抽样法与灵敏度法k_(eff)不确定度量化

核反应堆的中子学模拟计算中,核数据的不确定度导致的积分量计算结果的不确定度,通常采用基于微扰理论的灵敏度与不确定度分析方法 (简称灵敏度法)量化.灵敏度分析法原则上只适用于线性模型,且一般输运计算程序难以直接进行灵敏度分析.而抽样法直接抽样核数据输入中子学计算程序进行计算,通过对计算结果的统计分析评估计算量的不确定度.抽样法易于实现、计算精确、且适用性强.在灵敏度分析与不确定度量化程序SURE中,增加了抽样法不确定度的量化功能.为将抽样法不确定度量化应用于复杂问题的模拟计算,需对其进行细致的考核.为此,选取简单的临界基准实验模型,分别采用灵敏度分析法和抽样法进行不确定度量化,得到了各核素各反应道核数据导致的k_(eff)计算不确定度.对比显示,两种方法的不确定度计算结果有很好的符合,验证了SURE程序抽样法功能的正确性.抽样法计算的k_(eff)符合正态分布,说明在一般核数据的不确定度范围内,k_(eff)与核数据近似成线性关系,利用灵敏度分析法评估k_(eff)计算值的不确定度是适用的.

Uncertainty quantification in the calculation of keff using sensitity and stochastic sampling method

The sensitivity and uncertainty analysis(S/U) method based on the first order perturbation theory is commonly employed to calculate the uncertainties in-nuclear reactor's integral parameters, such as the neutron effective multiplication factor(k_eff), due to uncertainties in nuclear data. However, this method is only theoretically suitable for the linear model because of its first order approximation. Moreover, S/U method is difficult to incorporate into a neutronics code, because the adjoint angular flux is needed to obtain the sensitivity coefficient of an integral parameter to nuclear data. Meanwhile, the sampling approach based on parametric random sampling of input parameters, an easy implemented method, evaluates the uncertainties in the integral parameters by performing a set of neutronics simulations inputted with a set of stochastic nuclear data sampled from a multinomial normal distribution with nuclear cross section mean values and covariance data. The sampling approach is considered as a more exact method, as linear approximation is not needed. With the increase of computational power, the sampling methods with consuming more time are now possible. The sampling approach is incorporated into SURE, a sensitivity and uncertainty analysis code developed in IAPCM, as a functional module. A careful verification of the new function is necessary before it is used to analyze complicated problems, such as multi-physical coupling calculations of nuclear reactor. Two simple fast criticality benchmark experiments, namely Godiva(HEU-MET-FAST-001) and Jezebel(PU-MET-FAST-001), are selected to verify the sampling module of SURE. The uncertainties in nuclear data are given by multigroup covariance matrices processed from ENDF/B-VⅡ. 1 data. The uncertainties in the computed value of k_eff resulting from uncertainties in the nuclear data are calculated with both S/U and sampling methods. The uncertainties due to reaction cross sections for each nuclide in two benchmarks given by two methods with the multigroup covariance matrices are in good agreement. Since the S/U module of SURE code is verified extensively, the correctness of the sampling function of the code is confirmed as well. The distribution of the k_eff from the sampling approach obeys the normal distribution pretty well, which indicates that k_eff varies linearly with the nuclear data under its uncertainty range, since the nuclear data used in calculations are assumed to be normal distribution in the sampling method. The results from the sampling method also support the S/U method with linear approximation as a suitable uncertainty quantification method for k_eff calculation.