Abstract

Because of the complexity of the nuclear reactor system, traditional statistical sampling methods, such as random sampling and Latin hypercube sampling, often lead to unstable uncertainty quantification results of the reactor physics analysis. In order to make the analysis results robust, traditional sampling methods require a large number of samples, which brings a huge computation cost. For this reason, this paper proposes a new sampling scheme based on the moment matching method to generate efficient samples for the uncertainty quantification of reactor physics calculations. A linear programming model is established to minimize the deviations of the first- and second-order moments. The generated samples can better reflect the statistical characteristics of the real distribution than classical sampling methods. A series of numerical experiments is carried out to demonstrate the superiority of the proposed moment matching sampling method, which can quickly provide more reliable uncertainty quantification results with a small sample size.

摘要

由于核反应堆系统的复杂性，传统的统计抽样方法，如随机抽样、拉丁超立方抽样等，往往导致反应堆物理分析的不确定性量化结果不稳定。为了使分析结果具有鲁棒性，传统的采样方法需要大量的样本，这带来了巨大的计算成本。为此，本文提出了一种基于矩匹配法的新采样方案，为反应堆物理计算的不确定性量化生成有效样本。建立线性规划模型以最小化一阶和二阶矩的偏差。生成的样本比经典的抽样方法更能反映真实分布的统计特征。