Westinghouse 3-loop BWR).

OECD/NEA WGAMA “FASTRUN”

• RASTEP is one of the analysis tools participating in

OECD/NEA WGAMA/WPNEM Task Group International benchmarking project on fast-running software tools used to

model fission product releases during accidents at nuclear power plants

• RASTEP will be applied to scenarios related to three reactor designs:– Case 1 Oskarshamn 3 (ASEA-Atom BWR)

– Case 2 Peach Bottom (GE BWR, Mark I)

– Case 3 Surry (W 3-loop PWR)

• The following shows the results of the simulation of the scenario defined for Oskarshamn 3

Scenario for Case 1 /

Oskarshamn 3 BWR0 h • Scram due to transient TSxD (turbine trip with dump blocking)

• Containment isolation.

• AFWS starts and maintains level in RPV. Reactor pressure

remains at 7 MPa

• Complete loss of RHR followed by heating up of condensation

pool

2,4 h • Primary system depressurization due to low water level in RPV,

ECCS fails to start

8,8 h • AFWS stops due to temperature in the suppression pool

reaching 120 C

10, 8 h • Flooding of lower drywell (initiated by very low water level in

RPV)

13,8 h • Filtered containment venting automatically initiated (drywell

pressure 0,6 MPa)

19 h • Reactor vessel melt-through

Other

assumptions

• Containment inerted

• No operator actions credited

1 h 6 h 12 h 24 h

RHR No = = =

AFWS Yes = No =

Depressurisation Not known Yes = =

ECCS Not known No = =

Water in RPV Above top Decreasing Uncovered N/A

Pressure in RPV Steady 7MPa = = Low

Containment spray No = = =

LDW water filled Not known = Yes =

Containment hydrogen level No = = >2%

Core temperature OK OK High N/A

Filtered venting system No = Yes =

Details for Case 1 / Oskarshamn

3 BWR• Timeline – how does the source term prediction develop over time

1 h 6 h 12 h 24 h

RHR No = = =

AFWS Yes = No =










Time 1 h

After 1 hour

RHR failed

AFWS in operation

ECCS status not known

Water above top of core

99% probability of no release

1% probability of source term ”Transient, filtered venting,

no spray”

<< 1% probability of unfiltered release

1 h 6 h 12 h 24 h

RHR No = = =

AFWS Yes = No =












Time 6 h

After 6 hours

RHR failed

AFWS in operation

ECCS failed

Water below top of core


95% probability of source term ”Transient, filtered

venting, no spray”

3% probability of unfiltered release

1 h 6 h 12 h 24 h

RHR No = = =

AFWS Yes = No =












Time 12 h

After 12 hours

RHR failed

AFWS failed

ECCS failed

Core uncovered

Venting initiated


98% probability of source term ”Transient, filtered

venting, no spray”

2% probability of unfiltered release

Conclusions

• RASTEP has a potential for use in support of:

– Rapid source term prediction update based on plant data

– What-if-analyses in connection with severe accident sequences

– Training

• QA crucial both initially and for model maintenance

• Favourable cost-benefit as most of the information (basic) is already

available

• Wide functionality possible to implement

• WGAMA FASTRUN scenario analysis

– Early identification of the most probable release scenario

– Application to other reactor types believed to be achievable with a reasonable

conversion effort

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