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The mission of the Decommissioning and Environmental Sciences (DES) Division is to promote the development and use of those skills and technologies associated with the use of nuclear energy and the optimal management and stewardship of the environment, sustainable development, decommissioning, remediation, reutilization, and long-term surveillance and maintenance of nuclear-related installations, and sites. The target audience for this effort is the membership of the Division, the Society, and the public at large.
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ANS Student Conference 2025
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Fusion Science and Technology
Latest News
Uncertainty contributes to lowest uranium spot prices in 18 months
A combination of plentiful supply and uncertain demand resulted in spot pricing for uranium closing out March below $64 per pound, with dips down to about $63.50 during mid-March—the lowest futures prices in 18 months, according to tracking by analysis firm Trading Economics. Spot prices have also fallen steadily since the beginning of 2024. Meanwhile, long-term prices have held steady at about $80 per pound at the end of March, according to Canadian front-end uranium mining, milling, and conversion company Cameco.
Ander Gray, Andrew Davis, Edoardo Patelli
Fusion Science and Technology | Volume 77 | Number 7 | November 2021 | Pages 802-812
Technical Paper | doi.org/10.1080/15361055.2021.1895667
Articles are hosted by Taylor and Francis Online.
In this paper we perform nuclear data uncertain propagation with Total Monte Carlo, where the transport simulation is repeated for random evaluations of the data. The Oktavian Iron, Oktavian Nickel, and the Frascati Neutron Generator (FNG) neutron streaming SINBAD benchmarks were evaluated with OpenMC. Gaussian random deviates were drawn from the ENDF/B-VII.1 and TENDL-2017 libraries where the covariances were available. Uncertainty from multiple nuclides was propagated simultaneously assuming inter-nuclide independence. When the individual statistical uncertainty is negligible compared to the data uncertainty, then standard probability theory may be applied. If this is not the case and both need to be considered, we use Imprecise Probabilities (IP) to perform further analysis. We show how uncertain experimental data may be compared to uncertain simulation in the context of IP, and show how an uncertainty-based sensitivity analysis can be performed with IP.