Independent thesis Advanced level (professional degree), 20 credits / 30 HE credits
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.
The Fast Total Monte Carlo method (Fast TMC) is a further development of Total
Monte Carlo - a reliable, general and flexible way to study how uncertainties
propagate from differential nuclear data to integral results. The main idea is not new
or unique for the field: integral quantities of interest are computed multiple times
using differential data which is randomly sampled from distributions that quantify the
uncertainty of the differential data; the spread in the results is then used in the
quantification of the propagated uncertainties.
This text compares UO-2 fuel to two types of MOX fuel with respect to propagated
nuclear data uncertainty, primarily in the neutron multiplication factor k-eff, by
applying Fast TMC to a typical PWR pin cell model in the Monte Carlo transport code
SERPENT, including burnup. An extensive amount of nuclear data uncertainties is
taken into account, including transport and activation data for 105 isotopes, fission
yields for 13 actinides and thermal scattering data for hydrogen in water.
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.