Characterisation of the spatial variability of material properties of Gilsocarbon and NBG-18 using random fields

Characterisation of the spatial variability of material properties of Gilsocarbon and NBG-18 using random fields
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使用随机场表征 Gilsocarbon 和 NBG-18 材料特性的空间变异性

DOI:
10.1016/j.jnucmat.2018.09.008
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发表时间:
2018
影响因子:
3.1
通讯作者:
Arregui-Mena J
Arregui-Mena J
中科院分区:
工程技术2区
文献类型:
--
作者:
Arregui-Mena J

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石墨是第四代概念的候选材料,在英国被用作先进气冷堆(AGR)的慢化剂。坯料内存在空间材料可变性,导致不同部件之间的材料属性值不同。材料特性和辐照效应的变化会在核反应堆石墨堆芯中产生应力集中和不同的机械响应。为了验证材料的变异性,地质统计学技术,称为变差和随机场理论,适用于研究的密度和杨氏模量的坯料的Gilsocarbon和NBG-18石墨等级。变差分析是一种用于估计材料属性值具有显著空间相关性的距离的技术,称为波动尺度或空间相关长度。本文使用随机场理论来创建模型,模拟原始数据集的原始空间和统计分布。这项研究发现不同的值的密度和杨氏模量的相关长度周围的边缘的Gilsocarbon坯料,而在NBG-18的情况下,类似的相关长度,发现整个坯料。给出了几个随机场的例子,以再现在原始数据中发现的空间模式和值。
Graphite is a candidate material for Generation IV concepts and is used as a moderator in Advanced Gas-cooled Reactors (AGR) in the UK. Spatial material variability is present within billets causing different material property values between different components. Variations in material properties and irradiation effects can produce stress concentrations and diverse mechanical responses in a nuclear reactor graphite core. In order to characterise the material variability, geostatistical techniques called variography and random field theory were adapted for studying the density and Young's modulus of a billet of Gilsocarbon and NBG-18 graphite grades. Variography is a technique for estimating the distance over which material property values have significant spatial correlation, known as the scale of fluctuation or spatial correlation length. The paper uses random field theory to create models that mimic the original spatial and statistical distributions of the original data set. This study found different values of correlation length for density and Young's modulus around the edges of a Gilsocarbon billet, while in the case of NBG-18, similar correlation lengths where found across the billet. Examples of several random fields are given to reproduce the spatial patterns and values found in the original data.
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