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 Nuclear Energy Conference & Expo (NECX)
August 24–27, 2026
Dallas, TX|Hilton Anatole
Latest Magazine Issues
Jul 2026
Jan 2026
2026
Latest Journal Issues
Nuclear Science and Engineering
August 2026
Nuclear Technology
Fusion Science and Technology
Latest News
Savannah River Site completes concrete work for Saltstone Disposal Unit 11
The Savannah River Site has completed all concrete construction on its “mega-size” Saltstone Disposal Unit (SDU) 11 at the Saltstone Disposal Facility in Aiken, S.C. The several SDUs at the site are designed to provide safe, permanent storage for decontaminated salt solution from the Salt Waste Processing Facility (SWPF) as production is ramped up. The SDUs are crucial components of SRS’s liquid waste program, allowing the site to meet the cleanup responsibilities of the Department of Energy’s Office of Environmental Management.
Pietro Maccari, Andrea Bersano, Fabrizio Gabrielli, Fulvio Mascari
Nuclear Technology | Volume 212 | Number 8 | August 2026 | Pages 1906-1920
Research Article | doi.org/10.1080/00295450.2024.2430128
Articles are hosted by Taylor and Francis Online.
Severe accident (SA) codes and their core degradation models have to deal with strongly nonlinear and discontinuous phenomena. In the application of uncertainty quantification to SA simulations, the combination of such phenomena may lead to a strong increase in the uncertainty propagated through the simulation, as well as to the chaotic behavior of the output variables. In this framework, the application of the limit surface search method of the RAVEN tool is proposed for a case where cliff-edge effects of SA phenomena determine a bifurcation of an output figure of merit. The algorithm is based on a predictive method making use of a support vector machine model, and it is applied with the aim of separating those input values that lead to different phenomenologies among the uncertainty calculations. The case study is in regard to the uncertainty analysis of the ASTEC code simulation of the QUENCH6 experimental test conducted in the framework of the International Atomic Energy Agency Coordinated Research Project I31033.