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Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Latest News
ANS names 2026 Congressional Fellows
Kasper
Hayes
The American Nuclear Society has officially selected two of its members to serve as its 2026 Glenn T. Seaborg Congressional Science and Engineering Fellows. Alyssa Hayes and Benjamin Kasper will help the Society fulfill its strategic goal of enhancing nuclear policy by working in the halls of Congress, either in a congressional member’s personal office or with a committee, starting next January.
“The Congressional Fellowship program has put ANS in a unique position to provide significant technical assistance to Congress on nuclear science, energy, and technology, with great results,” said Congressional Fellowship Special Committee chair Harsh Desai, himself a former Congressional Fellow. “This once-in-a-lifetime professional development opportunity will allow them to learn the art of policymaking and potentially pursue it as part of their careers beyond the fellowship.”
Yih-Tsuen Wu, A. Berge Gureghian, Budhi Sagar, Richard B. Codell
Nuclear Technology | Volume 104 | Number 2 | November 1993 | Pages 297-308
Technical Paper | Special Issue on Waste Management / Radioactive Waste Management | doi.org/10.13182/NT93-A34891
Articles are hosted by Taylor and Francis Online.
An uncertainty and probabilistic sensitivity study of a hypothetical underground high-level waste (HLW) repository intersected by a vertical fracture or fault and under saturated conditions is presented. Several recently developed probabilistic methods, including the advanced mean value method and the adaptive importance sampling method, are applied to a previously developed one-dimensional analytical model. These probabilistic methods are based on a limit-state formulation and provide an effective means of computing performance probability distribution and probabilitybased random parameter sensitivities. A numerical example related to the transport of 237Np in a system of layered fractured rock is used to illustrate the application of these probabilistic methods for efficient uncertainty and probabilistic sensitivity analyses.