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Undeclared uranium hitches a ride on cobalt exports from Congo, study says
Philippe (left) and Manzuk quantified the amount of uranium that has been exported from the DRC in cobalt shipments or left behind in the environment. (Photo: Joel Hallberg/UW–Madison)
Researchers at the University of Wisconsin–Madison and Princeton University have published a study in Nature Communications that calls attention to a blind spot in nuclear nonproliferation: The Democratic Republic of the Congo (DRC) has exported thousands of metric tons of uranium, and there is no accounting for where it has gone.
In partnership with Lighthouse Reports and the Financial Times, UW–Madison nuclear engineering professor and nuclear security expert Sébastien Philippe and Ryan Manzuk, a geologist and research fellow in Philippe’s group and at Princeton, conducted the study using countrywide mineralization and geochemical data.
B. T. Rearden, M. L. Williams, M. A. Jessee, D. E. Mueller, D. A. Wiarda
Nuclear Technology | Volume 174 | Number 2 | May 2011 | Pages 236-288
Technical Paper | Special Issue on the SCALE Nuclear Analysis Code System / Radiation Protection | doi.org/10.13182/NT174-236
Articles are hosted by Taylor and Francis Online.
In SCALE 6, the Tools for Sensitivity and UNcertainty Analysis Methodology Implementation (TSUNAMI) modules calculate the sensitivity of keff or reactivity differences to the neutron cross-section data on an energy-dependent, nuclide-reaction-specific basis. These sensitivity data are useful for uncertainty quantification, using the comprehensive neutron cross-section-covariance data in SCALE 6. Additional modules in SCALE 6 use the sensitivity and uncertainty data to produce correlation coefficients and other relational parameters that quantify the similarity of benchmark experiments to application systems for code validation purposes. Bias and bias uncertainties are quantified using parametric trending analysis or data adjustment techniques, providing detailed assessments of sources of biases and their uncertainties and quantifying gaps in experimental data available for validation. An example application of these methods is presented for a generic burnup credit cask model.