Appendix A
A Probabilistic Risk Assessment Perspective of QMU

B. John Garrick, Committee Member

PURPOSE

It is the purpose of this appendix to consider if the several decades of experience with the application of probabilistic risk assessment (PRA) (Garrick, 2008), especially with respect to nuclear power plant applications, involve methods that might complement or benefit the QMU methodology. The quantification of margins and uncertainties (QMU) methodology refers to the methods and data used by the national security laboratories to predict nuclear weapons performance, including reliability, safety, and security. Both communities, PRA and QMU, have similar challenges. They are being asked to quantify performance measures of complex systems with very limited experience and testing information on the primary events of interest. The quantification of the uncertainties involved to establish margins of performance is the major challenge in both cases. Of course the systems of the two communities are very different and require system-specific modeling methods. To date the emphasis in the QMU effort has been on a reliability prediction process, not yet the important performance measures of safety and security. PRA focuses on what can go wrong with a system and thus could be an ideal method for assessing the safety and security of nuclear weapon systems.

NOTE: This Appendix was authored by an individual committee member. It is not part of the consensus report. The appendix provides a description of PRA and probability of frequency concepts that are discussed in the report.



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Appendix A A Probabilistic Risk Assessment Perspective of QMU B. John Garrick, Committee Member PuRPOSE It is the purpose of this appendix to consider if the several decades of experience with the application of probabilistic risk assessment (PRA) (Garrick, 2008), especially with respect to nuclear power plant appli- cations, involve methods that might complement or benefit the QMU methodology. The quantification of margins and uncertainties (QMU) methodology refers to the methods and data used by the national security laboratories to predict nuclear weapons performance, including reliabil- ity, safety, and security. Both communities, PRA and QMU, have similar challenges. They are being asked to quantify performance measures of complex systems with very limited experience and testing information on the primary events of interest. The quantification of the uncertainties involved to establish margins of performance is the major challenge in both cases. Of course the systems of the two communities are very differ- ent and require system-specific modeling methods. To date the emphasis in the QMU effort has been on a reliability prediction process, not yet the important performance measures of safety and security. PRA focuses on what can go wrong with a system and thus could be an ideal method for assessing the safety and security of nuclear weapon systems. NOTE: This Appendix was authored by an individual committee member. It is not part of the consensus report. The appendix provides a description of PRA and probability of frequency concepts that are discussed in the report. 

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 EvALuATiON Of qMu METhODOLOGy The approach taken in this review is to highlight the PRA method of quantification, comment on applying PRA to weapon performance assess- ment, discuss possible links and differences between QMU as currently used and PRA, and to identify possible PRA enhancements of QMU. The QMU approach itself is covered elsewhere in this report. THE PRA APPROACH TO QuANTIFICATION The PRA approach highlighted is based on the framework of the trip- let definition of risk (Kaplan and Garrick, 1981): R = {}c, where R denotes the risk attendant on the system or activity of interest. On the right, Si denotes the ith risk scenario (a description of something that can go wrong). Li denotes the likelihood of that scenario happen- ing and Xi denotes the consequences of that scenario if it does happen. The angle brackets enclose the risk triplets, the curly brackets { } are mathspeak for “the set of,” and the subscript c denotes “complete,” mean- ing that all of the scenarios, or at least all of the important ones, must be included in the set. The body of methods used to identify the scenarios (Si) constitutes the “Theory of Scenario Structuring.” Quantifying the L i and the Xi is based on the available evidence using Bayes’ theorem, illus- trated later. In accordance with this set of triplets definition of risk, the actual quantification of risk consists of answering the following three questions: 1. What can go wrong? (Si) 2. How likely is that to happen? (Li) 3. What are the consequences if it does happen? (Xi ) The first question is answered by describing a structured, organized, and complete set of possible risk scenarios. As above, we denote these sce- narios by Si. The second question requires us to calculate the “likelihoods,” Li , of each of the scenarios, Si. Each such likelihood, Li, is expressed as a “frequency,” a “probability,” or a “probability of frequency” curve (more about this later). The third question is answered by describing the “damage states” or “end states” (denoted Xi ) resulting from these risk scenarios. These damage states are also, in general, uncertain. Therefore these uncertain- ties must also be quantified, as part of the quantitative risk assessment process. Indeed, it is part of the quantitative risk assessment philoso-

