ML19011A444

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Lecture 9-3 PRA Frontier 2019-01-23
ML19011A444
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Issue date: 01/16/2019
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Nathan Siu 415-0744
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The Frontier: Grand Challenges and Advanced Methods Lecture 9-3 1

Overview Key Topics

  • PRA Grand Challenges
  • General Methods

- AI/Big Data

- Dynamic PRA 2

Overview Resources

  • N. Siu, K. Coyne, and F. Gonzalez, Knowledge Management and Knowledge Engineering at a Risk-Informed Regulatory Agency:

Challenges and Suggestions, U.S. Nuclear Regulatory Commission, March 2017. (ADAMS ML17089A538)

  • N. Siu, "Risk assessment for dynamic systems: an overview,"

Reliability Engineering and System Safety, 43, 43-73, 1994.

  • T. Aldemir (ed.), Advanced Concepts in Nuclear Energy Risk Assessment and Management, World Scientific Publishing Co (2018). (Available from:

https://www.worldscientific.com/worldscibooks/10.1142/10587) 3

Overview Other References

  • N. Siu and K. Coyne, Knowledge Engineering at a Risk-Informed Regulatory Agency: Challenges and Suggestions, in Knowledge in Risk Assessment and Management, T. Aven and E. Zio, eds., Wiley, 2018.
  • N. Siu, "Dynamic accident sequence analysis in PRA: A Comment on

'Human Reliability Analysis - Where Shouldst Thou Turn?'," Technical Note, Reliability Engineering and System Safety, 29, No. 3, 359-364, 1990.

  • D. Helton, Scoping Study on Advanced Modeling Techniques for Level 2/3 PRA, U.S. Nuclear Regulatory Commission, May 2009. (ADAMS ML091320447)

Overview The PRA Frontier

  • PRA is an integrative, systems engineering enterprise
  • Can stimulate advances in specific disciplines, but typically

- Dont own phenomena

- Advances involve new collection, adaptation, integration, assembly

  • Frontier

- Long-standing completeness issues (Grand Challenges)

- New tools and approaches 5

Grand Challenges Grand Challenges

  • Errors of commission

- TMI-2: throttling safety injection

- Chernobyl 4: disabling automatic trips

- Fukushima Dai-ichi 1: isolating passive system

  • Organizational factors

- Major role in accidents

- Arguably major role in most if not all incidents (e.g., Browns Ferry, Blayais, Davis-Besse)

  • Security-related events; design faults

- Controversies over fundamental framework

- Information availability 6

General Methods Technological Advances

  • Artificial Intelligence (AI)/Big Data
  • Simulation Methods and Dynamic PRA 7

AI/Big Data

  • Historical attempts

- Expert systems, e.g., fault diagnosis

- Automated model construction

  • Recent interest

- Prognostics

- Text mining 8

Big Data: Prognostics and Reliability Big Data: Prognostics and Reliability

  • Concept: use field data and physics of failure models to anticipate failures and develop prevention strategies
  • Longstanding interest in nuclear community, gaining ground with technological advances and applications to conventional power plants
  • A non-nuclear example: Formula 1 race cars

- 1600 lbs, turbocharged hybrids

- Engineering models

  • Real-time support during race
  • Empirically calibrated through testing
  • ~100 supporting staff at home office

- Miniscule performance D => major effects A. Gilbertson, 2016 9

Big Data: Prognostics and Reliability Real-Time Monitoring: Qualification at Circuit of the Americas (COTA)

Footage courtesy of A. Gilbertson Formula 1 Video COTA Turn 15 Entry speed ~210 km/h Speed at apex ~84 km/h Braking distance ~30m Braking power ~800 kW Load ~3g 10

Big Data: Prognostics and Reliability Data Streams

  • 120+ sensors (car and driver)
  • 1000-2000 wireless channels
  • Low latency - o(ms)
  • 2 GB/lap, 3 TB/race
  • Thermal cameras during qualification 11

Big Data: Prognostics and Reliability Data Use

  • Engineering models

- Empirically calibrated through testing

- Predict performance and wear over time

- Used to develop/modify (in real time) race strategies

  • Use in a Formula 1 PRA? Models dont cover all factors

- Road debris

- Other drivers

  • Would an analogous approach for NPPs catch risk-significant conditions?

