The objective of this paper is to assess the applicability of three Deterministic Sampling (DS) methods (namely DS-Standard, DS-Simplex and DS-Hadamard) for uncertainty quantification of manufacturing tolerances for global safety parameters, such as the multiplicative factor (k_eff), and local quantities, such as the fission rate. More specifically, the IPEN/MB-01 research reactor (from LWR-RESR benchmark) has been modeled in the Monte Carlo code SERPENT to which the sampling methods have been applied. Twenty different input parameters (related to geometry and materials) were considered as sources of uncertainties. Uncertainty estimates for k_eff by DS were all compatible among the three methodologies (with a maximum nominal difference of 5 pcm) and in agreement with values reported in the benchmark (within the statistical Monte Carlo uncertainty of 15 pcm). Fission rate results obtained by DS showed full compatibility with results obtained by conventional stochastic sampling methods (such as Random Sampling or Latin Hypercube Sampling) and with those reported in the benchmark. The DS-Simplex showed the highest uncertainty in predicting the uncertainty on fission rate when compared to Random Sampling (with a maximum relative difference of 10% in the uncertainty estimate), whereas other DS methods showed a maximum deviation of 5%. The DS methods offer a remarkable reduction in the number of samples (from thousand to tens or hundreds according to the selected approach) required for an accurate uncertainty quantification for both global and local quantities.

本文的目的是评估三种确定性抽样 (DS) 方法（即 DS-Standard、DS-Simplex 和 DS-Hadamard）对全球安全参数的制造公差的不确定性量化的适用性，例如乘法因子 (k _eff ) 和局部量，例如裂变率。更具体地说，IPEN/MB-01 研究堆（来自 LWR-RESR 基准）已在应用了采样方法的蒙特卡罗代码 SERPENT 中建模。二十个不同的输入参数（与几何形状和材料有关）被认为是不确定性的来源。k _{eff 的}不确定性估计DS 在三种方法中都兼容（最大名义差异为 5 pcm）并且与基准中报告的值一致（在 15 pcm 的统计蒙特卡罗不确定性范围内）。DS 获得的裂变率结果显示与传统随机抽样方法（如随机抽样或拉丁超立方抽样）获得的结果以及基准中报告的结果完全兼容。与随机抽样相比，DS-Simplex 在预测裂变率的不确定性方面表现出最高的不确定性（不确定性估计中的最大相对差异为 10%），而其他 DS 方法显示出最大偏差为 5%。