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2026 Nuclear Energy Conference & Expo (NECX)
August 24–27, 2026
Dallas, TX|Hilton Anatole
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Mark Peters: Building on a strong foundation
Summer at the American Nuclear Society carries with it a sense of renewed momentum as the incoming president takes office and starts making plans for the year ahead. This has been particularly true in the last few years, as nuclear energy moves into a new era marked by broader public interest, stronger policy support, and a growing sense of possibility across the field. Mark Peters, the Society’s 72nd president, shares that optimism—and he is focused on turning it into results.
Nikolai Ryzhov, Valentina Svitelman, Kiselev Arkadi, Yudina Tatiana, Saveleva Elena
Nuclear Technology | Volume 212 | Number 8 | August 2026 | Pages 2067-2076
Research Article | doi.org/10.1080/00295450.2025.2521588
Articles are hosted by Taylor and Francis Online.
In the uncertainty analysis of severe accidents, which is becoming an integral part of the nuclear power plant safety assessment, the results of the Monte Carlo transformation of uncertain input parameter probability density functions may, in some cases, reveal several well-differentiated groups with significantly different consequences. Within the framework of The Nuclear Safety Institute (IBRAE) IBRAE’s participation in the International Atomic Energy Agency’s coordinated research project I31033, an uncertainty analysis of the SOCRAT code simulation of the QUENCH-06 experiment was performed. The experiment was conducted with a pressurized water reactor bundle under conditions that are expected during a severe accident terminated by reflooding the uncovered core.
Consideration of measurement uncertainties in the test simulation revealed a cliff edge effect that manifested itself in the far stronger degradation of the assembly with extensive melt formation. To supplement an earlier finding, sensitivity investigations were performed, which are reported in this paper. A new analysis was conducted using the previously obtained computational data and the reliability sensitivity index based on model output classification, which is widely used in system reliability analyses. The results showed that the occurrence of the cliff edge effect was mostly determined by the uncertainty of the electric power measurement. The obtained results were consistent with the physical understanding of the processes occurring in the test facility.