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Latest News
First astatine-labeled compound shipped in the U.S.
The Department of Energy’s National Isotope Development Center (NIDC) on March 31 announced the successful long-distance shipment in the United States of a biologically active compound labeled with the medical radioisotope astatine-211 (At-211). Because previous shipments have included only the “bare” isotope, the NIDC has described the development as “unleashing medical innovation.”
Stephen D. Unwin, Peter P. Lowry, Michael Y. Toyooka
Nuclear Science and Engineering | Volume 171 | Number 1 | May 2012 | Pages 69-77
Technical Paper | doi.org/10.13182/NSE11-18
Articles are hosted by Taylor and Francis Online.
Conventional probabilistic risk assessments (PRAs) are not well suited to addressing long-term reactor operations. Since passive structures and components are among those for which replacement can be least practical, they might be expected to contribute increasingly to risk in an aging plant; yet, passives receive limited treatment in PRAs. Furthermore, PRAs produce only snapshots of risk based on the assumption of time-independent component failure rates. This assumption is unlikely to be valid in aging systems. The treatment of aging passive components in PRA presents challenges. Service data to quantify component reliability models are sparse, and this is exacerbated by the greater data demands of age-dependent reliability models. Another factor is that there can be numerous potential degradation mechanisms associated with the materials and operating environment of a given component. This deepens the data problem since risk-informed management of component aging will demand an understanding of the long-term risk significance of individual degradation mechanisms. In this paper we describe a Bayesian methodology that integrates metrics of materials degradation susceptibility with available plant service data to estimate age-dependent passive component reliabilities. Integration of these models into conventional PRA will provide a basis for materials degradation management informed by predicted long-term operational risk.