A random onset model for degradation of high-reliability systems
- Los Alamos National Laboratory
Both the U. S. Department of Defense (DoD) and Department of Energy (DOE) maintain weapons stockpiles: items like bullets, missiles and bombs that have already been produced and are being stored until needed. Ideally, these stockpiles maintain high reliability over time. To assess reliability, a surveillance program is implemented, where units are periodically removed from the stockpile and tested. The most definitive tests typically destroy the weapons so a given unit is tested only once. Surveillance managers need to decide how many units should be tested, how often they should be tested, what tests should be done, and how the resulting data are used to estimate the stockpile's current and future reliability. These issues are particularly critical from a planning perspective: given what has already been observed and our understanding of the mechanisms of stockpile aging, what is an appropriate and cost-effective surveillance program? Surveillance programs are costly, broad, and deep, especially in the DOE, where the US nuclear weapons surveillance program must 'ensure, through various tests, that the reliability of nuclear weapons is maintained' in the absence of full-system testing (General Accounting Office, 1996). The DOE program consists primarily of three types of tests: nonnuclear flight tests, that involve the actual dropping or launching of a weapon from which the nuclear components have been removed; and nonnuclear and nuclear systems laboratory tests, which detect defects due to aging, manufacturing, and design of the nonnuclear and nuclear portions of the weapons. Fully integrated analysis of the suite of nuclear weapons surveillance data is an ongoing area of research (Wilson et al., 2007). This paper introduces a simple model that captures high-level features of stockpile reliability over time and can be used to answer broad policy questions about surveillance programs. Our intention is to provide a framework that generates tractable answers that integrate expert knowledge and high-level summaries of surveillance data to allow decision-making about appropriate trade-offs between the cost of data and the precision of stockpile reliability estimates.
- Research Organization:
- Los Alamos National Laboratory (LANL)
- Sponsoring Organization:
- DOE
- DOE Contract Number:
- AC52-06NA25396
- OSTI ID:
- 991244
- Report Number(s):
- LA-UR-09-04667; LA-UR-09-4667
- Country of Publication:
- United States
- Language:
- English
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