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Lightbridge announces first U-Zr fuel rod samples extruded at INL
Lightbridge Corporation announced today that it has reached “a critical milestone” in the development of its extruded solid fuel technology. Coupon samples using an alloy of zirconium and depleted uranium—not the high-assay low-enriched uranium (HALEU) that Lightbridge plans to use to manufacture its fuel for the commercial market—were extruded at Idaho National Laboratory’s Materials and Fuels Complex.
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.