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Division Spotlight
Decommissioning & Environmental Sciences
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.
Meeting Spotlight
2023 ANS Annual Meeting
June 11–14, 2023
Indianapolis, IN|Marriott Indianapolis Downtown
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Latest News
Destruction of Ukrainian dam threatens Zaporizhzhia
A Soviet-era dam downstream from the Zaporizhzhia nuclear power plant in southeastern Ukraine collapsed last evening, causing the water level of the Kakhovka Reservoir north of the dam to drop and raising new concerns over the already jeopardized safety of the Russian-occupied nuclear facility, Europe’s largest. The reservoir supplies water for, among other things, Zaporizhzhia’s cooling systems.
D. Pun-Quach, P. Sermer, F. M. Hoppe, O. Nainer, B. Phan
Nuclear Technology | Volume 181 | Number 1 | January 2013 | Pages 170-183
Technical Paper | Special Issue on the 14th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-14) / Reactor Safety | doi.org/10.13182/NT13-A15765
Articles are hosted by Taylor and Francis Online.
This paper presents a best estimate plus uncertainty (BEPU) methodology applied to dryout, or critical channel power (CCP), modeling based on a Monte Carlo approach. This method involves the identification of the sources of uncertainty and the development of error models for the characterization and separation of epistemic and aleatory uncertainties associated with the CCP parameter. Furthermore, the proposed method facilitates the use of actual operational data leading to improvements over traditional methods, such as sensitivity analysis, which assume parametric models that may not accurately capture the possible complex statistical structures in the system input and responses.