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UO-2 Versus MOX: Propagated Nuclear Data Uncertainty for k-eff, with Burnup
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. (Nuclear Reactions)
Nuclear Research and Consultancy Group NRG, Petten, The Netherlands.
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. (Nuclear Reactions)
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
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2014 (English)In: Nuclear science and engineering, ISSN 0029-5639, E-ISSN 1943-748X, Vol. 177, no 3, 321-336 p.Article in journal (Refereed) Published
Abstract [en]

Precise assessment of propagated nuclear data uncertainties in integral reactor quantities is necessary for the development of new reactors as well as for modified use, e.g. when replacing UO-2 fuel by MOX fuel in conventional thermal reactors.

This paper compares UO-2 fuel to two types of MOX fuel with respect to propagated nuclear data uncertainty, primarily in k-eff, by applying the Fast Total Monte Carlo method (Fast TMC) to a typical PWR pin cell model in Serpent, including burnup. An extensive amount of nuclear data is taken into account, including transport and activation data for 105 isotopes, fission yields for 13 actinides and thermal scattering data for H in H2O.

There is indeed a significant difference in propagated nuclear data uncertainty in k-eff; at 0 burnup the uncertainty is 0.6 % for UO-2 and about 1 % for the MOX fuels. The difference decreases with burnup. Uncertainties in fissile fuel isotopes and thermal scattering are the most important for the difference and the reasons for this are understood and explained.

This work thus suggests that there can be an important difference between UO-2 and MOX for the determination of uncertainty margins. However, the effects of the simplified model are difficult to overview; uncertainties should be propagated in more complicated models of any considered system. Fast TMC however allows for this without adding much computational time.

Place, publisher, year, edition, pages
2014. Vol. 177, no 3, 321-336 p.
Keyword [en]
Nuclear Data Uncertainty Propagation, Monte Carlo, MOX
National Category
Engineering and Technology Physical Sciences
Research subject
Nuclear Physics
Identifiers
URN: urn:nbn:se:uu:diva-212530DOI: 10.13182/NSE13-48ISI: 000339133300006OAI: oai:DiVA.org:uu-212530DiVA: diva2:678243
Available from: 2013-12-11 Created: 2013-12-11 Last updated: 2017-12-06Bibliographically approved

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Helgesson, PetterSjöstrand, HenrikAlhassan, Erwin

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