ANS PSA 2008 Topical Meeting - Challenges to PSA during the nuclear renaissance, Knoxville, Tennessee, September 6–11, 2008ANS PSA 2008 Topical Meeting - Challenges to PSA during the nuclear renaissance, Knoxville, Tennessee, September 6–11, 2008

Implementation of a Concept for a Risk-informed Diagnosis/Prognosis of Plant States through the RISARD System

Implementation of a Concept for a Risk-informed Diagnosis/Prognosis of Plant States through the RISARD System

Kwang-Il Ahn kiahn@kaeri.re.kr

Integrated Safety AssessmentKorea Atomic Energy Research Institute

Kwang-Il Ahn kiahn@kaeri.re.kr

Integrated Safety AssessmentKorea Atomic Energy Research Institute

KAERIKAERI 2

Motivation & Objectives

The Concept of RI-SAM

Computerized Tool SARD

Demonstrative Application

Concluding Remarks

⊙ Outline

KAERIKAERI 3

Key Ways for a Successful Implementation of SAM

Develop a proper SAM strategy by answering the questions:

How to reduce uncertainties in implementing the established SAM strategies? especially when available resources are limited.

Which essential safety function was lost at the time of the accident? That is, the root cause of the accident; Which safety systems are currently available for SAM?

What will be important future events? and what will be their evolution?

What are potential ‘success paths’ for SAM?

Utilize the computer-based methods & tools for supporting SAM:

capable of ① diagnosing the functional states of plant safety systems and ② predicting the future trends of key plant parameters as possible as quickly:

The diagnostic capability for plant states at the time of the accident is required to reduce the uncertainties in the current plant system state and to have a good basis for estimating future plant states.

Based on the current damage states of the plant, the prognostic capability for the possible evolution of the accident gives time enough to take an action for mitigating the consequence of the accident.

⊙ Motivation

KAERIKAERI 4

Our Approach for supporting SAM:

Utilize a PSA-based and SA phenomenological trends-based database (DB) (e.g., plant-, code-, accident sequence-specific SA analysis results) => SAR DB

Systematic use of SAM-related information

Quick & fast retrieval of the SAM-related information (Quick view)

Provide a computerized platform for a comprehensive use of SAR DB in a simple, fast and risk-informing way => RI-SARD

Proper information about the plant damage states at the time of the accident: the root cause of the accident (Diagnosis)

Insights on the possible evolution of the accident (critical parameters), based on the current damage states of the plant (Prognosis)

Develop the best strategy for supporting SAM (especially when available plant information is limited) => RI-SAM

Helpful in finding success paths for intended SAM actions

Helpful in providing appropriate actions to mitigate the accident

⊙ Objectives: RI-SAM Strategy & Tool

KAERIKAERI 5

⊙ RI-SAM: Diagnosis & Prognosis of Plant States

Monitor Plant Data & Signal: Identify the IE & CD statesDetermine the status/availability of systems needed to mitigate the IE

Signal Validation Process

ReSet Plant symptoms& accident time

DiagnosisSymptom-based

SARD modulePlant

symptoms

Plant damage states

Determine Plant symptoms

More Symptoms

?

Quick view Future trend of symptom parameters

Performance1. Key plant safety parameters for SAM2. Performance of

key SSCs

Decision for Implementation of the relevant SAM strategies

DetermineRelevant PDS (1)

AS screening (iteration loop)(2)

Auto switch to module for Prognosis of Future Plant Status

PrognosisScenario-based SARD module

Plant Conditions

Prioritize Frequency-based potential accident

sequences

ModificationLink to SA Simulatorfor Interactive Action

Success PathsCountermeas

ures

Note(1): A prescribed accident sequences by which uncertainty can be reduced in taking an action for SAM.Note(2): A process by which the prediction can be updated based upon successive data from plant.

(dynamic loop)(2)

Yes

No

KAERIKAERI 6

RISARD Key Functions & Modules

SARDB

• Code-specific or user-supplied SA accident sequence information:

characterized as system functionality & frequency (probabilistic information)

• Plant-, sequence-, code-specific predictions of key plant parameters:

phenomenological behavior (phenomenological information)

• Plant-, sequence-, code-specific predictions for key event histories:

based on system functionality & phenomena (evolution of plant states)

• Sensitivity results and a limited number of uncertainty analysis results:

based on available systems & different models (code uncertainty)

