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.

Uncertainty Quantification (UQ) of numerical simulations is highly relevant in the study and design of complex systems. Among the various approaches available, Polynomial Chaos Expansion (PCE) analysis has recently attracted great interest. It belongs to nonintrusive spectral projection methods and consists of constructing system responses as polynomial functions of the stochastic inputs. The limited number of required model evaluations and the possibility to apply it to codes without any modification make this technique extremely attractive. In this work, we propose the use of PCE to perform UQ of complex, multi-physics models for liquid fueled reactors, addressing key design aspects of neutronics and thermal fluid dynamics. Our PCE approach uses Smolyak sparse grids designed to estimate the PCE coefficients. To test its potential, the PCE method was applied to a 2D problem representative of the Molten Salt Fast Reactor physics. An in-house multi-physics tool constitutes the reference model. The studied responses are the maximum temperature and the effective multiplication factor. Results, validated by comparison with the reference model on 10³ Monte-Carlo sampled points, prove the effectiveness of our PCE approach in assessing uncertainties of complex coupled models.

Nuclear fusion reactors are composed of several complex components whose behavior may be not certain a priori. This uncertainty may have a significant impact on the evolution of fault transients in the machine, causing unexpected damage to its components. For this reason, a suitable method for the uncertainty propagation during those transients is required. The Monte Carlo method would be the reference option, but it is, in most of the cases, not applicable due to the large number of required, repeated simulations. In this context, the Polynomial Chaos Expansion has been considered as a valuable alternative. It allows us to create a surrogate model of the original one in terms of orthogonal polynomials. Then, the uncertainty quantification is performed repeatedly, relying on this much simpler and faster model. Using the fast current discharge in the Divertor Tokamak Test Toroidal Field (DTT TF) coils as a reference scenario, the following method has been applied: the uncertainty on the parameters of the Fast Discharge Unit (FDU) varistor disks is propagated to the simulated electrical and electromagnetic relevant effects. Eventually, two worst-case scenarios are analyzed from a thermal–hydraulic point of view with the 4C code, simulating a fast current discharge as a consequence of a coil quench. It has been demonstrated that the uncertainty on the inputs (varistor parameters) strongly propagates, leading to a wide range of possible scenarios in the case of accidental transients. This result underlines the necessity of taking into account and propagating all possible uncertainties in the design of a fusion reactor according to the Best Estimate Plus Uncertainty approach. The uncertainty propagation from input data to electrical, electromagnetic, and thermal hydraulic results, using surrogate models, is the first of its kind in the field of the modeling of superconducting magnets for nuclear fusion applications.