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 APPENDiX A phy to quantify all the uncertainties in all the parameters in the risk assessment. Some authors have added other questions to the above definition such as What are the uncertainties? and What corrective actions should be taken? The uncertainty question is embedded in the interpretation of “likelihood,” as noted later. The question about corrective actions is interpreted as a matter of decision analysis and risk management, not risk assessment per se. Therefore it is not considered a fundamental prop- erty of this definition of risk. Risk assessment does become involved to determine the impact of the corrective actions on the “new risk” of the affected systems. Using the triplet definition of risk as the overarching framework, the following steps generally represent the PRA process: Step . Define the system being analyzed in terms of what constitutes normal or successful operation to serve as a baseline for departures from normal operation. Step . Identify and characterize what constitutes an undesirable out- come of the system. Examples are failure to perform as designed, damage to the system, and a catastrophic accident. Step . Develop “What can go wrong?” scenarios to establish levels of damage and consequences while identifying points of vulnerability. Step . Quantify the likelihoods of the different scenarios and their attendant levels of damage based on the totality of relevant evidence available. Step . Assemble the scenarios according to damage levels and cast the results into the appropriate risk curves and risk priorities. Step . Interpret the results to guide the risk-management process. These six steps tend to collapse into the three general analytical pro- cesses illustrated in Figure A-1—a system analysis, a threat assessment, and a vulnerability assessment. That is, a PRA basically involves three main processes: (1) a system analysis that defines the system in terms of how it operates and what constitutes success, (2) an initiating event and initial condition assessment that quantifies the threats to the system, and (3) a vulnerability assessment that quantifies the resulting risk scenarios and different consequences or damage states of the system, given the pos- sible threats to the system. A valuable attribute of the triplet approach is that it can track multiple end states in a common framework. In Figure A-1 the system analysis is denoted as the “system states for successful operation.” The second part of the process requires a deter- mination of the threats to any part of the total system—that is, events

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 EvALuATiON Of qMu METhODOLOGy FIGURE A-1 The concept of an integrated threat and vulnerability risk assessment. that could trigger or initiate a disturbance to an otherwise successfully operating system. The third part of the process structures the course and Figure A-1 consequence of events (scenarios) that could emanate from specific initiat- Bitmapped, low-res ing events or initial conditions. A number of thought processes and analytical concepts are employed to carry out the three processes conceptualized in Figure A-1. They in- volve an interpretation of “likelihood,” a definition of “probability,” the algorithms of deductive and inductive reasoning, the processing of the evidence, the quantification and propagation of uncertainties, and the assembly of the results into an interpretable form. Some of the more important concepts are highlighted. Three explicit and quantitative interpretations of likelihood are “fre- quency,” “probability,” and “probability of frequency.” • frequency. If the scenario is recurrent—that is, if it happens repeat- edly—then the question How frequently? can be asked and the answer can be expressed in occurrences per day, per year, per trial, per demand, etc. • Probability (credibility). If the scenario is not recurrent—if it hap- pens either once or not at all—then its likelihood can be quantified in terms of probability. “Probability” is taken to be synonymous

