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2026 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
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NRC commissioners talk reforms, roles at Day 1 of RIC 2026
Even a last-minute cancelation from Department of Energy Secretary Chris Wright could not derail the optimism permeating day 1 of the Nuclear Regulatory Commission’s annual Regulatory Information Conference (RIC).
The optimistic theme came up several times during the morning plenary sessions that highlighted Tuesday’s agenda. The NRC commissioners who spoke said the optimism was a result of the “nuclear renaissance” they are encountering that feels different from past nuclear-related revivals that didn’t materialize.
M. Gobien, R. Guo (NWMO)
Proceedings | 16th International High-Level Radioactive Waste Management Conference (IHLRWM 2017) | Charlotte, NC, April 9-13, 2017 | Pages 255-264
The Nuclear Waste Management Organization (NWMO) is responsible for the implementation of Adaptive Phased Management, the federally-approved plan for the safe long-term management of Canada’s used nuclear fuel. Under this plan, used nuclear fuel will ultimately be placed within a deep geological repository in a suitable host rock formation.
While a site has not yet been identified, NWMO conducts safety assessment studies for hypothetical sites to inform its siting and design programs. This paper presents an overview of a probabilistic assessment completed in support of the postclosure safety assessment evaluating the most recent container and placement concept for a hypothetical crystalline rock geosphere.
The probabilistic assessment consists of Monte Carlo simulations carried out using the system model SYVAC3-CC4 in which the full range of possible parameter values for the hundreds of distributed input parameters were explored. Unlike the deterministic assessment which test specific variations of the conservative Base Case, the probabilistic assessment uses the results from thousands of simulations and allows one to draw conclusions about model sensitivity as well as test the effects of inherent variability in the underlying model data.