ANS 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
Kwang-Il Ahn
[email protected]
Integrated Safety Assessment
Korea Atomic Energy Research Institute
⊙ Outline
 Motivation & Objectives
 The Concept of RI-SAM
 Computerized Tool SARD
 Demonstrative Application
 Concluding Remarks
KAERI
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⊙ Motivation
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.
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⊙ Objectives:

RI-SAM Strategy & Tool
Our Approach for supporting SAM:



KAERI
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
4
⊙ RI-SAM:
Diagnosis & Prognosis of Plant States
Monitor Plant Data & Signal:
Identify the IE & CD states
Determine the status/availability of
systems needed to mitigate the IE
Signal Validation
Process
Plant
symptoms
Plant
Conditions
Determine
Plant symptoms
ReSet
Plant symptoms
& accident time
Diagnosis
Symptom-based
SARD module
AS screening (iteration loop)(2)
Yes
Prioritize
Frequency-based
potential accident
sequences
More
Symptoms
?
No
Auto switch to module for Prognosis of Future Plant Status
Performance
Plant
damage states
Quick view
Future trend
of symptom
parameters
Determine
Relevant PDS (1)
(dynamic loop)(2)
Prognosis
Scenario-based
SARD module
1. Key plant safety
parameters for SAM
2. Performance of
key SSCs
Success Paths
Countermeas
ures
Decision for
Implementation
of the relevant
SAM strategies
Modification
Link to SA Simulator
for Interactive Action
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.
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⊙ A Computerized Tool:
RISARD
RI-SARD
Key Functions & Modules
• Code-specific or user-supplied SA accident sequence information:
SARDB
RI-SARD
Menu
System
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)
• 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)
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⊙ SARD:
Data Sets Operation
SAR Information
- Level 1/2 PSA
- Accident Analysis
- SAM Information
- The other SAR Inform
Formatted SAR Data Sets
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
SARD System Operation
Data Allocation into
SARDB
SARDB: Databank
(MS Access DB)
Data Search & Retrieval,
Graphical Display
- Scenario-based Plant
Responses & Behavior
- Plant symptom-based
Potential Accident Sequences
- Status of Plant System &
Containment Systems
Database Update &
Modification
SAR-informed
Decision-making
(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)
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⊙ SARD:
SARDB Generation (1)
Plant-specific PDS ET
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 time
Developing trends of key
events during accident
MELCOR
HISPLT
SARDB
MELCOR Run
KAERI-IPLOT
Plantspecific
Accident
Scenarios
SARDB
Generation Module
MAAP Run
SARDB:
MELCOR/
MAAP DB
Plot Data
Parameter list
for comparison
SARD:
Plant state
Prognosis/
Diagnosis
KAERI
Typical Form of SARDB
Allocation of Plant-, Code-specific SA Analysis Results into SARDB
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⊙ SARD:

