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2026 Nuclear Energy Conference & Expo (NECX)
August 24–27, 2026
Dallas, TX|Hilton Anatole
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Two steps forward for U.K. advanced nuclear
This week, two significant announcements have emerged from the United Kingdom’s advanced reactor sector.
On June 14, Rolls-Royce, the United Kingdom National Nuclear Laboratory, and the Japan Atomic Energy Agency announced that they had signed two trilateral memorandums of cooperation to collaborate on “advanced modular reactor (AMR) technology, specifically high-temperature gas-cooled reactors (HTGR), and the coated particle fuel these reactors will use.”
Separately, on June 16, Bellevue, Wash.–based TerraPower announced that its Natrium reactor design has been formally submitted for U.K. regulatory review. The company also announced the formation of a new subsidiary, TerraPower UK Ltd.
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.