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Division Spotlight
Accelerator Applications
The division was organized to promote the advancement of knowledge of the use of particle accelerator technologies for nuclear and other applications. It focuses on production of neutrons and other particles, utilization of these particles for scientific or industrial purposes, such as the production or destruction of radionuclides significant to energy, medicine, defense or other endeavors, as well as imaging and diagnostics.
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
Webinar: MC&A and safety in advanced reactors in focus
Towell
Russell
Prasad
The American Nuclear Society’s Nuclear Nonproliferation Policy Division recently hosted a webinar on updating material control and accounting (MC&A) and security regulations for the evolving field of advanced reactors.
Moderator Shikha Prasad (CEO, Srijan LLC) was joined by two presenters, John Russell and Lester Towell, who looked at how regulations that were historically developed for traditional light water reactors will apply to the next generation of nuclear technology and what changes need to be made.
Technical Session|Panel
Thursday, November 11, 2021|12:40–2:40PM EST
Session Chair:
Michelle Bensi (Univ Maryland)
Session Organizer:
Alternate Chair:
Carol S. Smidts (OSU)
Session Producer:
Zach Berkow
Our fundamental understanding of both external hazards and nuclear power plant (NPP) responses to those hazards has increased in recent years as a result of the expanded use of external hazard probabilistic risk assessment (XHPRA). In parallel, recent operating experience has led to an increased understanding of and appreciation for the ways that external hazard events can affect NPP structures, systems, and components (SSCs) as well as the factors influencing human performance during hazard events. While XHPRA knowledge and experience continue to grow, it remains challenging to represent the spatially and temporally dynamic nature of certain external hazard events within existing PRA modeling tools. Moreover, there is notable variability between hazard groups (e.g., flooding, seismic, and high wind hazards) with respect to the conventions of practice in XHPRA. These differences arise due to the differing XHPRA experience bases across hazard groups as well as fundamental differences in the nature of hazard events and the strategies plants use to respond. As a result, broad uncertainties associated with XHPRA remain. These include uncertainties related to: (1) characterization of the severity and frequency of hazard events; (2) the physical impacts of hazard events on NPP SSCs; (3) event progressions; and (4) the impacts of external hazards on human performance (including FLEX strategies). Further, external hazards can create unique challenges related to site access, evacuation/sheltering, and concurrent/secondary/induced hazards. In existing XHPRA practice, the aforementioned uncertainties and knowledge gaps have typically been addressed via conservative assessments, expert judgment, or simplified models and assumptions. Improvements in model realism can yield important risk insights through the enhancement of plant response procedures. They can also help to expand the utility of XHPRA to assist in evaluating alternative response strategies. There is a need for a risk-informed strategy to identify, characterize, and prioritize drivers of hazard uncertainty. To address this challenge, this panel session will bring together experts in multiple aspects of XHPRA to discuss: • These drivers of uncertainty in XHPRA • Differences in practice (and origins of those differences) among hazard groups • Future directions and potential benefits of efforts to improve model realism and reduce uncertainties
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