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 APPENDiX A with “credibility.” Credibility is a scale invented to quantitatively measure the degree of believability of a hypothesis, in the same way that scales were invented to measure distance, weight, tem- perature, etc. Thus, in this usage, probability is the degree of credibility of the hypothesis in question based on the totality of relevant evidence available. • Probability of frequency. If the scenario is recurrent (like a hurricane, for example) and therefore has a frequency whose numerical value is not, however, fully known, and if there is some evidence relevant to that numerical value, then Bayes’ theorem (as the fundamental principle governing the process of making inference from evidence) can be used to develop a probability curve over a frequency axis. This “probability of frequency” interpretation of likelihood is often the most informative, and thus is the preferred way of capturing/quantifying the state of knowledge about the likelihood of a specific scenario. Having proposed a definition of probability, it is of interest to note that it emerges also from what some call the “subjectivist” view of prob- ability, best expressed by the physicist E.T. Jaynes (2003): A probability assignment is ‘subjective’ in the sense that it describes a state of knowledge rather than any property of the ‘real’ world, but is ‘objective’ in the sense that it is independent of the personality of the user. Two rational beings faced with the same total background of knowl- edge must assign the same probabilities. The central idea of Jaynes is to bypass opinions and seek out the underlying evidence for the opinions, which thereby become more objec- tive and less subjective. Recalling the interpretation of probability as credibility, in this situa- tion, probability is a positive number ranging from zero to one and obeys Bayes’ theorem. Thus, if we write p(H|E) to denote the credibility of hypothesis H, given evidence E, then p(E|H) p(H|E) = p(H) , p(E) which is Bayes’ theorem. It tells us how the credibility of hypothesis H changes when new evidence, E, occurs. Bayes’ theorem is a simple two- step derivation from the product rules of probability and plausible rea- soning. This theorem has a long and bitterly controversial history but in recent years has become widely understood and accepted. A central feature of probabilistic risk assessment is making uncer-

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 EvALuATiON Of qMu METhODOLOGy tainty an inherent part of the analysis. Uncertainty exists, to varying degrees, in all the parameters that are used to describe or measure risk. Of course there are sources of uncertainty other than parameter uncertainty, such as uncertainty about whether a particular phenomenon is being cor- rectly modeled. A common approach to assessing modeling uncertainty is to apply different models to the same calculation in an attempt to expose modeling variability. Adjustments are made to the model to increase confidence in the results. The lack of confidence resulting from such an analysis can be a basis for assigning a modeling uncertainty component to parameter uncertainty in order to better characterize the total uncertainty of the analysis. In PRA, parameter uncertainties are quantified by plotting probability curves against the possible values of these parameters. These probability curves are obtained using Bayes’ theorem. Before the risk scenarios themselves can be quantified, the initiat- ing events (IE) or the initial conditions (IC) of the risk scenarios must be identified and quantified.1 The relationship between the initial states (IEs and ICs), the system being impacted, and the vulnerability of the system being impacted is illustrated in Figure A-1. A deductive logic model—that is, a fault tree or master logic diagram— is developed for each initiating event of a screened set. The structure of the logic model is to deduce from the “top events”—that is, the selected set of hypothetical IEs or ICs—the intervening events down to the point of “basic events.” A “basic event” can be thought of as the initial input point for a deductive logic model of the failure paths of a system. For the case of accident risk, a basic event might be fundamental information on the behavior of structures, components, and equipment. For the case of a natural system such as a nuclear waste disposal site, a basic event could be a change in the ICs having to do with climate brought about by green- house gases. For the case of terrorism risk, the basic event relates to the intentions of the terrorist—that is, the decision to launch an attack. For the case of a nuclear weapon system, either environments or conditions could impact weapon performance. The intervening events of the master logic diagram for terrorism risk are representations of the planning, train- ing, logistics, resources, activities, and capabilities of the terrorists. The intervening events of the master logic diagram for accident risk are the processes and activities that lead to the failure of structures, components, and equipment. The intervening events of the ICs for a nuclear waste disposal site could be factors that influence climate, and the intervening 1 Both IE and IC terminology are used, since for some systems such as the risk of a nuclear waste repository the issue is not so much an initiating event as it is a set of initial conditions such as annual rainfall.