12

Big Data: Prognostics and Reliability Sample Old But Interesting Events Year Plant Feature 1975 Browns Ferry Cable fire affects multiple units HVAC water falls into MCR panel; SRV cycles, 1985 Hatch sticks open 1993 Cooper External flood, one evacuation route blocked Steam line rupture, intermittent electrical 1997 Fort Calhoun grounds High wind and external flood affect multiple 1999 Blayais units, site access 2001 Maanshan High energy arc fault, station blackout 13

Big Data: Prognostics and Reliability More Interesting Events - Multi-Unit Precursors Date Plant Description Date Plant Description 6/22/82 Quad Cities LOOP, Maintenance 8/14/03 6 Sites LOOP, NE Blackout 8/11/83 Salem LOOP, Clogged screens 6/14/04 Palo Verde LOOP, Offsite fault 7/26/84 Susquehanna SBO, Bkr mis-aligned 9/25/04 St. Lucie LOOP, Hurricane 5/17/85 Turkey Point LOOP, Brush fires 5/20/06 Catawba LOOP, Switchyard fault 7/23/87 Calvert Cliffs LOOP, Offsite tree 3/26/09 Sequoyah LOOP, Bus fault 3/20/90 Vogtle SBO, Truck hit support 4/16/11 Surry LOOP, Tornado 8/24/92 Turkey Point LOOP, Hurricane 4/27/11 Browns Ferry LOOP, Winds/tornadoes 12/31/92 Sequoyah LOOP, Switchyard fault 8/23/11 North Anna LOOP, Earthquake 10/12/93 Beaver Valley LOOP, Offsite fault 3/31/13 ANO LOOP/Trip, Load drop Trip, Foreign material in 6/28/96 LaSalle SW Tunnel 4/17/13 LaSalle LOOP, Lightning 6/29/96 Prairie Island LOOP, High winds 5/25/14 Millstone LOOP, Offsite fault 14

Big Data: Prognostics and Reliability Observation

  • Prognostics could be useful for some PRA scenarios but probably not for many others
  • Need to think about specific needs of PRA when formulating an N+1 project 15

AI/Big Data: Text Mining A Different Use of AI/Big Data

  • Concept: use smart tools to identify and enable efficient use of documents for PRA/RIDM

- Responses to queries

- Active alerts

  • Example: Le Blayais (1999)*

- International Nuclear Event Scale (INES) Level 2 (out of 7): Incident with significant failures in safety provisions E. De Fraguier, Lessons learned from 1999 Blayais

- Reported in several venues flood: overview of EDF flood risk management plan, U.S. NRC Regulatory Information Conference, March 11, 2010.

- Missed by general PRA community

  • Actual vs. potential losses
  • Perceived importance of flooding

- Now generally considered a precursor to Fukushima

  • See Lecture 7-2 16

Fundamental knowledge engineering challenge - understanding the message(s)

Challenge Type Example Phrase Challenge for KE Tool Ambiguity (multiple meanings for lost = failed (versus missing, bewildered, pumps of Train A were lost the same word or phrase) etc)

Context dependence (meaning essential is part of system name (vs. descriptor, depends on other factors, essential service water as in references to non-essential service water including document type, purpose, pumps pumps) structure, and surrounding text)

Implicitness (meaning is not Of the facilities which were flooded the following should stated directly, and must be Widely separated text needs to be combined to be noted: rooms containing inferred from other facts in infer that the ESW pump rooms were flooded the essential service water document) pumps alternatives include different words, e.g., has The essential service water Non-uniqueness (multiple ways four pumps in two separate trains or different system of each unit comprises of making a statement with the constructions, e.g., There are four pumps in the four pumps on two same meaning) essential service water system, arranged in independent trains (A and B) independent trains A and B.

