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2022 ANS Annual Meeting
June 12–16, 2022
Anaheim, CA|Anaheim Hilton
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DOE extends application deadline for nuclear credit program
The Department of Energy today announced an extension to its deadline for applications and sealed bid submissions under the $6 billion Civil Nuclear Credit (CNC) Program, launched earlier this year.
According to the DOE, owners and operators of nuclear power reactors most at risk of premature retirement due to economic difficulties have 47 more days to submit applications for certification and sealed bids for credits. The deadline for the first CNC award cycle, originally set for tomorrow, is now 11:59 p.m. MDT on July 5.
Y. Richet, G. Caplin, J. Crevel, D. Ginsbourger, V. Picheny
Nuclear Science and Engineering | Volume 175 | Number 1 | September 2013 | Pages 1-18
Technical Paper | dx.doi.org/10.13182/NSE11-116
Articles are hosted by Taylor and Francis Online.
Nuclear criticality safety assessment often requires groupwise Monte Carlo simulations of k-effective in order to check subcriticality of the system of interest. A typical task to be performed by safety assessors is hence to find the worst combination of input parameters of the criticality Monte Carlo code (i.e., leading to maximum reactivity) over the whole operating range. Then, checking subcriticality can be done by solving a maximization problem where the input-output map defined by the Monte Carlo code expectation (or an upper quantile) stands for the objective function or “parametric” model. This straightforward view of criticality parametric calculations complies with recent works in Design of Computer Experiments, an active research field in applied statistics. This framework provides a robust support to enhance and consolidate good practices in criticality safety assessment. Indeed, supplementing the standard “expert-driven” assessment by a suitable optimization algorithm may be helpful to increase the reliability of the whole process and the robustness of its conclusions. Such a new safety practice is intended to rely on both well-suited mathematical tools (compliant optimization algorithms) and computing infrastructure (a flexible grid-computing environment).