ML17263B165
| ML17263B165 | |
| Person / Time | |
|---|---|
| Issue date: | 09/20/2017 |
| From: | Nathan Siu NRC/RES/DRA |
| To: | |
| References | |
| Download: ML17263B165 (42) | |
Text
PSA R&D: Changing the Way We Do Business?*
N. SiuANS International Topical Meeting on Probabilistic Safety Assessment (PSA 2017)Pittsburgh, PASeptember 24
-28, 2017*The views expressed in this presentation are not necessarily those of the U.S. Nuclear Regulatory Commission 2IntroductionThis talk-Provides some personal, pragmatic views on R&D intended to support Risk-Informed Decisionmaking (RIDM) in the nuclear industry-Addresses formulation, practice, and support of R&D;* not a
c atalog of topic areas
-Presented in two partsGeneral R&D contextRemarks for different PRA communities (developers, analysts, users) And about that title-*In the remaining slides, the focus on RIDM support is understood R&D CONTEXT AND PERSPECTIVES 4R&D: A Broad Enterprise-Provides technical advice, tools, and information to meet organizational needsBroader than development of
theory and methodsAt NRC, includes activities to suppor t-Decisions on specific issues
-Infrastructure (standards, etc.)
-Understanding (context for d ecisions)-Communication with stakeholdersMethods, Models, Tools, Databases, Standards,Guidance, -General KnowledgeSupportingAnalysesDecisionR&D 5-with Multiple StakeholdersDevelopersAnalysts/ReviewersUsers 5 6Personal Background => PerspectivesDeveloperAnalyst/ReviewerUser(Advice)6 7Dynamic R&D EnvironmentNeeds-"New" events/findings
-"New" technologies/designs
-Increasing demands on PRA technology
-Risk-informed applications (current plants)Resources-Computational infrastructure
-Demographics
-Budget 8Dynamic R&D Environment -Applications 9Dynamic R&D Environment -Demographics 9
10Dynamic R&D Environment
-RES Budget 11Assumptions"We hold these truths to be self-evident-"Risk-related Regulatory R&D (R4&D) has been and continues to be a necessary component of a healthy nuclear enterpriseR4&D products need to be implemented to affect safety There will continue to be challenges in R4&DNo one community has all of the answersRegulations andGuidanceOperationalExperienceLicensing andCertificationOversightDecision Support(e.g., research, PRA)
R4&D STAKEHOLDERS 13Discussion FrameworkSub-communities that are the focus of remarksDevelopers: academic c ommunityAnalysts/Reviewers:
PR A model buildersUsers: decision makersDevelopersAnalysts/ReviewersUsers 14DevelopersTypical challenges
-Academic contribution
-Nexus between personal and external interests
-Support (esp. with declining budgets!)R4&D solutions include
-Frameworks, methodologies, conceptual demonstrations
-N+1 projects
$$$
15Technology-Driven vs. Issue
-Driven R4&DIs it "Hammer Time"?A common (and valid) research strategy
{New Tech*} + {"Interesting" Problem} => {Research Topic
}(Of course) beware
-Force-fitting the problem to match the new tech
-Problems not truly requiring the new tech's special capabilities-Problems of tangential importance to major risk drivers*Technology = {methods, models, tools}
16Big Data: Prognostics and ReliabilityAn N+1 ExampleConcept: use field data and physics of failure models to anticipate failures and develop prevention strategies"Formula 1" races
-Heavily instrumented cars
-Engineering modelsReal-time support during raceEmpirically calibrated through testing~100 supporting staff at home office
-Miniscule performance => major effectsA. Gilbertson, 2016 1723 sec video: 2016 US Grand PrixBig Data: Prognostics and ReliabilityCOTA Turn 15Entry speed ~210 km/hSpeed at apex ~84 km/h Braking distance ~30m Braking power ~800 kW Load ~3gRace footage courtesy of A. Gilbertson 18Big Data: Prognostics and Reliability120+ sensors (car and driver)1000-2000 wireless channelsLow latency
-o(ms)2 GB/lap, 3 TB/raceThermal cameras during qualification 19Big Data: Prognostics and ReliabilityWill it work for R4&D?Engineering models
-Empirically calibrated through testing
-Predict performance and wear over time
-Used to develop/modify race strategies
-Models don't cover all factorsRoad debrisOther driversIn a NPP application, what risk
-significant failures would be caught by an analogous system?
