ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Explore membership for yourself or for your organization.
Conference Spotlight
2026 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
Latest Magazine Issues
Feb 2026
Jul 2025
Latest Journal Issues
Nuclear Science and Engineering
March 2026
Nuclear Technology
February 2026
Fusion Science and Technology
January 2026
Latest News
Mirion announces appointments
Mirion Technologies has announced three senior leadership appointments designed to support its global nuclear and medical businesses while advancing a company-wide digital and AI strategy. The leadership changes come as Mirion seeks to advance innovation and maintain strong performance in nuclear energy, radiation safety, and medical applications.
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.