RI-SARD

Menu System

• Automatic allocation SAR data sets into the SARDB

• PDS scenario-based prognosis of future events

• Plant symptom-based diagnosis of plant damage states

- frequency-based ranking of possible accident sequences

- functional states of systems for the given accident sequence

• SAMG-linked module (decision flow chart, entry time for SAMG)

• SSC performance (CD, support plate, induced RCS/SG, RPV LH, cont. failure)

• Code-to-code comparison (MAAP-MELCOR predictions)

• Additional diagnosis for accident initiators (on-going)

⊙ A Computerized Tool: RI-SARD

KAERIKAERI 7

Data Set Spec. for SARD

- Plant/Code/User ID- Accident Sequence Inform(1)

- Sensitivity Information (Plant systems & Model parameters)- Severe Accident Code Analysis Results(2) (Code Responses)- Summary of Key Accident Progression Events (Code Result)- Accident Mitigation Options- Data Set & Databank Index- Commentary Parts

Accident Sequence Types- PSA code-specific plant damage event trees for graphical use- User-specified events sequence

Database Update &Modification

Data Search & Retrieval, Graphical Display

- Scenario-based Plant Responses & Behavior- Plant symptom-based Potential Accident Sequences- Status of Plant System & Containment Systems

SARDB: Databank(MS Access DB)

SAR-informed Decision-making

Data Allocation intoSARDB

- Level 1/2 PSA - Accident Analysis- SAM Information- The other SAR Inform

SAR Information

Formatted SAR Data Sets SARD System Operation

(1) Severe Accident Initiators: LOCA (Large, Medium, Small), Loss of Off-site Power (LOOP), Station Blackout (SBO), Loss of Feed Water (LOFW), Interfacing System LOCA, Steam Generate Tube Rupture (SGTR), Anticipated Transient w/o Scram (ATWS), Loss of AC Bus (125V, 4.16KV), Large Secondary Side Break, General Transient

(2) Number of Categorized MAAP Response Parameters (Total 883): RCS/SG/ESF Information (134); Behavior of Core and Fuel (152); Lower Plenum Debris Behavior (77); Lower Head Failure Information (85); Containment Information (196); Source Term Information (229); Hydrogen Generation (10)

⊙ SARD: Data Sets Operation

KAERIKAERI 8Allocation of Plant-, Code-specific SA Analysis Results into SARDB

SARDB

Typical Form of SARDB

Plant-specific PDS ET

⊙ SARD: SARDB Generation (1)

MELCORHISPLT

KAERI-IPLOT

MELCOR Run

SARDB Generation Module

SARDB:MELCOR/MAAP DB

Plant-specific

Accident Scenarios

Plot Data

SARD:Plant statePrognosis/Diagnosis

Parameter listfor comparison

MAAP Run

MELCORHISPLT

KAERI-IPLOT

MELCOR Run

SARDB Generation Module

SARDB:MELCOR/MAAP DB

Plant-specific

Accident Scenarios

Plot Data

SARD:Plant statePrognosis/Diagnosis

Parameter listfor comparison

MAAP Run

Identify the initiating event & the status and availability of systems and equipment needed to avoid or mitigate the severe accident

PDS sequence: plant damage state + frequency

SA phenomenological trends with timeDeveloping trends of key events during accident

KAERIKAERI 9

 

Key Role of the PDS ET-based Diagnosis & Prognosis Provide the status of plant and cont. systems at the time of core damage

All potential ASs for an IE can be shown at a glance with its graphical form

Occurrence probability (or frequency) be systematically derived from PSA

The graphical form of PDS ET can be very useful in specifying a particular AS during the data loading and information retrieval process

Probability can be utilized as a criterion for screening the risk-significant ASs

Frequency

OPR1000 IEs

Number of ASs for each IE

A frequency criterion for AS screening

1.0E-11/ry 1.0E-10/ry 1.0E-9/ry

All (16) Several hundreds - 95

LOOP 120 12 (99% of total ASs) -

LBLOCA 30 7 (99% of total ASs) -Risk-informed SA Analysis

⊙ SARD: SARDB Generation (2)

KAERIKAERI 10

I.Es (/ry) Sequences Contribution (%) to I.E

Functional States of Safety Systems(success state: ‘/sss’, failed state: ‘sss’)

LLOCA(1.05x10-6)