17

AI/Big Data: Text Mining Other Challenges

  • Enormous (and rapidly growing) volume
  • Information from embedded structures
  • Database limitations

- Legacy documents only in hardcopy

- Faults in documents

- Poor OCR, metadata

  • Enormous interest

- Hype

- Rapid advances 18

AI/Big Data: Text Mining An old vision Im worried

  • Intelligent aides about the

- Library resource (e.g., Star Trek, mission, Dave.

1966-1969)

Cmon HAL,

- Active agent (e.g., 2001: A Space open the pod Odyssey, 1968) bay door

  • Key features include:

- Natural language information processing

  • Data collection, validation, understanding, reporting
  • Performed in interactive mode (even dialog!)

- Prioritization for attention, action 19

AI/Big Data: Text Mining Risk Information: Special Characteristics

  • Systems viewpoint

- Multiple technical disciplines

- Problem scale and complexity (multitude of scenarios, interacting pieces)

- Diverse and implicit sources of information (licensing basis, operating experience, past analyses, )

  • Rare events What can go

- Sparse data wrong?

- Large uncertainties

  • Broad user base

- Technical backgrounds

- Approaches to cope with uncertainties

- Responsibilities 20

AI/Big Data: Text Mining Status Tool Toy

  • Rapidly growing interest in NRC, industry
  • Small-scale exploratory studies (NRC, EPRI) Searcher Aide Explorer Oracle
  • Human-in-the-loop approaches likely most efficient and effective (at least in near term)
  • Developments will depend on Servant Partner intended mode of application 21

Dynamic PRA Dynamic PRA - Not If But When?

  • Concept

- General: Explicitly model driving forces on NPP elements and element responses

- Typical: Simulation-oriented analysis

  • Origin

- Fast reactor uncertainty analyses

- Control systems

- Human reliability analysis 22

Dynamic PRA Attractive Features

  • As with simulation approaches in general

- Potential for improved realism

- Consistency with current directions in engineering

- Natural, holistic framework for integrating multiple disciplines

  • Chronological orientation - understandable stories
  • Better use of rich information from events
  • For PRA/RIDM, potential to address difficult problems (including Grand Challenges)

- Errors of commission

- Passive system reliability 23

Dynamic PRA 1F1, 3/11/2011 Relative Time Hazard Systems Indications Operators/Workers ERC/ER team EP Time 14:46 0:00 Earthquake Scram MSIVs close, turbine Rx level drops 14:47 0:01 trips, EDGs start and load ICs start automatically RV pressure decreases; 14:52 0:06 RV level in normal range ICs removed from Cooldown rate Manually remove IC 15:03 0:17 service exceeding tech spec from service limits Disaster HQ established 15:06 0:20 in TEPCO Tokyo Determine only 1 train 15:10 0:24 IC needed; cycle A train First tsunami 15:27 0:41 arrives Second 15:35 0:49 tsunami arrives 15:37 0:51 Loss of AC 1537-1550: Gradual loss Determine HPCI of instrumentation, unavailable indications (including IC 15:37 0:51 valve status, RV level),

alarms, MCR main lighting TEPCO enters emergency plan (loss of 15:42 0:56 AC power); ERC established D/DFP indicator lamp 16:35 1:49 indicates "halted" Cannot determine RV Review accident Declared emergency level or injection status; management (inability to determine work to restore level procedures, start level or injection) 16:36 1:50 indication; do not put IC developing procedure to in service open containment vent valves without power 24

Dynamic PRA 1F1, 3/11/2011 (cont.)

