ML21138A793: Difference between revisions

From kanterella
Jump to navigation Jump to search
(StriderTol Bot insert)
 
(StriderTol Bot change)
Line 16: Line 16:


=Text=
=Text=
{{#Wiki_filter:I Still Have Nightmares About That Class*
{{#Wiki_filter:}}
PRA: why its complicated and why it doesnt have to be Nathan Siu Senior Technical Adviser for PRA Analysis                                                                  Special Guests:
Office of Nuclear Regulatory Research                                                                      Prof. George Apostolakis Dr. Harold S. Blackman Division of Risk Analysis Dr. Dennis C. Bley Dr. Robert J. Budnitz RES Staff Technical Seminar (Virtual) - Part 1                                                            Prof. Ali Mosleh John W. Stetkar May 13, 2021 (2:00-3:00)
Dr. Thomas R. Wellock
* The views expressed in this presentation are not necessarily those of the U.S. Nuclear Regulatory Commission.
 
After 40+ years, PRA seems intuitive to me Typewriters, punch cards => laptops It cant be done => modern risk-informed regulator Indian Point PRA Quad COMPBRN              Summer                            Cities (NRC-support)              at NRC                          IPEEE Browns Ferry Fire,        Join                Join                  Join            Join WASH-1400            PLG                MIT                    INL            NRC          9/11                          Fukushima            COVID-19 1975            1980              1985              1990              1995          2000              2005              2010            2015  2020 Punch card graphic adapted from: https://en.wikipedia.org/wiki/Punched_card#/media/File:FortranCardPROJ039.agr.jpg. Publicly available under Creative Commons Attribution-Share Alike 2.5 Generic conditions, 2
 
but it might not be to others An old survey                                                                                                        More recently Carolyn                            Kenny                Christopher (12)                              (9)                      (4)                          You no longer need to Who does        The Nuclear                                                                                          be a mathematical genius Wha? The Daddy            Regulatory government Me                          to run a reliability or risk work for?        Commission analysis.
Makes sure nuclear He reads a lot of What does plants dont go he do?          overboard or stuff and goes                    Write                            - Ola Bckstrm (2021)1 to meetings something like that 1Ola Bckstrm, The role of digital insight in a safer nuclear industry, Power, January 28, 2021. (Available from:
3    https://www.powermag.com/the-role-of-digital-insight-in-a-safer-nuclear-industry/)
 
Talk Outline
* PRA: what is it and why do it?                Alphabet Soup PRA = Probabilistic Risk Assessment
* Challenges and complications      RIDM = Risk-Informed Decision Making
* Strategies for reducing complexity
* Closing remarks 4
 
PRA: WHAT AND WHY 5
 
Risk Assessment
* Risk (per Kaplan and Garrick,1 adopted by NRC2)                                                                          Whats in a word?
  - What can go wrong?                                                                                              analysis, n., process of
  - What are the consequences?                                                                                      separating an entity into its constituent elements; process as
  - How likely is it?                                                                                              a method for studying the nature of something or determining its
* Qualitative as well as quantitative                                                                                essential features and their relationships
* Non-prescriptive, flexible
  - Does not define wrong or prescribe metrics for                                                                assessment, n., an estimation or judgment of value [emphasis consequences or likelihood                                                                                  added] or character
  - Does not define how risk is to be assessed 1S. Kaplan and B.J. Garrick, On the quantitative definition of risk, Risk Analysis, 1, 1981.
2See, for example:
6    - White Paper on Risk-Informed and Performance-Based Regulation (Revised), SRM to SECY-98-144, March 1, 1999.
    - Glossary of Risk-Related Terms in Support of Risk-Informed Decisionmaking, NUREG-2122, May 2013.
 
