A methodology, based on the multiattribute utility analysis, for the assessment of diverse fuel cycles for proliferation resistance was developed. This methodology is intended to allow for the assessment of the effectiveness of safeguards implementation at facilities within a large-scale fuel cycle and allow for the ability to choose technologies based in part on their effectiveness to deter the proliferation of nuclear materials. Fuel cycle facilities under consideration include nuclear reactors, reprocessing facilities, fuel storage facilities, enrichment plants, fuel fabrication plants, uranium conversion plants, and uranium mining and milling operations. The method uses a series of attributes (for example, Department of Energy attractiveness level, weight fraction of even Pu isotopes, measurement uncertainty, etc.) to determine a proliferation resistance measure for each step in a process flow sheet. Each of the attributes has a weighting that determines its importance in the overall assessment. Each attribute also has an associated utility function derived from both expert knowledge and physical characteristics that relates changes in the value of the attribute to its overall effect on the proliferation resistance measure. A method for aggregating proliferation resistance values for each process in a flow sheet into an overall nuclear security measure for the complete-cycle was also developed. This method is focused on preventing host nation diversion; however, a similar technique could be used to analyze the risk due to theft by an insider or outsider. This methodology has been applied successfully for example fuel cycles to demonstrate its viability as an assessment methodology and its capability in discriminating diverse fuel cycle options.

Proliferation Resistance Assessment Methodology for Nuclear Fuel Cycles

Denaturing fissile materials

On common-cause failures—Bibliography

Load-capacity (stress-strength) interference theory is used to model the time-dependent behavior of a 1-out-of-2: G redundant system and to examine common-mode failures. For single units subjected to Poisson distributed load arrivals, the random failures, infant mortality, and aging are associated with load variability, capacity variability, and capacity deterioration, respectively. This paper extends the analysis to a redundant system by using a Markov model to treat Poisson distributed loads arriving at units simultaneously. Loss of s-independence of the unit failures is analyzed with joint PDFs of load and capacity. In the rare-event approximation, the degree of redundancy loss is characterized by expressing the coefficient in the beta-factor method in terms of the load and capacity distributions.

A load-capacity interference model for common-mode failures in 1-out-of-2: G systems

Uncertainty propagation in fault-tree analyses using an exact method of moments

A comparative assessment of the economics and proliferation resistance of advanced nuclear energy systems

A comparison of methods for calculating system unavailability due to common cause failures: The beta factor and multiple dependent failure fraction methods

Error transmission in large complex fault trees using the ESCAF method

Quantification of nuclear diversion risks Promises and problems

Expert judgement concerning human performance in nuclear power plants is quantitatively coupled with actuarial data on such performance in order to derive plant-specific human-error rate probability distributions. The coupling procedure consists of a two-stage application of Bayes' theorem to information which is grouped by type. The first information type contains expert judgement concerning human performance at nuclear power plants in general. Data collected on human performance at a group of similar plants forms the second information type. The third information type consists of data on human performance in a specific plant which has the same characteristics as the group members. The first and second information types are coupled in the first application of Bayes' theorem to derive a probability distribution for population performance. This distribution is then combined with the third information type in a second application of Bayes' theorem to determine a plant-specific human-error rate probability distribution. The two-stage Bayesian procedure thus provides a means to quantitatively couple sparse data with expert judgement in order to obtain a human performance probability distribution based upon available information. Example calculations for a group of like reactors are also given.

Carolyn D. Heising

Papers

A comparative assessment of the economics and proliferation resistance of advanced nuclear energy systems

A comparison of methods for calculating system unavailability due to common cause failures: The beta factor and multiple dependent failure fraction methods

Error transmission in large complex fault trees using the ESCAF method

Quantification of nuclear diversion risks Promises and problems

Plant specification of generic human error data through a two-stage bayesian approach