LLOCA-2 17.01 /SIT*/LPI*/HPR*/HPH*CSS

LLOCA-3 19.25 /SIT*/LPI*/HPR*HPH*/CSS

LLOCA-5 11.50 /SIT*/LPI*HPR*/LPR/*HPH*/CSS

LLOCA-8 5.9 /SIT*/LPI*HPR*LPR*HPH*CSS

LLOCA-9 45.36 /SIT*LPI*/HPI*/HPR*/CSS

LLOCA-15 0.46 /SIT*LPI*HPI*HPR*LPR*HPH*CSS

LLOCA-17 0.15 SIT*/LPI*/HPR*/CSS

Sub total 99.63  

MLOCA(6.34x10-7)

MLOCA-2 28.15 /HPI*/HPR*/HPH*CSR

MLOCA-3 31.87 /HPI*/HPR*HPH*/CSR

MLOCA-5 10.07 /HPI*HPR*/LPR*/CSR

MLOCA-8 9.0 /HPI*HPR*LPR*CSR

MLOCA-9 19.1 HPI*/LPI*/LPR*/CSI*/CSR

MLOCA-19 0.76 HPI*LPI*CSI

Sub total 98.95  

SLOCA(1.92x10-6)

SLOCA-11 0.05 /HPI*/AFW*/ADV*HPR*LPR*/CSS

SLOCA-12 57.79 /HPI*/AFW*/ADV*HPR*LPR*CSS

SLOCA-13 0.27 /HPI*/AFW*/ADV*HPR*/LPR*/CSS

SLOCA-21 0.08 /HPI*/AFW*ADV*/MSSV*/HPR*BDL*/CSS

SLOCA-26 0.14 /HPI*/AFW*ADV*/MSSV*HPR*LPR*CSS

SLOCA-45 0.19 /HPI*AFW*/LPR*BDE*/CSS

SLOCA-55 1.19 HPI*/AFW*ADV*/MSSV*/HPR*BD*/CSS

SLOCA-57 5.12 HPI*/DPI*LPI*CSI

SLOCA-59 32.6 HPI*DPI*/LPI*/CSI

Sub total 97.43  

Dominant accident initiators:Frequency-based screening of PDS sequences

LLOCA

LargeLOCA

SIT

SITsInjection

LPI

LPSISInjection

HPI

HPSISInjection

HPR

HPSISRecir-

culation

LPR

LPSISRecir.

HPH

HPSISHot

Cold LegRecir.

CSI

ContainmentInjection

Spray

CSR

Recir.CoolingusingCSS

RFSI

CavityFloodingSystemInjection

LLOCA

CDSQ5

CDSQ2

CDSQ3

CSR

CSR

CDSQ4

LPR

CSR

CSR

HPI

HPR

CSR

CSI

CSR

RFSI

CDSQ6

CSR

HPR

CSR

LPR

CSR

LPI

CSR

HPR

CSR

HPI

CSR

CSI

RFSI

SEQ#

STATE

STATE

FREQ

1 OK

2 43

3 27

4 28

5 27

6 28

7 29

8 30

9 27

10 28

11 29

12 30

13 29

14 30

15 31

16 32

17 27

18 28

19 27

20 28

21 29

22 30

23 27

24 28

25 29

26 30

27 29

28 30

29 31

30 32

LLOCA PDS ET

⊙ SARD: SARDB Generation (3)

KAERIKAERI 11

SAMPLE: Key Events Summary for LF115 (MAAP) Time Events Code Functional Status 0.000 157:T MAIN FW OFF 0.000 224:T MOTOR-DRIVEN AUX FEED WATER FORCED OFF 0.000 232:T CHARGING PUMPS FORCED OFF 17.836 31:T PZR SPRAYS ON 37.431 13:T REACTOR SCRAM 37.431 156:T MSIV CLOSED 42.578 153:T SEC SV(S) FIRST OPEN BROKEN S/G 42.578 163:T SEC SV(S) FIRST OPEN UNBROKEN S/G'S 867.556 161:T UNBKN S/G DRY 870.375 151:T BROKEN S/G DRY 1109.376 3:TH VALVE FIRST OPENED 1109.376 4:TH VALVE FIRST OPENED 1109.376 5:TH VALVE FIRST OPENED 1113.182 35:T VOID FRACTION IN PZR < 0.1 1728.464 4:T MAIN COOLANT PUMPS OFF 2584.444 691:T TRUE: CORE HAS UNCOVERED 4888.784 509:T TRUE: MAX. CORE TEMP EXCEEDS 2200. F 5084.019 690:T TRUE: MAXIMUM CORE TEMPERATURE HAS EXCEEDED 2499 K 5188.688 508:T TRUE: MAX. CORE EXIT TEMP EXCEEDS 1200. F 5962.269 2:T RELOCATION OF CORE MATERIALS TO LOWER HEAD STARTED 5987.935 103:T UPPER COMPT. SPRAYS ON 6778.318 3:T RV FAILED 6794.329 5:T HPI ON 6794.329 6:T LPI ON 6857.934 188:T ACCUMULATOR WATER DEPLETED 8142.179 1003:T TRUE: 1 TH COMPT BURNING IN PROGRESS 8142.179 1048:T TRUE: 4 TH COMPT BURNING IN PROGRESS 8142.179 1063:T TRUE: 5 TH COMPT BURNING IN PROGRESS 8142.492 1033:T TRUE: 3 TH COMPT BURNING IN PROGRESS 9897.961 5:F HPI OFF 9897.961 181:T RECIRC SYSTEM IN OPERATION ….