20Big Data: Prognostics and ReliabilitySome Old But Interesting EventsYearPlantFeature1975Browns FerryCable fire affects multiple units1985HatchHVAC water falls into MCR panel; SRV cycles,sticks open1993CooperExternal flood, one evacuation routeblocked1997Fort CalhounSteam line rupture, intermittent electrical grounds1999BlayaisHigh wind and external flood affect multiple units, siteaccess2001MaanshanHighenergy arc fault, station blackout 21Big Data: Prognostics and Reliability Multi-Unit Precursor EventsDatePlantDescription6/22/82Quad CitiesLOOP, Maintenance8/11/83SalemLOOP, Cloggedscreens7/26/84SusquehannaSBO, Bkrmis-aligned5/17/85Turkey PointLOOP, Brush fires7/23/87Calvert CliffsLOOP, Offsite t ree3/20/90VogtleLOOP, Truck hitsupport8/24/92Turkey PointLOOP, Hurricane12/31/92SequoyahLOOP, Switchyard fault10/12/93Beaver ValleyLOOP, Offsite fault6/28/96LaSalleTrip, Foreign material in SW Tunnel6/29/96Prairie IslandLOOP, HighwindsDatePlantDescription8/14/036 SitesLOOP, NE Blackout6/14/04Palo VerdeLOOP, Offsite fault9/25/04St. LucieLOOP, Hurricane5/20/06CatawbaLOOP, Switchyard fault3/26/09SequoyahLOOP, Bus fault4/16/11SurryLOOP, Tornado4/27/11Browns FerryLOOP, Winds/tornadoes8/23/11North AnnaLOOP, Earthquake3/31/13ANOLOOP/Trip, Load drop4/17/13LaSalleLOOP, Lightning5/25/14MillstoneLOOP, Offsite fault 22Big Data: Prognostics and ReliabilityHow Do Things Fail? (Service Water)"the station declared all Core Standby Cooling Systems (CSCS), Emergency Core Cooling Systems (ECCS), and Diesel Generators (DG) inoperable due to foreign material identified on the floor of the service water tunnel-Although the systems were declared inoperable, they were available. The foreign material was an injectable sealant foam substance which had been used - in the Lake Screen House (LSH) to seal water seepage cracks." (LER 373/96-008)"-manual reactor shutdown - conservatively initiated ... due to concern for the safety and well being of a diver working in the - Unit 2 circulating water pump house discharge piping - communications with one diver was [sic] lost and the retrieval efforts by a second and third diver were initially unsuccessful in reestablishing contact.. The plant equipment and systems - worked as designed. The divers were unharmed." (LER 266/00-001) 23Big Data: Prognostics and ReliabilityAn Alternate/Complementary Line of R4&D?Searching is fundamental to PRA:
-First question of risk triplet: "What can go wrong?"
-PRA Procedures Guide and ASME/ANS PRA StandardSparse data, beyond design
-basis concerns => imagination neededOperational experience can fuel, temper, and support imagination
-Massive, unstructured databases
-How to better use?Investigation Committee on the Accident at Fukushima (7/23/2012): "TEPCO lacked a sense of urgency and imagination toward major tsunami, which could threaten to deal a fatal blow to its nuclear power plants.
"E. De Fraguier, "Lessons learned from 1999 Blayaisflood: overview of EDF flood risk management plan," U.S. NRC Regulatory Information Conference, March 11, 2010.
