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2026 Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
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SC Nuclear Summit focuses on V.C. Summer
The second annual South Carolina Nuclear Summit held last week featured utility executives and legislators from the state, as well as leaders from Brookfield Asset Management, which is being considered to restart construction on the two abandoned reactors at the V.C. Summer nuclear power plant in Fairfield County. The summit, at the University of South Carolina’s Colonial Life Arena, attracted more than 350 attendees. The event was hosted by the university’s Molinaroli College of Engineering and Computing.
Petter Helgesson, Dimitri Rochman, Henrik Sjöstrand, Erwin Alhassan, Arjan Koning
Nuclear Science and Engineering | Volume 177 | Number 3 | July 2014 | Pages 321-336
Technical Paper | doi.org/10.13182/NSE13-48
Articles are hosted by Taylor and Francis Online.
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 UO2 fuel by mixed-oxide (MOX) fuel in conventional thermal reactors. This paper compares UO2 fuel to two types of MOX fuel with respect to propagated nuclear data uncertainty, primarily in keff, by applying the Fast Total Monte Carlo method (Fast TMC) to a typical pressurized water reactor pin cell model in Serpent, including burnup. An extensive amount of nuclear data is taken into account, including transport and activation data for 105 nuclides, 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 keff; at zero burnup, the uncertainty is 0.6% for UO2 and ∼ 1% for the MOX fuels. The difference decreases with burnup. Uncertainties in fissile fuel nuclides 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 UO2 and MOX for the determination of uncertainty margins. However, it is difficult to estimate the effects of the simplified model; uncertainties should be propagated in more complicated models of any considered system. Fast TMC, however, allows for this without adding much computational time.