PRA  Risk assessment where likelihood is quantified in terms of probability
* Still flexible - definition does not mandate                                                                            Subjective Interpretation of Probability1 specific methods (e.g., event tree/fault tree analysis)
* Probability quantifies degree of belief
* Appropriate for decision support
* Typically: engineering analysis process
* Inherent in current PRAs (e.g., Bayesian
      - Models facility/process as an integrated system                                                              updating)
* Not universally accepted
      - Attempts to address all important scenarios                                                                    Subjectivity uncomfortable for many (within study scope)                                                                                          Technical objections (appropriateness of a lottery model for characterizing
      - Attempts to use all practically available,                                                                          subjective uncertainty) relevant information (not just statistics) 1See:
    - G. Apostolakis, Probability and risk assessment: the subjectivistic viewpoint and some suggestions, Nuclear Safety, 9, 305-315(1978).
    - G. Apostolakis, The concept of probability in safety assessments of technological systems, Science, 250, 1359-1364(1990).
7  - M. Granger Morgan, Use (and abuse) of expert elicitation in support of decision making for public policy, National Academy of Sciences Proceedings (NASP), 111, No. 20, 7176-7184, May 20, 2014.
 
Why PRA?                                                                                              Risk assessment is a set of tools, not an end in itself. The limited resources available should be spent to generate information that helps risk managers PRA Policy Statement (1995)1                                                                          to choose the best possible course of action among the available options.
* Increase use of PRA technology in all regulatory                                                                        -    National Research Council, 1994 matters It [fire PRA] aint perfect but its the
  - Consistent with PRA state-of-the-art                                                              best thing weve got.
  - Complement deterministic approach, support defense-                                                                                            - G. Holahan in-depth philosophy                                                                              Our tendency is to focus on things
* Benefits:                                                                                            that are interesting and make them (1) Considers broader set of potential challenges                                                    important. The thing that we have to do is focus on what really is (2) Helps prioritize challenges important (3) Considers broader set of defenses                                                                                                        - R. Rivera, 2020 1U.S. Nuclear Regulatory Commission, Use of Probabilistic Risk Assessment Methods in Nuclear Activities; Final Policy Statement, 8      Federal Register, 60, p. 42622 (60 FR 42622), August 16, 1995
 
Risk information has uses beyond immediate decision    Adapted from NUREG-2150 support 9
9
 
Moving Forward
* Past successes1 => expectation of future successes
* Past results => anticipation of Average Plant CDF 1.00 Probability {one or more accidents before t}
0.90      International Fleet ~ 440 rx            10-4/ry future challenges                                                                                                                              0.80 0.70 5*10-5/ry
* Continued investment => readiness                                                                                                              0.60 0.50 to meet challenges, maintain NRC 0.40 0.30 0.20                                                10-5/ry international leadership                                                                                                                        0.10 0.00 0        10      20      30        40  50 Years from Now 1For examples, see Probabilistic Risk Assessment and Regulatory Decisionmaking: Some Frequently Asked Questions, NUREG-2201, September 2016.
10
 
NPP PRA: ITS CHALLENGING 11
 
Fatality Rate by Vehicle Type (2018)
Fatalities/105 Vehicles Lots of Data => Statistical Analysis                                                                                                                                        15.0 10.0 Cars SUVs 5.0                                                                            Pickups 0.0                                                                            Vans 2009  2010  2011  2012  2013  2014  2015    2016  2017  2018 Alcohol-Impaired Driving From Traffic Safety Facts: Research Note, U.S. Dept. of Transportation, 2016.
Fatality Rates per 106 VMT (2018)
Motor Vehicle Fatalities                                                                                                                                                                            U.S. Average: 0.32 Fatality Rate (per 100M VMT) 60,000                                                                        6.00 Maryland: 0.20 50,000                                                                        5.00 40,000                                                                        4.00 Fatalities 30,000                                                                        3.00                                                                  Accident Causes                                                                  2005-2007 Driver Errors 20,000                                                                        2.00 10,000                                                                        1.00                                                                                                                                                Recognition Driver Decision 0                                                                          0.00                                  Vehicle                                                                                                      Performance 2009  2010  2011  2012  2013  2014  2015  2016  2017  2018 Environment                                                                                                  Non-Performance Unknown                                                                                                      Other Data from https://crashstats.nhtsa.dot.gov 12
 