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 APPENDiX A events for a nuclear weapon system could be environments that impact weapon yield. Once the initiating events are quantified, the resulting scenarios could be structured to the undesired consequences or end states. The actual quantification of the risk scenarios is done with the aid of event trees similar to the one in Figure A-2. An event tree is a diagram that traces the response of a system to an initiating event, such as a terrorist attack, to different possible end points or outcomes (consequences). A single path through the event tree is called a “scenario” or an “event sequence.” The terms are sometimes used interchangeably. The event tree displays the systems, equipment, human actions, procedures, processes, and so on that can affect the consequences of an initiating event depending on the suc- cess or failure of intervening actions. In Figure A-2 boxes with the letters A, B, C, and D represent these intervening actions. The general conven- tion is that if a defensive action is successful, the scenario is mitigated. If the action is unsuccessful, then the effect of the initiating event continues as a downward line from the branch point as shown in Figure A-2. For accident risk, an example of a mitigating system might be a source of emergency power. For terrorism risk, an action that could mitigate the hijacking of a commercial airliner to use it as a weapon to crash into a football stadium would be a remote takeover of the airplane by ground control. For a natural system, a mitigating feature might be an engineered barrier, and for a nuclear weapon a mitigating system might be the shield- ing of external radiation. Each branch point in the event tree has a probability associated with it. It should be noted that the diagram shown in Figure A-2 shows only two branches (e.g., success or failure) from each top event. However, a top event can have multiple branches to account for different degrees of degradation of a system. These branch points have associated “split f(A I) I I 1 – f(A I) S=IABCD ϕ(S) = ϕ(I) f (A I) f (B IA) f (C IAB) f (D IABC) FIGURE A-2 Quantification of a scenario using an event tree.

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 EvALuATiON Of qMu METhODOLOGy fractions” that must be quantified based on the available evidence. The process involves writing an equation for each scenario (event sequence) of interest. For example, the path through the event tree that has been highlighted in Figure A-2 could be a scenario that we wish to quantify. The first step is to write a Boolean equation for the highlighted path. If we denote the scenario by the letter S, we have the following equation, S = I AB CD , where the bars over the letters indicate that the event in the box did not perform its intended function. The next step is to convert the Boolean equation into a numerical calculation of the frequency of the scenario. Let- ting ϕ stand for frequency and adopting the split fraction notation, f(…), of Figure A-2 gives the following equation for calculating the frequency of the highlighted scenario: ϕ(S) = ϕ(I)f (A|I)f ( B|IA)f ( C IAB)f ( D|IABC) | The remaining step is to communicate the uncertainties in the fre- quencies with the appropriate probability distributions. This is done using Bayes’ theorem to process the elemental parameters (Figure A-3). The “probability of frequency” of the individual scenarios is obtained by convoluting the elemental parameters in accordance with the above equation. Once the scenarios have been quantified, the results take the form shown in Figure A-4. Each scenario has a probability-of-frequency curve in the form of a probability density function quantifying its likelihood of occurrence. The total area under the curve represents a probability S=IABCD ϕ(S) = ϕ(I) f (A I) f (B IA) f (C IAB) f (D IABC) ’ ’ ’ FIGURE A-3 Bayes’ theorem used to process parameters. Figure A-3 Bitmapped, low-res