24Big Data: Prognostics and Reliability"Mr. Watson come here, I want you."Advanced knowledge engineering technologies can helpIT challenges include:
-Faulty data
-Embedded structure
-Machine learning
-SpeedPRA user challenges include:
-"Use case" identification, specification
-Working with IT: develop, test solutionsICA 2.2IntelligentPersonal AssistantsWatsonSearcher,ExplorerAide, OracleTool,ToyServant,Partner 25Analysts/ReviewersTypical challenges
-Need near-term solutions: heavy time/budget pressure
-Huge problem size and complexity
-Multiple technical communities/cultures
-State of technology: Too much/little
d iversity, "Holes"PRA solutions include
-"Tried and true"; reluctance to try new approaches
-Engineering judgment
-Completeness uncertaintyReverse ageism in the PRA community 26Dynamic PRA: What and WhyLiteral view: explicit treatment of timeMore broadly: explicit treatment of dynamic phenomena (simulation-based)Potential advantages include
-Direct ties to other science/engineering models (with usual V&V)
-Elimination of intermediate modeling
a pproximations
-Natural language for interdisciplinary
en terprise-Consistent with external tech world
-Most direct approach to some tough problems
-Academically rewarding 27Dynamic PRA: Why NotDynamics not key issue for many problemsModels can be complex:
-Resource-intensive (construction, validation, computation, analysis/sensemaking)
-Inscrutable (at least to practitioners)
-Vulnerable to sub
-model applicability limits
-Massive output, but information?Long gestation, in early phase of maturity
-Starting to expand from academic centers
-Few real applications of full
-power tools
-Reward system has likely inhibited search for
sim ple but practical applicationsThe Aldemir Tank 28Dynamic PRA: An Opportunity Missed?Object-oriented simulation: long history, fully developed general technology
-Operations Research and "System dynamics"-Military simulations and supporting tools
-Considered but rejected for general PRA
qua ntification (aleatory uncertainty)
-Highly limited, demonstration
-oriented NPP PRA applications (power recovery)Basis for advanced Vulnerability Assessment toolsWell-suited for FLEX?
29Fire PRA: From Research to ApplicationFollowing 1975 Browns Ferry fire
-NRC supported fire PRA R&D at UCLA and RPI
-Methodology and tools used in industry
-sponsored PRAs-Approach used in NUREG
-1150-Formed basis for guidance
-Currently continuing improvements on piece partsSuccess factors
-Real problem
-NRC and industry involvement
-Researchers directly involved in application 30UsersDecision maker challenges include
-Managing/leveraging resources
-Prioritizing needs and activities Short-vs. long-termOrganizationalMultiple stakeholders
-Difficult decisionsTechnical complexityHigh uncertainty, diverse viewsMulti-variateA key adviser challenge: communication"Aleatory"-
31Communication Challenges: Model Uncertainty 32Communication Challenges: Model UncertaintyModel uncertainty is important and realSome R4&D questions
-Ensemble "better" than best estimate + sensitivities? Under what conditions?-How important is it to understand the technical reasons for model
-to-model variability?
-Ensemble doesn't necessarily capture (revealed) reality: what to do
w hen all options have significant costs and potential consequences?"If anything on these products causes confusion, ignore the entire product."http://my.sfwmd.gov/sfwmd/common/images/weather/plots.html 33Communication Challenges: GatekeepingPre-Fukushima WGRISK report on external hazards PSA
-Varied country responsesSome treatment for internal events (CCF, LOOP, LOHS)Research on some hazards (seismic, ty phoon)Some with no special considerations
-Topic, findings, or recommendations not provided in ConclusionsOmission: lack of actionable messageR4&D question: When (under what circumstances) should we boost the signal? How? [see Blayais]
34Communication Challenges: Unintended MessagesHow not to start an R4&D program-IDFire PRA "Issue" IDFire PRA "Issue" I1Adequacy of fire events database P1Circuit interactions I2Scenario frequencies P2Availability of safe shutdown equipment I3Effect of plant operations, including comp measures P3Fire scenario cognitive impact I4Likelihood of severe fires P4Impact of fire induced environment on operators E1Source fire modeling P5Role of fire brigade in plant response E2Compartment fire modeling R1Main control room fires E3Multi-compartment fire modeling R2Turbine building fires E4Smoke generation and transport modeling R3Containment fires H1Circuit failure mode and likelihood R4Seismic/fire interactions H2Thermal fragilities R5Multiple unit interactions H3Smoke fragilities R6Non-power and degraded conditions H4Suppressant
-related fragilities R7Decommissioning and decontamination B1Adequacy of data for active and passive barriers R8Fire-induced non
-reactor radiological releases B2Barrier performance analysis tools R9Flammable gas lines B3Barrier qualificationR10Scenario dynamics B4Penetration sealsR11Precursor analysis methods S1Adequacy of detection time dataR12Uncertainty analysis S2Fire protection system reliability/availability O1Learning from experience S3Suppression effectiveness (automatic, manual)
O2Learning from others S4Effect of compensatory measures on suppression O3Comparison of methodologies S5Scenario-specific detection and suppression analysis O4Standardization of methods42 = 1 [year]From: N. Siu, J.T. Chen, and E. Chelliah, "Research Needs in Fire Risk Assessment," NUREG/CP
-0162, Vol. 2, 1997.