Fundamental NPP PRA                                  Accident        In a nutshell                                Note TMI 2          Anticipated transient +                      Unlikely confluence Challenge: Little/No Plant-                          (1979)          additional failures and errors                of likely events Level Data                                            Chernobyl 4 (1986)
Systems test in unstable regime, violating procedures Single-minded aim to perform test Fukushima
* Sparse data                                        Daiichi 1-3    Beyond design basis tsunami Extremely unlikely catastrophic event
    - Few accidents/serious incidents                (2011)
    - Statistical relevance challenged by design and 2021: ~18700 reactor-years operational changes
    - Interest in specific plant => further reduced                                                    significant data set                                                                                          precursor
* Coping strategies
    - Decomposition-based systems modeling (e.g., event trees, fault trees)                                                                            precursor
    - Specialized estimation procedures (e.g.,
Bayesian statistics, expert elicitation) for model elements
=> Complexity (no free lunch)                                            Licensee Event Reports 1969-2019 (~4360 ry)
(No significant precursors since 2002; one under review) 13
 
PRA Complications
* Inherent in problem, e.g.,                                                                                          com*pli*cat*ed, adj. consisting of many parts not easily
    - Complex phenomenology (often beyond                                                                              separable; difficult to analyze, understand, explain, etc.
experience)
    - Multiple technical disciplines, roles, and For many years, risk assessment required perspectives                                                                                                    a high level of abstraction and an elite team of analysts fully immersed in the
* Highlighted (or even introduced) by                                                                                  ways of every single component and their failure profiles. A heady task for any risk analyst, but one made doubly hard by the coping strategies for sparse data                                                                                  exacting requirements of nuclear.
                                                                                                                                            - Ola Bckstrm (2021)1 1Ola Bckstrm, The role of digital insight in a safer nuclear industry, Power, January 28, 2021. (Available from:
14  https://www.powermag.com/the-role-of-digital-insight-in-a-safer-nuclear-industry/)
 
Complex Phenomenology: Scenario Dynamics (1)
Time            Hazard                Systems                      Indications              Operators/Workers                      ERC/ER team                            EP Time 14:46  0:00 Earthquake    Scram MSIVs close, turbine trips, EDGs 14:47  0:01                                                  Rx level drops start and load RV pressure decreases; RV level 14:52  0:06                ICs start automatically in normal range Cooldown rate exceeding tech 15:03  0:17                ICs removed from service                                          Manually remove IC from service spec limits Disaster HQ established in TEPCO 15:06  0:20 Tokyo Determine only 1 train IC 15:10  0:24 needed; cycle A train First tsunami 15:27  0:41 arrives Second tsunami 15:35  0:49 arrives 15:37  0:51                Loss of AC 1537-1550: Gradual loss of instrumentation, indications 15:37  0:51                                                                                  Determine HPCI unavailable (including IC valve status, RV level), alarms, MCR main lighting TEPCO enters emergency plan 15:42  0:56                                                                                                                                                  (loss of AC power); ERC established D/DFP indicator lamp indicates 16:35  1:49 "halted" Review accident management      Cannot determine RV level or      Review accident management procedures, start developing    injection status; work to restore  procedures, start developing  Declared emergency (inability to 16:36  1:50 procedure to open containment  level indication; do not put IC in procedure to open containment determine level or injection) vent valves without power      service                            vent valves without power 15
 
Complex Phenomenology: Scenario Dynamics (2) 16
 
Coping with Dynamics
* Aggregation (bundling)
* Simplified timing + success criteria For an early discussion of transitions between sequences, see G. Apostolakis and T.L. Chu, Time-dependent accident sequences 17  including human actions, Nuclear Technology, 64, 115-26 (1984).
 
Complication: Multiple Disciplines, Multiple Roles Different points of view:
* Whats important to the analysis?
* Whats an acceptable solution approach?
Analysts/
Users Reviewers Plant Systems Electrical              Human Factors Mechanical Civil NPP        Fire Protection Materials      PRA          Earth Sciences Probability Nuclear                  & Statistics Operational    Systems Developers Experience    Science 18
 
External Flooding at Plant X: Model Scope?
U.S. watershed image from https://www.nps.gov/miss/riverfacts.htm 19
 
Diverse Views: From Coping to Benefitting?
From You PRA Guys/Gals to Us PRA Guys/Gals?
* Clear definition of analysis needs, interfaces
* Stakeholders 101: early, open engagement
* Future: integrated native language analysis (e.g., dynamic PRA)?
20
 
Complication: Numerous Possibilities
* Many paths to core damage
* Many ways to fail each barrier in path 21
 