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 APPENDiX A of 1. The fractional area between two values of ϕ represents the confi- dence—that is, the probability—that ϕ has the values over that interval (see below). Figure A-4 shows the curve for a single scenario or a set of scenarios leading to a single consequence. Showing different levels of damage, such as the risk of varying numbers of injuries or fatalities, requires a different type of presentation. The most common form is the classical risk curve, also known as the frequency-of-exceedance curve or the even more esoteric label, the complementary cumulative distribution function. This curve is constructed by ordering the scenarios by increasing levels of damage and cumulating the probabilities from the bottom up in the ordered set against the different damage levels. Plotting the results on log-log paper generates curves such as those shown in Figure A-5. Suppose P3 in Figure A-5 has the value of 0.95—that is, a probability of 0.95. We can be 95 percent confident that the frequency of an X1 conse- quence or greater is ϕ1. The family of curves (usually called percentiles) can include as many curves as necessary. The ones most often selected in practice are the 5th, 50th, and 95th percentiles. A popular fourth choice is the mean. A common method of communicating uncertainty in the risk of an event is to present the risk in terms of a confidence interval. To illustrate confidence intervals some notation is added to the above figures, which now become Figures A-6 and A-7. If the area between ϕ1 and ϕ2 of Figure A-6 takes up 90 percent of the area under the curve, we are 90 percent con- fident (the 90 percent confidence interval) that the frequency is between ϕ1 and ϕ2. Figure A-7 can also be read in terms of a confidence interval. Let P1 be 0.05, P3 be 0.95, ϕ1 be one in 1,000, ϕ2 one in 10,000, and X1 be 10,000 fatalities. Because P3 minus P1 is 0.90, we are 90 percent confident that the frequency of an event having 10,000 fatalities or more varies from one every 1,000 years to one every 10,000 years. Although risk measures such as those illustrated in Figures A-6 and A-7 answer two questions—What is the risk? How much confidence is there in the results?—they are not necessarily the most important output Probability (P) Frequency ( ϕ ) FIGURE A-4 Probability of frequency curve. Figure A-4 God type, bitmpped line art

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0 EvALuATiON Of qMu METhODOLOGy Frequency ( ϕ ) ϕ 1 X1 Consequence (X) FIGURE A-5 Risk curves for varying consequences. Figure A-5 Good outside yps of the risk assessment. Often the most important output is the exposure bitmapped art and interior type of the detailed causes of the risks, a critical result needed for effective risk management. The contributors to this risk are buried in the results assembled to generate the curves in Figures A-6 and A-7. Most risk assess- ment software packages contain algorithms for ranking the importance of contributors to the risk. For a Specific Consequence Probability (P) 1 2 Frequency ( ) FIGURE A-6 Probability density. FIGURE A-6

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 APPENDiX A For Varying Consequences ϕ2 P3 P2 ϕ1 P1 X1 Consequence (X) ( FIGURE A-7 Cumulative probability. APPLyINg PRA TO WEAPON A-7 FIGURE PERFORMANCE ASSESSMENT Since the probabilistic risk assessment was developed to apply to any type of risk assessment, it is believed that it could be a framework for assessing the risk in any type of system, natural or engineered, weapon or nonweapon. However, it could not be applied to nuclear weapons without using the whole host of computer codes and analytical pro- cesses that have been developed to support the current efforts for the quantification of margins and uncertainties methodology developed by the national security laboratories. In fact, because of the advanced state of development of the laboratory codes for calculating confidence ratios of performance margins and uncertainties, the most prudent use of the PRA thought process is probably for safety and security issues and ele- ments such as the impact on weapon performance of stockpile storage or other events associated with the stockpile-to-target sequence. In fact, these elements could be the major contributors to the risk of poor weapon performance. In general, PRA methods have been successfully applied to nonwar- head operational elements of the nuclear weapon functional life cycle. This still leaves open the question of how PRA might be used to quantify the risk of less-than-acceptable performance of a nuclear warhead. Pro- viding a full answer to this question is beyond the scope of this appendix, but it is possible to describe the concept. Suppose as a part of an assessment of the risk of a warhead not

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 EvALuATiON Of qMu METhODOLOGy Radiation High Explosive Unboosted Boost Gas Boosted Yield Input Implosion Fission Burn Fission Yi i=1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 FIGURE A-8 Single-stage boosted fission explosive event tree. performing to its specification, consideration is given to the risk of the Figure A-8 weapon’s primary explosion performance being compromised by exter- nal radiation. (More information on this topic is included in Note 11 in the classified Annex.) In particular, an initiating event is defined as the frequency per mission hour of an external radiation pulse of sufficient energy to impact weapon performance. The frequency of such an event would have to be based on multiple sources of evidence, including the state of the technology for defensive systems (which could, for example, come from intelligence reports), the mitigation capability of the weapon system itself, evasive procedures, mission conditions, etc. To be sure there would be uncertainties, which means that the frequency would have to be represented by a probability distribution in “probability of frequency” format. Figure A-8 is a conceptual interpretation of the events that would have to successfully occur for a single-stage boosted fission explosive to perform its intended function. The event tree identifies the possible pathways triggered by the initi- ating event. The end states are a range of primary yields of the different event sequences (scenarios). Of course, the physics of the process will lead to many of the branch points being bypassed and the number of outcome states being reduced. The events may be briefly described as follows: • radiation input. An external radiation source impinged on the weapon system. The event is represented as a frequency per mis-