35Closing RemarksR4&D has helped change the way we do businessContinuous improvement + changing times => some suggestions-Developers: Dig a little deeper
-Analysts/reviewers: Give '
em a chance-Advisers: Be like SherlockThere's gold inthese hills-36Give 'Ema Chance [2]"Grizzled Vets""Young Pups" ADDITIONAL SLIDES 38Dynamic R&D Environment
-Industry Needs*Finding ClosureRisk-Informed SSC Categorization (50.69)Risk-Informed Completion Times (TSTF-505)Fire PRA Realism Methods VettingAggregationRealism in Reactor
O versight ProcessFLEX in Risk
-Informed D ecision Making*From public meeting of NRC and Industry Risk
-Informed Steering Committees, April 13, 2017 (ML17104A315) 39 0500 1000 1500 2000 2500 3000 3500 4000 4500 0 200 400 600 8001000 1200 1400NRC Budget (FY 1976
-FY 2017)Actual ($M)Fiscal YearBudget ($M)Data from NUREG
-1350 (NRC Information Digest)Staff (FTE) 40Big Data: Prognostics and ReliabilityHow Do Things Fail? (Power)"-both units tripped automatically from 100% power following a Loss of Offsite Power (LOOP) event. The event began when a fault occurred internal to a current transformer associated with one of the switchyard power circuit breakers. A second current transformer failure, along with the actuation of differential relaying associated with both switchyard busses, cleared both busses and separated the units from the grid... The root cause analysis - determined that - certain switchyard relay tap setting changes were not implemented." (LER 413/06-001)"-Units 1 and 2 received an automatic reactor trip on reactor coolant pump (RCP) buses undervoltage. A loss of Common Station Service Transformer (CSST) C caused a loss of power to two unit boards on each unit that feed RCPs- The cause of the bus fault was determined to be degraded bus bar insulation and water intrusion into the CSST D secondary bus duct." (LER 327/09-003) 41Historical View on Searching"PRA Procedures Guide," NUREG/CR-2300 (1983)The search for dependent failures should be performed as described in Section 3.7 and incorporated as appropriate into the plant and system models.A preliminary systems analysis can thus be a vital step in the search for
i nitiators, helping to ensure completeness in the definition of accident sequences.Another approach is to more formally organize the search for initiating
e vents by constructing a top level logic model and then deducing the appropriate set of initiating events.A systematic search of the reactor
-coolant pressure boundary should be performed to identify any active elements that could fail or be operated in such a manner as to result in an uncontrolled loss of coolant.a more formal search and documentation of all elements that depend on
i nput from another source beyond the identified system boundary may be appropriate.
42External Flooding: A Really Big PictureSparse data and concerns with extrapolation => mechanistic analysisDaunting scale
-Regional analysis
-Human actions
-Besides flooding level: duration, deb ris, dynamic forces, warning time
-Multi-site impactsHow can R4&D help?
-Multiple, heterogeneous evidence sets =>
uncertainty-Demonstration of validity of more restricted
r epresentation