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Growth beyond megawatts
Hash Hashemianpresident@ans.org
When talking about growth in the nuclear sector, there can be a somewhat myopic focus on increasing capacity from year to year. Certainly, we all feel a degree of excitement when new projects are announced, and such announcements are undoubtedly a reflection of growth in the field, but it’s important to keep in mind that growth in nuclear has many metrics and takes many forms.
Nuclear growth—beyond megawatts—also takes the form of increasing international engagement. That engagement looks like newcomer countries building their nuclear sectors for the first time. It also looks like countries with established nuclear sectors deepening their connections and collaborations. This is one of the reasons I have been focused throughout my presidency on bringing more international members and organizations into the fold of the American Nuclear Society.
Jagjit Singh Matharu, Vidya Devi
Nuclear Science and Engineering | Volume 193 | Number 3 | March 2019 | Pages 314-324
Technical Paper | doi.org/10.1080/00295639.2018.1538280
Articles are hosted by Taylor and Francis Online.
This paper presents a novel approach for uncertainty propagation of neutron-induced activation cross-section measurement using unscented transformation (UT). Generally, the first-order sensitivity analysis (sandwich formula) method is used for uncertainty propagation in cross-section measurement. It is based on a linear approximation of Taylor series expansion of the function of input parameters and gives satisfactory results for smooth nonlinear functions having relatively small uncertainties. On the contrary, the UT technique is completely defined by the moments of random process and hence produces better results for error propagation in the nonlinear case with large uncertainties. The UT method is easier to implement and gives results as accurate as the sandwich formula and Monte Carlo techniques. This work examines the application of the UT method in nuclear science as an alternate to the sandwich formula and Monte Carlo methods.