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 APPENDiX A sion hour of an external radiation pulse of sufficient energy to impact weapon performance. • high explosie implosion. If the radiation fluence impinging on a boosted fission explosive is varied, the performance of the device will vary. • unboosted fission. The degraded performance of the high explo- sive implosion will reduce the criticality of the explosive fission- able material and reduce the amount of fission energy generated before the boost stage. • Boost gas burn. The boost is dependent on a sufficient amount of fission energy to heat and compress the boost gas to thermonu- clear fusion conditions. The number of boost neutrons produced is affected. • Boosted fission. The final yield is determined by the number of boosted fission events. Boosted fission scales with the number of boost neutrons available. • yield. The end states are probability of frequency (POF) distribu- tions of different yields, including the design yield. Having a POF distribution for each of these scenarios sets the stage for developing risk curves for a particular initiating event. The outputs of the event tree are calculated as described in the section “The PRA Approach to Quantification.” The results from the event tree can be assembled into several different forms. One form would be to probabilistically add all the less-than-design-yield POF distributions to achieve the probability density curve for the risk of the primary not reaching its intended yield. This result would be in the form shown in Figure A-4, which character- izes the risk, including uncertainty, of this stage not performing to its specification. A second form, if there are multiple degraded end states to be consid- ered, would be to arrange the end state POF curves of the degraded yields in order of increasing degradation and cumulate them from the bottom to the top in the form of a complementary cumulative distribution function. The result is given in Figure A-5, which quantifies the risk of different degraded yields of single-stage boosted fission explosion with probability, P, as the parameter of the model. A third form of presenting the results would be the POF curve repre- senting the success scenario. These three results represent a comprehensive set of metrics for mea- suring the performance with uncertainty of the primary fission explosion under the specific threat of a single initiator. To complete the risk assess- ment of the single-stage boosted fission explosive requires the consid- eration of all the important risk contributing initiators. Usually that is

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 EvALuATiON Of qMu METhODOLOGy done by creatively defining a relatively small number of initiating event “categories” that represent several individual initiating events. POSSIbLE LINkS bETWEEN QMu AND PRA Among the common challenges to both the QMU and PRA meth- odologies is a convincing treatment of parameter and modeling uncer- tainty. Linking supporting evidence to the PRA and QMU calculations is critical to providing transparency and confidence in uncertainty analyses. Experience indicates that the key to uncertainty analysis is not so much data limitations as it is to have a system in place to capture and process the data and information that are often available but perhaps not easy to retrieve or in the proper form. Experience with nuclear power plant PRAs has shown this many times. For example, the systematic processing of maintenance and operations data has provided a robust database for assessing nuclear plant risk, which was thought to not be possible when PRAs were first implemented. Of course, this is not a database of many of the events of interest such as core melts or large releases of radioactive materials. Fortunately, not many such events have occurred. But, it is an important database for precursor events to these more serious events. If the precursor events where there are data are logically connected to the events of interest by detailed logic models, then the opportunity exists to appropriately propagate the uncertainties to the desired end states. The nuclear weapons field would seem to be in a situation similar to that of the nuclear power field. While there is no actual testing being per- formed on full-scale nuclear weapons, data are being developed through precursor tests and weapons management activities. Nuclear explosive safety teams have been analyzing and observing assembly, disassembly, and repair activities for decades. An examination of this robust experience in nuclear weapons operations would seem to be similar to the experience in nuclear power maintenance and operations, especially with respect to safety and security issues. To be sure, many nuclear explosive safety activities go beyond the nuclear explosive package of nuclear warheads and some data needs of the nuclear explosive package are unique, thus limiting data collection opportunities. Nevertheless, it would appear that opportunities exist for large-scale data collection and processing in the weapons field. It is interesting to observe that both communities have benefited considerably by increased use of Bayesian methods to infer the performance characteristics of their respective systems’ components.

