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The mission of the Decommissioning and Environmental Sciences (DES) Division is to promote the development and use of those skills and technologies associated with the use of nuclear energy and the optimal management and stewardship of the environment, sustainable development, decommissioning, remediation, reutilization, and long-term surveillance and maintenance of nuclear-related installations, and sites. The target audience for this effort is the membership of the Division, the Society, and the public at large.
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Latest News
G7 pledges support for nuclear at Italy meeting
The Group of Seven (G7) recommitted its support for nuclear energy in the countries that opt to use it at a Ministerial Meeting on Climate in Italy last month.
In a statement following the April meeting, the group committed to support multilateral efforts to strengthen the resilience of nuclear supply chains, referencing the goal set by 25 countries during last year’s COP28 climate conference in Dubai to triple global nuclear generating capacity by 2050.
Diego Mandelli, Andrea Alfonsi, Congjian Wang, Zhegang Ma, Carlo Parisi, Tunc Aldemir, Curtis Smith, Robert Youngblood
Nuclear Technology | Volume 207 | Number 3 | March 2021 | Pages 363-375
Technical Paper | doi.org/10.1080/00295450.2020.1776030
Articles are hosted by Taylor and Francis Online.
A new generation of dynamic methods has started receiving attention for nuclear reactor probabilistic risk assessment (PRA). These methods, which are commonly referred to as dynamic PRA (DPRA) methodologies, directly employ system simulators to evaluate the impact of timing and sequencing of events (e.g., failure of components) on accident progression. Compared to classical PRA (CPRA) methods, which are based on static Boolean logic structures such as fault trees and event trees (ETs), DPRA methods can provide valuable insights from an accident management perspective. However, as of today this class of methods has received limited attention in practical applications. One factor is DPRA research and development has progressed mostly as an alternative to state-of-practice CPRA methods (i.e., disconnected from currently employed PRA methods). This disconnect is addressed in this paper by presenting several algorithms that can be employed to bridge the gap between CPRA and DPRA. First, algorithms designed to identify differences between CPRA and DPRA results are presented. The identification process compares the CPRA ET sequence or the minimal cut sets (MCSs) obtained by CPRA with the set of transients simulated by the DPRA. If inconsistencies are observed, solutions are provided to incorporate these differences back into the CPRA by employing DPRA to inform existing CPRA. We performed this incorporation either probabilistically (e.g., by updating MCS probability) or topologically (by adding new branching conditions or sequences in the ET).