## 1 Introduction  
The propagation of a nuclear response's uncertainty in fusion-related neutronic simulations is an area of intense research activity because this information enables an assessment of the safety margins of the results. The complexity of the fusion facilities and geometry models used for the simulations defines the necessity of the fidelity assessment for the results obtained due to the inherent uncertainty of the nuclear data involved in the calculations. The evaluation of the nuclear reaction data includes natural uncertainty due to (i) nuclear model parameter deviations and (ii) the uncertainty of the experimental data used for the assessments. Modern nuclear data evaluations include as a prerequisite the uncertainty of all reaction cross sections presented in the form of covariance matrixes. Various nuclear reactions' particle emission spectra have different uncertainties in different energy regions depending on the model's parameter deviations and the availability of the experimental database. Thereby, for some reactions, the estimated uncertainty can vary from a few up to dozens of percent in different energy regions. Assuming that the typical material compositions used in the fusion technology can include many chemical elements, the number of nuclear data uncertainties that can be involved in the calculations becomes big. To perform the assessment of the integral nuclear response's uncertainty due to nuclear data, the covariance matrixes presented in the evaluated data files involve making use of the sophisticated mathematical approach, such as perturbation theory, which must be included in the particle transport code [1]. The MCNP code [2] adopted as a standard computer tool for fusion applications does not include the possibility of invoking the full-scale use and processing of the covariance data during particle transport calculations with different neutron sources. Instead, a simplified adjoint-weighted perturbation method is implemented in the MCNP to access changes in k\({}_{\text{eff}}\) due to material substitution or to calculate sensitivity coefficients of the k\({}_{\text{eff}}\) for nuclear data [2]. This approach implies several significant simplifications of covariance data and it treats the uncertainty of the particle emission spectra (differential and double differential) in a very approximate way or even neglects them. An alternative way to calculate the propagation of the nuclear data uncertainty in a realistic 3D large-scale geometry is to apply Monte Carlo analyses utilizing intensive computer simulations, the so-called total Monte Carlo (TMC) approach [3; 4; 5; 6]. It assumes using not one set of nuclear data files included in the evaluated data library but thousands of such data sets, all of them derived from the same original library using perturbation theory. An important note here is that all random data files for one isotope used in the calculations were produced making use of the nuclear models' parameters, obeying normal distribution, deviating from the most probable one within the accepted range and therefore all these files are equivalent, and they can be also used in the usual way for particle transport simulations. By randomly choosing one set of data from the thousand possible ones, an MCNP run can be started as the result of one requested integral nuclear response, for example, the tritium breeding ratio (TBR) in a fusion reactor. The multiple repetition of this procedure ultimately results in the probability distribution of the integral nuclear response in the large-scale system. If results can be fitted with Gaussian distribution, i.e., they obey normal distribution, the most probable value of the integral nuclear response and the standard deviation of the results can also be obtained. The assessment of the TBR uncertainty only for Pb cross sections ((n,2n), (n,el) and (n,n\({}^{\prime}\))) in the HCLL DEMO concept [7], performed utilizing TMC approach, demonstrated its applicability for the fusion reactor.