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 APPENDiX A APPARENT DIFFERENCES bETWEEN THE APPROACHES One of the main differences between the two approaches (PRA and QMU), at least from an outsider’s perspective, is the transparency of the performance assessment. The QMU assessments are packaged in a series of highly sophisticated computer codes that have a history of many decades. These codes represent the legacy memory and expert systems of decades of experience in predicting weapon performance. The sophistica- tion of the codes and the matter of security compromises their transpar- ency. However, nuclear power is highly regulated, and the transparency of its safety analysis has always been an inherent requirement of the process. Thus, it is expected that the safety analysis methods for nuclear power plants would make the basic structure and results of the modeling highly visible and accessible. Another difference between the two approaches is that, at present at least, they are trying to answer different questions. The QMU question is currently driven by a reliability perspective and PRA by a risk perspec- tive. Of course, to understand reliability one must know what the risks are and vice versa. But they are different because the emphasis in the models is different. The final form of the results in the QMU approach is a reliability number and the final result in a PRA is the risk of damage and adverse consequences. Both approaches attempt to quantify margins of performance and the uncertainties involved. There will indeed be con- vergence to common goals as QMU begins to address more explicitly such issues as safety, security, the stockpile-to-target sequence, and stockpile aging. POSSIbLE PRA ENHANCEMENTS OF QMu This appendix started out with the goal of identifying possible enhancements of the QMU methodology as a result of the very large experience base in probabilistic risk assessment, especially in the case of nuclear power. Perhaps the biggest contribution that PRA could make to the QMU methodology would be a comprehensive PRA of each basic weapon system. Experience with PRA strongly supports the view that the information and knowledge base created in the course of performing the PRA could contribute to the credibility of the QMU process. Almost every phase of nuclear power plant operation has been favorably affected by PRAs, from maintenance to operating procedures, from outage plan- ning to plant capacity factors, from sound operating practices to recovery and emergency response, and from plant simulation to operator training. It is logical to expect the same would be true for the QMU process, for

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 EvALuATiON Of qMu METhODOLOGy conducting weapon performance assessments, and for carrying out the nuclear explosives safety process. Some of the characteristics of PRA that might enhance the QMU process are (1) explicitness of event sequences (scenarios) leading to degraded performance, (2) ranking of contributors to nonperformance, (3) the probability of frequency concept for presenting results (see earlier discussion), (4) increased emphasis on evidence-based distribution func- tions (as opposed to assumed distributions such as Gaussian), and (5) the actual quantification of the risk of degraded performance. As suggested above, the PRA thought process could very well be the primary vehicle for quantifying the safety and security risk of nuclear weapon systems and of other steps in the nuclear weapon functional life cycle such as the stockpile-to-target sequence and the issue of the aging stockpile and its effect on performance. The PRA framework is compatible with tracking multiple performance measures including safety, military compatibility, and logistics. One final thought about how PRA might enhance the QMU process has to do with the changing of management mindsets about performance metrics. PRA has altered the thinking of nuclear power plant management about the importance of having multiple metrics for measuring risk and performance of complex systems. Maybe the weapons community has to do the same thing with its leadership. A single number for weapons reliability is not a confidence builder in understanding the performance characteristics of something as complicated as a nuclear weapon, where there is a need to expose the uncertainties in the reliability predictions. REFERENCES Garrick, B.J. 2008. quantifying and Controlling Catastrophic risks, Elsevier Press. Jaynes, E.T. 2003. Probability Theory; The Logic of Science, Cambridge University Press. Kaplan, S., and B.J. Garrick. 1981. On the Quantitative Definition of Risk, risk Analysis 1(1): 11-27.