SARDB Generation (2)
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
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⊙ SARD:
SARDB Generation (3)
Dominant accident initiators:
Frequency-based screening of PDS sequences
LLOCA PDS ET
Large
LOCA
SITs
Injection
LPSIS
Injection
HPSIS
Injection
HPSIS
Recirculation
LPSIS
Recir.
HPSIS
Hot
Cold Leg
Recir.
Containment
Injection
Spray
Recir.
Cooling
using
CSS
Cavity
Flooding
System
Injection
LLOCA
SIT
LPI
HPI
HPR
LPR
HPH
CSI
CSR
RFSI
CDSQ2
S
E
Q
#
S
T
A
T
E
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
I.Es (/ry)
FREQ
LLOCA
(1.05x10-6)
CDSQ3
CSR
CSR
CDSQ4
Sequences
Contribution
(%) to I.E
Functional States of Safety Systems
(success state: ‘/sss’, failed state: ‘sss’)
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-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-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
LPR
CSR
CSR
HPR
CSR
MLOCA
(6.34x10-7)
CDSQ5
CSR
HPI
CSI
LLOCA
RFSI
CSR
CSR
HPR
LPR
CSR
CDSQ6
CSR
SLOCA
(1.92x10-6)
HPR
CSR
LPI
CSR
HPI
CSI
RFSI
KAERI
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⊙ SARD:
SARDB Generation (5)
Events history:
Plant system
status with time
PDS
sequencespecific SA
code
analysis
Parameters history:
SA code parameter
behavior with time
KAERI
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
….
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⊙ SARD:
SARDB Generation Module
SAR Data
ASQ data
Plant Data
SA Code data Code results
Specification of Code Data (Multiple)
AMP & Summary Data
Specification of the Target Scenario
Specification of Databank Index
Specification of Sensitivity Information
Specification of Code Data (Single)
Check of the Allocated Information
SARDB
MS Access DB
KAERI
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⊙ SARD:
Two-way information Retrieval
Set target
Plant ID & Code ID
Scenario Base (1)
Symptom Base (2)
PSA Information:
IE & Target Sequence
Plant Symptoms:
- Code Parameters
- Time windows
More symptoms?
AS Screening
Data Search:
Plant-/Code-/AS
sequence-specific
Responses
-
Display
Plant states
Base response
Sensitivity case
SAMG parameters
SSC performance
Events History
Prioritize Accident
Scenarios (i = 1, n), in
a risk-informing way
Auto
Switc
h
Target Sequence
End
of
Searc
h
(1) Retrieval of the specified- accident sequence-based plant/code behavior (Accident Diagnosis)
(2) Retrieval of plant symptoms-based potential accident sequences (Accident Prognosis)
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⊙ SARD: Plant Symptom-based Diagnosis
PDS ET Events Functional Status
Set Plant & Code information
List of potential
plant damage
states
The most probable
plant system state
Switch to
Switch
to Module
the Scenario-based
the Scenario-based Module
Progression
of key events
Future trend of
plant parameters
User-specified
plant symptoms
KAERI
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⊙ SARD:
PDS sequence-based Prognosis
Display of the Corresponding PDS ET
Set Plant & Code information
PDS ET Events Functional Status
User- specified
accident conditions
User- specified
code/plant
parameters
Future history key events &
plant parameters
KAERI
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⊙ Demo Application: Diagnosis of PDS sequences
Target: OPR1000-/MAAP-specific SARDB for 6 Initiating Events
(Large/Medium/Small LOCAs, LOOP, SBO, SGTR)
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_S012
SBLOCA_S059
SBLOCA_S055
LOOP_S053
SBLOCA_S058
SBLOCA_S013
SBLOCA_S045
SBLOCA_S026
SBLOCA_S021
SBLOCA_S011
LOOP_S064
SBLOCA_S070
1.109E-06
6.256E-07
2.281E-08
1.205E-08
1.150E-08
5.270E-09
3.590E-09
2.593E-09
1.598E-09
1.058E-09
4.141E-10
1.759E-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
KAERI
SBLOCA_S012, SBLOCA_S013, SBLOCA_S011
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⊙ Demo Application: Prognosis of Future Trend
Future Trends of ‘TWCR’ for the
Predicted 11 PDS Sequences
After Screening
Diagnostic result: future trend of
‘TWCR’ for ‘SBLOCA_S012’
SBLOCA_S012: SLOCA*/RT*/HPI*/AFW*/SR1*HPR*/DPR*LPR*CSR
TWCR: temperature of water in core (K)
SBLOCA_S012: TWCR
KAERI
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⊙ SAM-Decision Flow Chart (DFC)-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
LBLOCA-S03
S/G Water Level
Entry time: 6.85 sec.
SAMG Entry Time !
Entry time:10.4 sec.
RCS Pressure
Entry time: 30.32 sec.
Containment Pressure
KAERI
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⊙ SAM-SSC Performance-failure time & probability
Making predictions about when core damage, core support
plate failure, induced RCS & SG creep failure, reactor vessel
failure, and containment failure will occur
SBLOCA-S26
Water level in RPV
Core uncover
at 19876 sec.
No induced creep
failure
RCS HL: unbroken
RCS HL: broken
S/G: unbroken
S/G: broken
RPV LH Creep at
37355 sec.
PRV LH creep
P-tube heatup
KAERI
P-tube ejection
Debris jet impingement
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⊙ Concluding Remarks

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 riskinforming 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




KAERI
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.
20
⊙ Concluding Remarks

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)
KAERI
21
Thank you for your attention !!!