Coping with Multiple Scenarios
* Model simplifications, e.g.,
  - Screening
  - Grouping (often with bounding quantification)
* Boolean algebra, reliability theory,1 e.g.,
for independent basic events, where                                                                                                  CAFTA RISKMAN 1      1        1        1                                                            Risk Spectrum
* Software tools to implement theory 1 See, for example, R.E. Barlow and F. Proschan, Statistical Theory of Reliability and Life Testing Probability Models, To Begin 22  With, Silver Spring, MD, 1975. (Available in the NRC Technical Library: TS173.B37 c.1)
 
Complication: Sparse Data Potomac River Flooding (Little Falls, VA) 30 28 26 Flood Height (ft) 24 22 20 18 16 Major Flood 14 Moderate Flood 12 10 1930      1940      1950      1960      1970      1980      1990      2000        2010  2020 Data from: https://water.weather.gov/ahps2/crests.php?wfo=lwx&gage=brkm2&crest_type=historic 23
 
Coping with Sparse Data: Modeling + Bayesian Estimation Potomac River (Little Falls, VA)1
* First cut bounding analysis: major flood1 => catastrophic flood Date              Flood Height (ft)
* Frequency of major flooding ()
5/14/1932                15.25
      - Prior state-of-knowledge: minimal 2/27/1936                14.69
      - Evidence: 12 major floods over 1932-2019 (87 years) 3/19/1936                28.10
      - Bayes Theorem:                                      ,                                                          4/28/1937                23.30
                                                                                ,                                      10/30/1937                15.62
      - Posterior state-of-knowledge:                                        Poisson    Non-informative                10/17/1942                26.88 4/29/1952                14.17 05 = 0.079/yr probability density 8/20/1955                17.60 prior                                                50 = 0.13/yr                  6/24/1972                22.03 posterior              95 = 0.21/yr mean = 0.14/yr                11/7/1985                17.99 1/21/1996                19.29 0.00          0.05      0.10        0.15        0.20            0.25          0.30        9/8/1996                17.84 Major Flood Frequency (/yr)
* More sophisticated analysis if needed (e.g., frequency-magnitude analysis (perhaps with expert elicitation) 1 Data                from: https://water.weather.gov/ahps2/crests.php?wfo=lwx&gage=brkm2&crest_type=historic 2Major                Flood: height > 14 ft 24
 
More Complications: Expert Elicitation >> BOGGSAT1 what we know
* Mechanism to support decision making                                                              what we believe                P{XlC,H}
    - Diverse, authoritative views
    - Broad range of evidence                                                                                            proposition/event      conditions of of concern        probability
* Social process => social biases; need                                                                                                            statement
    - Formal elicitation processes (e.g., SSHAC2)                                                    Level          Characteristics
    - Sufficient time and resources                                                                    1          TI only (literature review, personal experience)
* Need to remember purpose and context;                                                                2          TI interacts with proponents and resource experts follow-on experimentation, analysis, etc.                                                            3 TI brings together proponents and resource experts may be needed 4          TFI organizes expert panel to develop estimates TI = Technical Integrator TFI = Technical Facilitator/Integrator 1BOGGSAT:  Bunch of guys and gals sitting around a table 2SSHAC:  Senior Seismic Hazard Analysis Committee. See R. J. Budnitz, et al., Recommendations for Probabilistic Seismic Hazard 25  Analysis: Guidance on Uncertainty and Use of Experts, NUREG/CR-6372, 1997.
 
You no longer need to be a mathematical genius to run a reliability or risk analysis.
                                                                                                                          - Ola Bckstrm (2021)1 SO PRA CAN BE COMPLICATED.
DOES IT HAVE TO BE?
1Ola Bckstrm, The role of digital insight in a safer nuclear industry, Power, January 28, 2021. (Available from:
26  https://www.powermag.com/the-role-of-digital-insight-in-a-safer-nuclear-industry/)
 
It depends. (Tough problems => increased complexity)
* Technically challenging
    - Complex phenomenology
    - Multiple disciplines, roles, perspectives
* Tough decisions (higher-fidelity solutions)
    - high stakes
    - multiple stakeholders
    - multiple risk attributes
    - uneven distribution of risks and benefits
    - large uncertainties                      From Indian Point Emergency Plan (ML15357A005) 27
 