PDS sequence-

specific SA code

analysis

Parameters history:SA code parameter behavior with time

Events history: Plant system

status with time

⊙ SARD: SARDB Generation (5)

KAERIKAERI 12

Specification of the Target Scenario

AMP & Summary Data

Plant Data

SAR Data

ASQ data

SA Code data Code results

Specification of Sensitivity Information

Specification of Code Data (Multiple)

Specification of Code Data (Single)

Check of the Allocated Information

Specification of Databank Index

SARDBMS Access DB

⊙ SARD: SARDB Generation Module

KAERIKAERI 13

Display - Plant states - Base response - Sensitivity case - SAMG parameters - SSC performance - Events History

End of Searc

h

Scenario Base (1) Symptom Base (2)

PSA Information:IE & Target Sequence

Data Search: Plant-/Code-/AS

sequence-specific Responses

Set target Plant ID & Code ID

Plant Symptoms: - Code Parameters - Time windows

Prioritize Accident Scenarios (i = 1, n), in a risk-informing way

Target Sequence

More symptoms? AS Screening

Auto Switc

h

(1) Retrieval of the specified- accident sequence-based -based plant/code behavior (Accident Diagnosis)(2) Retrieval of plant symptoms-based -based potential accident sequences (Accident Prognosis)

⊙ SARD: Two-way information Retrieval

KAERIKAERI 14

⊙ SARD: Plant Symptom-based Diagnosis

Switch to the Scenario-based Module

User-specified plant symptoms

The most probable plant system stateList of potential

plant damage states

Progression of key events Future trend of

plant parameters

User-specified plant symptoms

The most probable plant system stateList of potential

plant damage states

Progression of key events Future trend of

plant parameters

Switch to the Scenario-based Module

PDS ET Events Functional Status

Set Plant & Code information

KAERIKAERI 15

⊙ SARD: PDS sequence-based Prognosis

User- specified accident conditions

Future history key events &plant parameters

User- specified code/plant parameters

User- specified accident conditions

Future history key events &plant parameters

User- specified code/plant parameters

Display of the Corresponding PDS ET PDS ET Events Functional StatusSet Plant & Code information

KAERIKAERI 16

⊙ Demo Application: Diagnosis of PDS sequences

1. Initial Plant Symptom (1) TWCR (temperature of water in core, K) [580-600] for Time Window (Sec.) [110-130]

Matched PDS Sequences Freq. (/ry) Functional States of Safety Systems

SBLOCA_S012SBLOCA_S059SBLOCA_S055LOOP_S053SBLOCA_S058SBLOCA_S013SBLOCA_S045SBLOCA_S026SBLOCA_S021SBLOCA_S011LOOP_S064SBLOCA_S070

1.109E-066.256E-072.281E-081.205E-081.150E-085.270E-093.590E-092.593E-091.598E-091.058E-094.141E-101.759E-12

SLOCA*/RT*/HPI*/AFW*/SR1*HPR*/DPR*LPR*CSR SLOCA*/RT*HPI*DPI*/LPI*/LPR*/CSI*/CSR SLOCA*/RT*HPI*/DPI*LPI*/CSI*/CSR LOOP*/RT*/AFW*SR1*/SR2*MSHR*BD*/LPI*/LPR*/CSI*/CSR SLOCA*/RT*HPI*/DPI*LPI*CSI*SLOCA*/RT*/HPI*/AFW*/SR1*HPR*DPR*/LPR*/CSR SLOCA*/RT*/HPI*AFW*BDE*/LPR*/CSR SLOCA*/RT*/HPI*/AFW*SR1*/SR2*HPR*LPR*CSR SLOCA*/RT*/HPI*/AFW*SR1*/SR2*/HPR*MSHR*BDL*/CSR SLOCA*/RT*/HPI*/AFW*/SR1*HPR*/DPR*LPR*/CSR LOOP*/RT*/AFW*SR1*/SR2*MSHR*BD*LPI*CSI*RFSI SLOCA*/RT*HPI*DPI*LPI*CSI*RFSI

