Nuclear data uncertainty quantification in the ARC fusion reactor

It is widely recognized that the design of a nuclear reactor requires also an uncertainty quantification (UQ) analysis, in the spirit of the Best Estimate Plus Uncertainty (BEPU) approach. The evaluation of uncertainties is especially important in the case of a nuclear fusion machine such as the Affordable Robust Compact (ARC) reactor, where the uncertainties of the nuclear data of uncommon nuclides in the nuclear field, like fluorine, beryllium and lithium, can have a significant impact on fundamental design parameters, such as the achievable Tritium Breeding Ratio (TBR). In this work, three different methods, namely the fast Total Monte Carlo (fTMC), the GRS and the Unscented Transform (UT), have been employed for the neutronic UQ analysis of ARC, with the use of the Monte Carlo code Serpent. These methods lead to similar values in terms of relative standard deviation on the TBR due to nuclear data, and can be considered as fast alternatives to brute-force sampling methods. The paper also provides suggestions to select the best approach according to the kind of analysis performed and to the nuclides considered in the study. The main outcome of these analyses suggests that the uncertainties in nuclear data of fluorine, beryllium and lithium is sufficiently small to prevent the TBR to assume values below the design constraints.