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Strontium: Supply-and-demand success for the DOE’s Isotope Program
The Department of Energy’s Isotope Program (DOE IP) announced last week that it would end its “active standby” capability for strontium-82 production about two decades after beginning production of the isotope for cardiac diagnostic imaging. The DOE IP is celebrating commercialization of the Sr-82 supply chain as “a success story for both industry and the DOE IP.” Now that the Sr-82 market is commercially viable, the DOE IP and its National Isotope Development Center can “reassign those dedicated radioisotope production capacities to other mission needs”—including Sr-89.
P. E. Labeau, J. M. Izquierdo
Nuclear Science and Engineering | Volume 150 | Number 2 | June 2005 | Pages 115-139
Technical Paper | doi.org/10.13182/NSE05-A2506
Articles are hosted by Taylor and Francis Online.
The theory of probabilistic dynamics (TPD) offers a framework capable of modeling the interaction between the physical evolution of a system in transient conditions and the succession of branchings defining a sequence of events. Nonetheless, the Chapman-Kolmogorov equation, besides being inherently Markovian, assumes instantaneous changes in the system dynamics when a setpoint is crossed. In actuality, a transition between two dynamic evolution regimes of the system is a two-phase process. First, conditions corresponding to the triggering of a transition have to be met; this phase will be referred to as the activation of a "stimulus." Then, a time delay must elapse before the actual occurrence of the event causing the transition to take place. When this delay cannot be neglected and is a random quantity, the general TPD can no longer be used as such. Moreover, these delays are likely to influence the ordering of events in an accident sequence with competing situations, and the process of delineating sequences in the probabilistic safety analysis of a plant might therefore be affected in turn. This paper aims at presenting several extensions of the classical TPD, in which additional modeling capabilities are progressively introduced. A companion paper sketches a discretized approach of these problems.