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Division Spotlight
Fuel Cycle & Waste Management
Devoted to all aspects of the nuclear fuel cycle including waste management, worldwide. Division specific areas of interest and involvement include uranium conversion and enrichment; fuel fabrication, management (in-core and ex-core) and recycle; transportation; safeguards; high-level, low-level and mixed waste management and disposal; public policy and program management; decontamination and decommissioning environmental restoration; and excess weapons materials disposition.
Meeting Spotlight
2025 ANS Annual Conference
June 15–18, 2025
Chicago, IL|Chicago Marriott 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
NC State, SRNS partner to attract young talent
North Carolina State University and Savannah River Nuclear Solutions (SRNS) have joined forces to address the ongoing need for specialists in nuclear and criticality safety engineering (N&CSE) at the Department of Energy’s Savannah River Site, near Aiken, S.C.
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.