Reducing PRA Complexity Source                        Simplification Strategy                BUT Complex
* Simplify regulated systems/processes
* Beware of simplistic characterizations (e.g.,
phenomenology
* Increase certainty in rarity of off-    gravity never fails => natural circulation normal conditions (facilitates        cooling will always work) screening)
* Remember real-world testing and
* Obtain more empirical data (reducing    maintenance needs => extra bits and pieces, need for sub-modeling)                off normal configurations and procedures
* Improve PRA technology1 to improve
* Remember even simple systems can have focus on whats important              complex behaviors (e.g., dynamic resonances)
Multiple disciplines,          Improved communication                Beware of unintended side effects (e.g., reducing roles, perspectives                                                  diversity through forcing a view)
Tough decision                Reduce stakes (e.g., by reducing
* Recognize some risk metrics (e.g., for problem (driving              potential consequences), enabling        enterprise risk) might be less sensitive to need for high-fidelity lower-fidelity model                              design/operational changes PRA model)
* Recognize technical arguments for reduced concern might not be accepted 1PRA 28          Technology = PRA methods, models, tools, data
 
Internal Risk Communication Challenge
* Principle: the decision maker should be an informed consumer of risk information
* What do the DMs need to know? Is perceived complexity a barrier to effective Adapted from NUREG-2150 communication?
Barriers?
Other Considerations
* Current regulations
* Safety margins
* Defense-in-depth PRA is for my PhDs
* Monitoring Quantitative Qualitative 29
 
Reducing Perceived Complexity Strategy                                                                          BUT Improve training and communication: ensure focus is
* Beware of turning PRA into a black box oracle; DMs on what DMs need to know                                                              need to appreciate (without overemphasizing) limitations and uncertainties
* Ensure NRC has (or has access to) experts who understand and can communicate limitations and uncertainties, especially when addressing novel applications (designs, processes, decision problems)
Improve PRA technology1 to increase focus on whats                                Same as above but ever so much more so important (e.g., analytics-informed automated PRA)
Wait: take advantage of growing societal experience                                Dont wait too long (technology rejection is the result of with and acceptance of analytics (e.g., sports),                                  social processes, established attitudes can be difficult to modeling (e.g., weather), real-world risk scenarios2                              overcome) and trade-offs (e.g., climate change, pandemics) 1PRA  Technology = PRA methods, models, tools, data 2According  to https://www.etymonline.com, the current, common use of scenario (Italian, sketch of the plot of a play) as an imagined 30  situation first occurred in 1960 as a reference to hypothetical nuclear wars.
 
Were Not Alone
* Other industries and other countries perform risk                                  1978 assessments for a wide range of applications (simple to complex). Examples:
    - Chemical process industry
    - NASA                                                                            1985
    - Netherlands (all industries, all hazards)
* Potentially instructive: review of requirements and practices for lower-risk applications 2020 1Oosterscheldedam photo from 31    https://commons.wikimedia.org/wiki/File:Oosterscheldedam_storm_Rens_Jacobs.jpg
 
Example: Layers of Protection Analysis (LOPA)1
* Intention: reduce inconsistency in qualitative assessments without requiring full PRA
 
==Purpose:==
estimate risk (order-of-magnitude frequencies, qualitative consequences), assess adequacy of protection layers
* Adequacy assessed via risk matrix 1See M. Kazarians and K. Busby, Use of simplified risk assessment methodology in the process industry, Proceedings International 32    Conference Probabilistic Safety Assessment and Management (PSAM 14), Los Angeles, CA, September 16-21, 2018.
 
Change Emphasis to Improve Communication?
(And Banish Nightmares?)
The Engineering Story System Familiarization:          Scenario Analysis    Risk-Informed Decision Making
- How do things work?
- How can they fail?
33
 
PRA Simplification: Some Cautionary Notes
* Past NPP PRA simplifications have gravitated to more detailed models
    - RSSMAP/IREP1 => NUREG-1150
    - ASP plant class models => SPAR
* Simplified model results and insights can be harder to interpret and use
    - Reduced scope => unknown importance of out-of-scope contributors
    - Game over conservatism => masking of important contributors
* Better, cheaper, and faster - realistic                                                      Risk Reduction Alternatives (notional) result of learning or wishful thinking?
1RSSMAP  = Reactor Safety Study Methodology Applications Program (4 plants, 1978-1982) 34      IREP = Interim Reliability Evaluation Program (4 plants, 1980-1982)
 