2. Two Additional Plant Symptoms (2) PPS (pressure in primary system, MPa) [12.50-12.51] for Time Window (Sec.) [110-130] (3) TGUP (temperature of gas in upper plenum, K) [600-620] for Time Window (Sec.) [110-130]

The corresponding PDS Sequences

SBLOCA_S012, SBLOCA_S059, SBLOCA_S055, SBLOCA_S058, SBLOCA_S013, SBLOCA_S045, SBLOCA_S026, SBLOCA_S021, SBLOCA_S011, SBLOCA_S070

3. Two Additional Plant Symptoms (4) TWCR (temperature of water in core, K) [550-600] for Time Window (Sec.) [1950-2050] (5) TWCR (temperature of water in core, K) [435-445] for Time Window (Sec.) [2950-3050]

The corresponding PDS Sequences SBLOCA_S012, SBLOCA_S013, SBLOCA_S011

Target: OPR1000-/MAAP-specific SARDB for 6 Initiating Events (Large/Medium/Small LOCAs, LOOP, SBO, SGTR)

KAERIKAERI 17

Diagnostic result: future trend of ‘TWCR’ for ‘SBLOCA_S012’

Future Trends of ‘TWCR’ for the Predicted 11 PDS Sequences

After ScreeningAfter Screening

TWCR: temperature of water in core (K)

SBLOCA_S012: TWCR

SBLOCA_S012: SLOCA*/RT*/HPI*/AFW*/SR1*HPR*/DPR*LPR*CSR

⊙ Demo Application: Prognosis of Future Trend

KAERIKAERI 18

⊙ SAM-Decision Flow Chart (DFC)-SAMG entry time

S/G Water Level

RCS Pressure

Containment Pressure

SAMG Entry Time !

Making predictions about future trend of the 7 plant safety parameters to trigger the relevant SAMG and their

entry times, based on the user-specified thresholds

Entry time: 6.85 sec.

Entry time:10.4 sec.

Entry time: 30.32 sec.

LBLOCA-S03

KAERIKAERI 19

Water level in RPV

RPV LH Creep at 37355 sec.

Core uncover at 19876 sec.

No induced creep failure

RCS HL: unbroken RCS HL: broken

S/G: unbroken S/G: broken

PRV LH creep P-tube ejection

P-tube heatup Debris jet impingement

Making predictions about when core damage, core support plate failure, induced RCS & SG creep failure, reactor vessel

failure, and containment failure will occur

⊙ SAM-SSC Performance-failure time & probability

SBLOCA-S26

KAERIKAERI 20

Summary

Based on a concept of a RI-SAM, the present RI-SARD system explores a symptom-based diagnosis of potential PDS sequences in a risk-

informing way & a plant damage sequence-based prognosis of key plant parameter

behavior, in a simple, fast, and efficient way.

The replicated use of both processes makes it possible to extract information required for taking the intended SAM actions, consequently leading to an answer about what is the best strategy for SAM.

An example application through the OPR1000- and MAAP code-specific SAR DB has shown that the present approach can

enhance a diagnostic capability for anticipated plant states, give the SAM practitioners more time to take actions for mitigating the accident, reduce the still relatively large uncertainty in the field of SAM, and consequently, help guide the TSC staffs through a severe accident.

⊙ Concluding Remarks

KAERIKAERI 21

Future Plan for Improvement

Will involve the ability to link decisions made by RISARD with the SAM procedure and SA simulator, so that the impact of the SAM actions on an accident progression can be feedback to in an interactive way to a user.

Will involve the use of a more structured approach capable of ① diagnosing the current plant system state, ② predicting the most probable accident pathway during the progress of an accident, and ③ taking the best strategy to terminate its progression into an undesirable consequence, including a linking with

a diagnostic logic tree to diagnose effectively potential plant damage states, a simplified APET capable of predicting the progress of accidents accurately,

and a more sophisticated logical rule capable of extracting appropriate SAM

strategies for a given plant damage state

In addition, we will explore increasing the number of accident types recognized by RI-SARD (e.g., various spectrum of break sizes for LOCA & SGTR)

⊙ Concluding Remarks

Thank you for your attention !!!Thank you for your attention !!!