摘要
提供符合核数据已知统计矩信息和物理约束的随机扰动样本,是核数据作为分析输入的各类堆芯物理计算相关的统计学习算法的基础。合理扰动的核数据样本是保证堆芯物理响应量特征提取、降阶建模等数据驱动人工智能模型预测准确性的重要因素之一。选取能够满足核数据自身物理约束特征的概率密度分布是保证上述核数据随机抽样合理性的关键。本文针对核数据库中常见的两类物理约束特征,即非负性取值约束(例如裂变产额数据、核反应截面数据等)以及归一化约束(例如衰变分支比等),研究其对应的概率密度分布选取方法并提供相应的抽样算法。结合评价核数据库中提供的核数据不确定度信息,本文对不同概率密度分布下的核数据随机抽样效果进行对比,并给出概率密度分布的选取建议。
For statistical learning algorithms that are related to various core physical calculations with nuclear data as the analysis input,providing stochastic disturbance samples that are consistent with the known statistical moment information and physical constraints of nuclear data is fundamental.Reasonably perturbed nuclear data samples are one of the important factors to ensure the prediction accuracy of data-driven artificial intelligence models such as core physical response feature extraction and reduced-order modeling.Selecting the probability density distribution that can meet the physical constraints of nuclear data itself is the key to ensure the rationality of the above stochastic sampling of nuclear data.This work focuses on two types of physical constraints that are commonly seen in the evaluated nuclear data library,namely,nonnegativity constraints(e.g.fission product yield,nuclear reaction cross section)and normalization constraints(e.g.decay branch ratio),studies the corresponding probability density distribution selection methods and provides the corresponding sampling algorithms.Combined with the uncertainty information of nuclear data provided in the evaluated nuclear data library,this work compares the stochastic sampling effects of nuclear data with different probability density distributions,and gives some suggestions on the selection of probability density distributions.
作者
王毅箴
郝琛
Wang Yizhen;Hao Chen(College of Nuclear Science and Technology,Harbin Engineering University,Harbin,150001,China)
出处
《核动力工程》
北大核心
2025年第2期38-47,I0003,共11页
Nuclear Power Engineering
基金
国家自然科学基金(12405203)。
