Neutronic modeling of fusion machines requires a detailed representation of their complex geometry in order to properly evaluate various parameters of interest such as energy deposition and tritium production in the breeding blanket. In this work, the neutronics of the Affordable, Robust, Compact (ARC) fusion reactor is modeled with the Monte Carlo particle transport code Serpent developed at VTT Technical Research Centre of Finland as an alternative to other, more established, tools in the fusion community such as the Monte Carlo N-Particle Transport (MCNP) code. The tritium breeding ratio (TBR) and the power deposited by neutrons and photons inside the breeding blanket of ARC are evaluated. Considerations related to activation of materials and to neutron shielding are not taken into account. As a first step, estimations have been obtained adopting a spatially uniform neutron source inside the plasma chamber. A second set of calculations has been performed considering a nonuniform source that takes into account a more realistic neutron generation distribution, with higher values at the center of the plasma and reduced rates toward the plasma edge. The results obtained with Serpent have been compared with available literature values for the TBR and the power deposition, confirming that Serpent can be considered a suitable alternative code for the neutronic analysis of fusion reactors like ARC. The TBR presented in this paper (1.0853) is in good agreement with the value found in the literature, with a relative difference of 0.49%. The total power deposition has a maximum relative difference of 12% for the components of interest in the present work.

It is widely recognized that the safe and robust design of a nuclear system requires an uncertainty propagation analysis concerning the various nuclear data used as input parameters, especially in view of the most recent design methodologies like the best-estimate plus uncertainty approach. The evaluation of the input uncertainties and their propagation to the design parameters of interest is particularly important in the case of nuclear fusion machines, such as the Affordable Robust Compact (ARC) reactor, which features the presence of uncommon isotopes in the nuclear engineering field, like fluorine, beryllium, and lithium. The uncertainties of the nuclear data of these nuclides can have a significant impact on the fundamental design parameters, such as the target tritium breeding ratio (TBR). Hence, in this work we investigate the application of different methods for propagating the nuclear data uncertainty to the parameters of interest, computed with the Serpent 2 Monte Carlo code. All the methods proposed in this work share the feature of being nonintrusive, implying that they can be profitably employed independently of the physical and/or computational model adopted. The methods discussed in this work are the fast Total Monte Carlo, the GRS, the unscented transform, and the polynomial chaos expansion. The first three methods led to similar values in terms of relative standard deviation of the TBR due to nuclear data and can be considered as fast alternatives to brute-force sampling methods. For these three methods, the present paper suggests how to select the best approach according to the kind of analysis to be performed and the nuclides considered in the study. The effect of the use of different nuclear data libraries and of different input covariance matrices is also examined. The main outcome of these analyses suggests that the uncertainties in the nuclear data of nickel, fluorine, beryllium, and lithium are sufficiently small (i.e., smaller than 1%) to prevent the TBR from assuming values below the design constraints. The overall uncertainty on the TBR of ARC due to the nuclides here considered was evaluated to be ~0.9%. Concerning the polynomial chaos expansion approach, this paper shows that its application is computationally inefficient compared to the other techniques when the input data dimensionality are very large, as for the case of nuclear data.