Relative Time Hazard Systems Indications Operators/Workers ERC/ER team EP Time 16:45 1:59 Determine RV level Emergency cancelled Tsunami alert Workers on way to 16:55 2:09 check D/DFP had to turn back Lose ability to determine Reentered emergency 17:07 2:21 RV level or injection plan status Site superintendent directs investigation of 17:12 2:26 using fire protection to inject water Estimated core 17:15 2:29 uncovery in 1 hr Tsunami alert 17:19 2:33 cleared Diesel-driven fire pump Pressure above 100 psi Manually open valves (in started and left to idle dark) from fire protection system to core spray 17:30 2:44 system; take turns holding D/DFP switch to keep in standby Govt orders seawater 18:00 3:14 injection DC power partially MO-3A and MO-2A 18:18 3:32 returned indicate closed MO-3A and MO-2A Open IC valves MO-3A 18:18 3:32 opened and 2A. Steam from condenser observed MO-3A closed Remove IC from service (concerned about failing lines). Entered R/B and T/B to manually open 18:25 3:39 MOV for FP lineup.

Hard time finding valve, had wrong key, hard to operate hand wheel.

Long time. 25

Dynamic PRA 1F1, 3/11/2011 (cont.)

Relative Time Hazard Systems Indications Operators/Workers ERC/ER team EP Time Core degradation and 18:46 4:00 major release (4-5 hr after trip)

Close valves for broken Ask Tokyo for more fire Following PM question outdoor FP pipes. engines about possible Broke lock to allow recriticality, TEPCO 19:00 4:14 passage between Units makes strong request to 2 and 3. site superintendent to suspend seawater injection Govt. declares nuclear 19:03 4:17 emergency No pressure indication in MCR; Reactor pressure 20:07 5:21 = 6.89 MPa (1000 psi) local indication Small portable generator MCR has temporary 20:49 6:03 installed lighting Local authorities order 20:50 6:04 evacuation within 2 km Level indication 21:19 6:33 restored; level = 0.20 m (8) above TAF Prime minister orders 21:23 6:37 evacuation within 3 km; sheltering out to 10 km MO-3A opened Place IC in service; 21:30 6:44 steam observed Access to RB restricted due to dose rates -

21:51 7:05 indirect indication of core uncover Level = 0.55 m (21.7) 22:00 7:14 above TAF Drywell pressure = 0.50 Restoration team from 23:50 9:04 MPa (87 psi) above ERC enables reading design Offsite power supply 23:59 9:13 trucks arrive by midnight 26

Dynamic PRA Errors of Commission - Importance of Context

  • Can always add a basic event Operator Stops RCIC.
  • Possible but sufficiently probable? Why or why not?

27

Dynamic PRA Predominant Approach: Discrete Dynamic Event Trees Historical More Recently Adapted from: N. Siu, "Risk assessment for dynamic systems: an overview,"

Reliability Engineering and System Safety, 43, 43-73, 1994 J. LaChance, et al., Discrete Dynamic Probabilistic Risk Assessment Model Development and Application, SAND2012-9346, Sandia National Laboratories, October 2012.

28

Dynamic PRA International R&D Organization Tool(s)

Electricité de France (EDF) RAVEN, PyCATSHOO Gesellschaft für Anlagen und Reaktorsicherheit (GRS) MC-DET Idaho National Laboratory (INL) RAVEN Ohio State University (OSU) ADAPT Sandia National Laboratories (SNL) ADAPT University of California at Los Angeles (UCLA) ADS/IDAC University of Maryland (UMD) ADS/IDAC, ADAPT Universidad Politécnica de Madrid (UPM) SCAIS 29

Dynamic PRA Implementation Challenges

  • Technical (many being addressed)

- Phenomenological sub-models

- Data

- V&V

- Computational resources

- Aids to support searches

- Aids to support sensemaking

  • Economic

- Demonstrating added value

- Demonstrating acceptable resource requirements 30

Dynamic PRA Implementation Challenges (cont.)

  • Socio-organizational

- Perception that dynamic PRA is necessarily complex

- Developer community mindset

  • Increased detail > increased realism
  • Importance of insights (vs. bottom line results)
  • Openness to concerns raised by skeptics

- User community mindset

  • Potential value of different approaches
  • Awareness of trends outside NPP PRA

- Targeting of development activities

  • R&D => product development
  • Increased emphasis on actual problem solving (beyond demos)
  • Role in PRA toolbox
  • What expertise is needed, how to develop and maintain 31
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