## 1 Introduction  
Nuclear facilities, like fission reactors and, in the future, fusion reactors, are extremely complex systems both from a physical and a technological point of view. For example, in fusion reactors, many components are the first of their kind. The widespread use of fusion reactors in the future will be possible if these technologies reach maturity in terms of industrial production. At the same time, the construction of the first fusion reactors will help to achieve this maturity. However, currently, the behavior of these components is characterized by intrinsic uncertainties. When simulating a transient, the uncertainties propagate from the single components to the overall system behavior, possibly leading to serious damages to the reactor. For this reason, it is relevant, from the design phase, to estimate the impact of the uncertainty of the data used as inputs to the model on the final results of the simulations. This will us allow to set proper thresholds on the uncertainties requested from the suppliers of the different components to avoid damage to the reactor. The reference option for the non-intrusive (i.e., not requiring modifications of the model)uncertainty propagation (UP) analysis is the Monte Carlo (MC) method. This approach is based on the definition and the random sampling of a set of uncertain inputs. Then, deterministic calculations with these inputs are performed and the final results can be aggregated to obtain statistical information about the output. However, in view of the computational time required to perform each single simulation with complex high-fidelity codes, the use of this method is often not feasible due to the very large number of simulations required to reach statistical convergence. To face this problem, several different methods have been proposed in the literature. An option is to reduce the number of simulations for the uncertainty quantification approximating the input distribution rather than the model, like in the Unscented Transform (UT) technique [1, 2]. Another possibility is to build a surrogate model and use it instead of the real model to perform the uncertainty quantification, as in Gaussian process modeling [3] and Polynomial Chaos Expansion (PCE) [4, 5]. The main advantage of this alternative is that the surrogate model is orders of magnitude faster than the corresponding real model. The PCE has been employed in this work, using the python (version 3.10) module _chaospy_ (version 4.3.12) [6] to analyze the uncertainty propagation during a Fast-current Discharge (FD) in the Divertor Tokamak Test (DTT) facility Toroidal Field (TF) magnet system. In Figure 1, the geometry of a single DTT TF coil is reported. The uncertainty is given by the parameters of the characteristic of the Fast Discharge Unit (FDU) varistor, influencing the evolution of the coil current during FD. This causes both electrical (e.g., modification of coil peak voltage, variation of deposited energy in the FDU, etc.) and electromagnetic (EM) drawbacks; indeed, varying the evolution of the coil currents means varying the magnetic field time derivative, which translates to a modification of the eddy currents induced within the TF coil casing and, as a consequence, a modification to the Joule power deposited. In this work, the uncertainties of the above-mentioned results have been assessed using PCE. The electrical results have been obtained via an object-oriented model developed using the Modelica language in the open source environment OpenModelica [7]. The uncertainty obtained with PCE has been benchmarked against the MC method since the computational time required was still reasonable. On the other hand, the EM results have been computed with 3D-FOX [8]. In this case, the benchmark of the PCE outcomes with those of the MC was impossible due to the excessive computational time required by the EM simulations. For this reason, a benchmark of the results obtained with PCE against the UT method is proposed.