论文标题
基于X射线CT的Triso燃料识别算法
Algorithms for TRISO Fuel Identification Based on X-ray CT Validated on Tungsten-Carbide Compacts
论文作者
论文摘要
三结构 - 偏性（TRISO）燃料是正在开发的候选高级反应堆类型的最成熟燃料类型之一。 Triso-Fuel鹅卵石连续流过反应堆芯，可以重新插入反应堆中，直到达到目标燃烧为止。识别单个燃料鹅卵石的能力将使我们能够计算核心中的燃料停留时间，并验证卵石流量计算模型，防止过度燃烧或过早的燃料排放，并在燃油循环期间保持特殊核材料的责任。在这项工作中，我们开发了3D图像重建和分割算法，以准确分割Triso颗粒并提取唯一的3D分布。我们已经开发了一种旋转不变和噪声识别算法，该算法使我们能够在存在旋转和噪音的情况下识别卵石并检索卵石ID。我们还报告了一个模型燃料样品的200 kV X射线CT图像重建的结果，该模型燃料样品由Lucite基质中的钨 - 卡比德（WC）内核组成。 Triso颗粒的3D分布以及其他特征，例如$^{235} $ U富集和通过中子多重性计数提取的燃烧水平，将在合理的时间内实现准确的燃油识别。
Tristructural-isotropic (TRISO) fuel is one of the most mature fuel types for candidate advanced reactor types under development. TRISO-fuel pebbles flow continuously through the reactor core and can be reinserted into the reactor several times until a target burnup is reached. The capability of identifying individual fuel pebbles would allow us to calculate the fuel residence time in the core and validate pebble flow computational models, prevent excessive burnup accumulation or premature fuel discharge, and maintain accountability of special nuclear materials during fuel circulation. In this work, we have developed a 3D image reconstruction and segmentation algorithm to accurately segment TRISO particles and extract the unique 3D distribution. We have developed a rotation-invariant and noise-robust identification algorithm that allows us to identify the pebble and retrieve the pebble ID in the presence of rotations and noises. We also report the results of 200kV X-ray CT image reconstruction of a mock-up fuel sample consisting of tungsten-carbide (WC) kernels in a lucite matrix. The 3D distribution of TRISO particles along with other signatures such as $^{235}$U enrichment and burnup level extracted through neutron multiplicity counting, would enable accurate fuel identification in a reasonable amount of time.