CONCLUDING REMARKS 35
 
The Bottom Line PRA can be complicated You know about conservation of mass,
* Inherent problem complexities energy, etc. Today were going to talk about
    - Systems and phenomenology                                        the Conservation of Difficulty.
    - High-stakes issues
* Coping strategies for problem complexity can introduce technical complexity Hoo boy.
    - Modeling simplifications and math                    Gotta get out
    - Estimation procedures to address sparse data        of this class!
* Multiple disciplines/communities => added complexity but complexity can [sometimes] be reduced
* Simplify problem (e.g., simplify analyzed system, reduce stakes of decision)
* Improve PRA technology (methods, models, tools, data)
* Improve training 36
 
Acknowledgments My views on PRA have, of course, been strongly influenced by my interactions with others. I can truthfully say that Ive learned from all of my colleagues and that Im still digesting some of these lessons. Special acknowledgments go to Professor George Apostolakis (my adviser and mentor in grad school and beyond, who gave me a framework and tools for thinking about PRA and its use); Dr. B. John Garrick (the importance of aiming for the truth, even if unpopular); Professor Norman Rasmussen (the importance of pragmatic engineering approaches even in R&D, theres no such thing as a worst case),
John Stetkar (the basics of practical NPP PRA in the field); Dr. Harold Blackman (the importance and rigor of human factors engineering); Professor Ali Mosleh, Dr. Dennis Bley, and Dr. Robert Budnitz (gracious sounding boards for ideas, wild or otherwise); and Dr. Thomas Wellock (the early history of PRA and what skeptics think about the enterprise). My particular thanks go to Dr. Dana Kelly, gone too soon, for fruitful discussions. I regret that we never got to write the Details Matter paper we were toying with.
37
 
ADDITIONAL SLIDES 38
 
Everyday Risk-Informed Decisions
* Should I
    -  Go for a run in the woods?                                                                                                      Teach me to
    -  Cross the street against the light?                                                                                            ignore that High
    -  Eat that last doughnut?                                                                                                        Wind warning
    -  Click on that emailed link?
    -  Go to the office when Im coughing?
    -  Get vaccinated?
    -  Visit NYC?
* What do I know?1 What are the current conditions?
* What are the risks? The benefits?1
* N.B. Risk is input to decision problem (choice among alternatives), not just FYI 1 And of course: What are the rules? What are the margins? Is there any defense in depth? Can I monitor the outcome(s) to influence future choices?
39
 
Risk information - not always for decision support.
(Sometimes people just want to know.)
MoCo Covid-19 Cases (%)
0.06 0.05 Daily Cases (%)
0.04 0.03 MoCo Dailies %
MoCo 7-Day (%)
0.02 0.01 0
COVID-19 data from: https://coronavirus.maryland.gov/datasets/mdcovid19-casesbycounty 40                  Estimated population for Montgomery County (2020): 1M
 
RIDM: A Changing Environment
* Internal
  - Overall direction (transformation)
  - Initiatives (e.g., Be riskSMART)
* External
  - Risk communication: risk maps, e.g.,
* Tsunami inundation zones (explicit), e.g., https://www.conservation.ca.gov/cgs/tsunami/maps
* Industrial risks (explicit), e.g., https://www.risicokaart.nl/
* Wildfire extent (implicit), e.g., https://inciweb.nwcg.gov/
* COVID-19 extent (implicit), e.g., https://coronavirus.maryland.gov/
  - Explicit representation of uncertainties (e.g., hurricane tracks)
  - Explicit acknowledgment of expert judgment informed by models (e.g., weather forecasting)
  - Tough, widely discussed risk problems (e.g., climate change, COVID-19) 41
 
On Using the Right Tool: Some Cautions
* If all you have is a hammer Event tree/fault tree analysis for a fundamentally continuous process?
* Using the wrong tool might not only be ineffective or inefficient, it might damage the tool Using PRA to prove a facility/process is safe?
42
 
Complexity: In the Eye of the Beholder Analysts/
Users Reviewers Developers
                      ,      0      ,      1,  ,
43
 
