[论文解读] Fuel rod classification from Passive Gamma Emission Tomography (PGET) of spent nuclear fuel assemblies

Riina Virta, Rasmus Backholm+6 more|arXiv (Cornell University)|Sep 24, 2020

Advanced X-ray and CT Imaging参考文献 11被引用 8

一句话总结

本论文提出了一种鲁棒的非破坏性方法，利用被动伽马发射断层扫描（PGET）结合迭代图像重建技术，对乏核燃料中的燃料棒进行分类。通过同时重建活度图与衰减图，并结合几何和材料先验信息，该方法在多种燃料类型和参数下，实现了对燃料棒（包括缺失、部分、水通道及可燃吸收体棒）的高精度分类，仅对存在燃料棒出现极少数假阴性结果。

ABSTRACT

Safeguarding the disposal of spent nuclear fuel in a geological repository needs an effective, efficient, reliable and robust non-destructive assay (NDA) system to ensure the integrity of the fuel prior to disposal. In the context of the Finnish geological repository, Passive Gamma Emission Tomography (PGET) will be a part of such an NDA system. We report here on the results of PGET measurements at the Finnish nuclear power plants during the years 2017-2020. Gamma activity profiles are recorded from all angles by rotating the detector arrays around the fuel assembly that has been inserted into the center of the torus. Image reconstruction from the resulting tomographic data is defined as a constrained minimization problem with a data fidelity term and regularization terms. The activity and attenuation maps, as well as detector sensitivity corrections, are the variables in the minimization process. The regularization terms ensure that prior information on the (possible) locations of fuel rods and their diameter are taken into account. Fuel rod classification, the main purpose of the PGET method, is based on the difference of the activity of a fuel rod from its immediate neighbors, taking into account its distance from the assembly center. The classification is carried out by a support vector machine. We report on the results for ten different fuel types with burnups between 5.72 and 55.0 GWd/tU, cooling times between 1.87 and 34.6 years and initial enrichments between 1.9 and 4.4%. For all fuel assemblies measured, missing fuel rods, partial fuel rods and water channels were correctly classified. Burnable absorber fuel rods were classified as fuel rods. On rare occasions, a fuel rod that is present was falsely classified as missing. We conclude that the combination of the PGET device and our image reconstruction method provides a reliable base for fuel rod classification.

研究动机与目标

• 开发一种可靠、非破坏性的分析（NDA）方法，用于在芬兰地质处置前验证乏核燃料的完整性。
• 通过实现棒级异常检测，解决现有NDA技术仅能检测总体材料偏差的局限性。
• 提高针对不同燃耗、冷却时间及富集度的乏燃料组件的图像重建与分类精度。
• 将燃料棒分类为不同类别（存在、缺失、异常或修改），以支持 safeguards 核查并减少误报。
• 通过自动化数据采集、图像重建与分类，将PGET与未来 safeguards 工作流程集成。

提出的方法

• PGET系统采用两个线性排列的准直CdZnTe（CZT）伽马射线探测器阵列，呈环形布置，从360个角度获取乏燃料组件的断层投影数据。
• 图像重建被表述为一个约束最小化问题，目标函数包含数据保真项及针对活度、衰减和探测器灵敏度的正则化项。
• 正则化项引入了关于燃料棒位置、直径以及预期活度/衰减范围的先验知识，以稳定逆问题求解。
• 采用Levenberg–Marquardt算法求解非线性最小化问题，实现活度图与衰减图的同时重建。
• 支持向量机（SVM）基于燃料棒相对于邻近棒的活度及其距组件中心的径向距离，对燃料棒进行分类。
• 该方法考虑了探测器响应，并采用能量窗口设置（400–600 keV，600–700 keV，700–1500/2000 keV，>1500/3000 keV），以聚焦关键裂变产物的伽马射线发射。

实验结果

研究问题

• RQ1 PGET结合迭代图像重建是否能可靠地检测出在多种乏燃料组件类型和参数下单根缺失的燃料棒？
• RQ2 当可燃吸收体棒和部分燃料棒的伽马活度或密度与标准燃料棒相近时，其分类准确性如何？
• RQ3 几何简化（如理想化棒位置）对图像重建与分类精度有何影响？
• RQ4 分类系统能否改进以检测被修改的燃料棒（如替换为低活度材料）而不将其误判为缺失？
• RQ5 如何改进前向模型以包含伽马射线散射及更真实的材料属性，从而提升重建保真度？

主要发现

• 所有测量的燃料组件（涵盖10种类型，燃耗范围5.72–55.0 GWd/tU，冷却时间1.87–34.6年，初始富集度1.9–4.4%）均被正确分类。
• 缺失燃料棒、部分燃料棒、水通道及可燃吸收体棒均被正确识别，且可燃吸收体棒被归类为燃料棒。
• 仅发生极少数误分类，即某根存在的燃料棒被错误分类为缺失，表明方法具有高敏感性与特异性。
• 活度低于平均值的异常燃料棒（如因燃耗较低所致）被当前SVM分类器误判为缺失，尽管其物理存在可在衰减图中清晰可见。
• 该方法在2017–2020年多个测量周期中表现出强鲁棒性，使用360个投影角度及每角度800–924 ms积分时间，性能保持一致。
• 未来改进方向包括：修正角落燃料棒的几何假设、优化活度-衰减边界范围，并在前向模型中引入散射效应以增强真实性。