Challenges and Whats Important:
In the Eye of the Beholder
* Near-term solutions: heavy
* Fundamental nature of risk problem time/budget pressure                                          (complexity, uncertainty, multiple consequence
* Huge problem size and types and potentially large magnitude, complexity Analysts/                          multiple stakeholders, )
Multiple technical                              Users communities/cultures      Reviewers
* Competing problems with attentional and
* State of technology: Too                                      resource demands much/little diversity, Holes
* Academic contribution Developers
* Nexus between personal/professional and external interests
* Support (especially with declining budgets) 44
 
Increasing Model Completeness (and Confidence)
Information Sources                                        Attitude                                                          it is incumbent upon the
* Hazard analysis tools, e.g.,
* Be open to possibilities                                        new industry and the
    - Failure Modes and Effects
* Use checklists but also search                                  Government to make every Analysis (FMEA)                                            for ways to get in trouble, e.g.,                            effort to recognize every
    - Hazard and Operability Studies                                                                                          possible event or series of
                                                                  - What might prompt operators (HAZOPS)                                                        to operate in an unstable                              events which could result
    - Master Logic Diagrams (MLD)                                      regime? Disable safety                                in the release of unsafe
    - Heat Balance Fault Trees                                        systems?                                              amounts of radioactive
                                                                  - What could cause a complete                              material to the
    - System-Theoretic Accident Model and Processes/Systems-                                    loss of AC and DC power?                              surroundings Theoretic Process Analysis                                  - What could cause coolant (STAMP/STPA)                                                    channel blockage?                                                  - W.F. Libby (1956)1
* Past events                                                    - What could cause removal of
* Other studies                                                        all control rods?
1W. F. Libby (Acting Chairman, AEC) - March 14, 1956 response to Senator Hickenlooper. [See D. Okrent, Reactor Safety, University of 45    Wisconsin Press, 1981. (NRC Technical Library TK9152 .O35, multiple copies)]
 
Harnessing Imagination:
Credible Possibilities Need Support (Causality)
OPERATOR TERMINATES  Possible ISOLATION CONDENSER OPERATION        but ISO-XHE-EOC-TERM plausible?
46
 
Integrator Expert Elicitation  Easy Button General Process Process
: 1) Preparation Design                                                                                                        2) Piloting/Training Group Workshop                                                        3) Interactions (Workshops) a)    Evaluate evidence b)    Develop, defend, and revise judgments Interaction                            Model Data                                              c)    Integrate judgments With                              Structure                                                                  4) Participatory Peer Review Interaction Individual Experts                      Interaction Group Workshop Interaction                          Model          Ground Motion            Uncertainty With                            Parameter            Forecast            Assessment Individual Experts                    Interaction        Interaction            Interaction Integrator Interaction Adapted from: R. J. Budnitz, et al., Recommendations for                          With                  Integration Probabilistic Seismic Hazard Analysis: Guidance on Uncertainty and          Individual Experts Use of Experts, NUREG/CR-6372, 1997.
47
 
Sources of Risk Communication Breakdowns1
* Differences in perception of information
    - Relevance
    - Consistency with prior beliefs
* Lack of understanding of underlying science
* Conflicting agendas
* Failure to listen
* Trust 1J.L. Marble, N. Siu, and K. Coyne, Risk communication within a risk-informed regulatory decision-making environment, International 48  Conference on Probabilistic Safety and Assessment (PSAM 11/ESREL 2012), Helsinki, Finland, June 25-29, 2012 (ADAMS ML120480139).
Listed causes are for breakdowns between risk managers and the public, but appear to be relevant to internal risk communication as well.
 
Bowtie Diagrams:
Different Visualization => Different Insights? Decisions?
From W. Nelson, How Things Fail - e.g. Deepwater Horizon and Fukushima - and Occasionally Succeed, presentation to U.S.
49  Nuclear Regulatory Commission, Det Norske Veritas AS, November 2, 2011.}}

Revision as of 11:36, 8 September 2021

RES Seminar Part 1 - Nightmares
ML21138A793
Person / Time
Issue date: 05/13/2021
From: Nathan Siu
NRC/RES/DRA
To:
Siu, Nathan - 301 415 0744
Shared Package
ML21138A647 List:
References
Download: ML21138A793 (49)
v • d • e


Text

Retrieved from "https://kanterella.com/w/index.php?title=ML21138A793&oldid=3282362"