Nuclear Fuel-debris Characterisation via Multimodal Spectroscopy and Analytics (NuFAMSA)

通过多模态光谱和分析进行核燃料碎片表征 (NuFAMSA)

• 批准号: EP/Y029445/1
• 负责人: Paul Murray
• 金额: $ 63.09万
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY029445%2F1
• 关键词: Nuclear; Fuel; debris; Characterisation; via

On 11th March 2011, a major Earthquake followed by a 15-metre tsunami caused an unprecedented accident in the Fukushima nuclear power plant. Fukushima Dai-ichi reactors suffered core meltdowns in the following days. Since then, complex activities for the removal of the melted fuel have been planned, where one of the main challenges is the characterisation of nuclear fuel-debris, including its detection, safeguard, retrieval and disposal.This research brings together a complementary and multidisciplinary expert team of researchers and industrialists from the UK and Japan to explore the use of hyperspectral imaging (HSI) along with other sensor technologies and data fusion for the effective characterisation of nuclear fuel-debris.Conventional visual techniques have already been used for remote visual assessment of the core debris in the reactors at Fukushima. Some spectroscopy approaches, able to collect data across the electromagnetic spectrum have already been proposed as more powerful techniques to distinguish fuel-containing debris from non-fissile core spall via their respective spectral fingerprints. These include Laser Induced Breakdown Spectroscopy (LIBS), Raman, gamma-ray spectroscopy, and neutron assay. However, these techniques are limited to point-based measurements, a key limitation leading to no spatial information of the scene under analysis being captured. Multiple point-based measurements across a spatial location are possible, but at the cost of being extremely time-consuming, within small areas, and with a poor spatial resolution. In practical terms, these techniques are unable to scan a spatial location, and only spectral data can be collected. To address this limitation, HSI is introduced in this research.HSI is able to simultaneously capture the spatial and spectral content of a given scene under analysis. Unlike conventional images, which capture three channels (Red, Green and Blue) in the visible range of the spectrum, HSI captures hundreds of channels covering not only the visible but part of the infrared range (normally 400-2500nm), going beyond what the human eye can see. In fact, in a hyperspectral image, the content of each pixel is a vector array containing the spectral response or fingerprint of the material captured by that pixel. Therefore, HSI is able to capture spectral information across a spatial location, exposing the different fingerprints of the materials present in the scene under analysis, and their distribution.The core idea of this project is to use HSI technology to generate spatial maps in which fuel-containing debris can be distinguished from non-fissile core spall, avoiding re-criticality during debris retrievals. Based on the different spectral fingerprints, it is also expected that HSI can be used to further characterise certain waste types and automatically detect them within an accurate location. Moreover, HSI can be combined with other sensor technologies, where HSI can be used as a pre-screening tool to direct the point-based acquisition of LIBS, gamma-ray spectroscopy neutron assay. We anticipate that the proposed research will lead to new and highly valuable inspection technology which can support nuclear decommissioning in Japan, the UK and around the world.

2011年3月11日，一场大地震和一场15米高的海啸在福岛核电站造成了前所未有的事故。在接下来的几天里，福岛第一核电站的堆芯熔毁。从那时起，计划了复杂的活动来清除熔化的燃料，其中主要挑战之一是核燃料碎片的表征，包括其检测、保护、回收和处置。这项研究汇集了一个由来自英国和日本的研究人员和工业家组成的互补的多学科专家团队，探索使用高光谱成像(HSI)以及其他传感器技术和数据融合来有效表征核燃料碎片。传统的视觉技术已经被用于对福岛反应堆堆芯碎片的远程视觉评估。一些能够收集电磁光谱数据的光谱学方法已经被提议作为更强大的技术，通过各自的光谱指纹区分含燃料碎片和非裂变堆芯散裂。这些技术包括激光诱导击穿光谱(LIBS)、拉曼光谱、伽马射线光谱和中子分析。然而，这些技术仅限于基于点的测量，这是导致无法捕获所分析场景的空间信息的关键限制。一个空间位置上的多个基于点的测量是可能的，但代价是非常耗时，在小区域内，并且空间分辨率很差。实际上，这些技术无法扫描空间位置，只能收集光谱数据。为了解决这一局限性，引入了HSI，HSI能够同时捕获分析中给定场景的空间和光谱内容。与传统的在可见光范围内捕捉三个通道(红、绿、蓝)的图像不同，HSI捕捉到数百个通道，不仅覆盖可见光，还覆盖部分红外范围(通常为400-2500 nm)，超出了人眼可以看到的范围。事实上，在高光谱图像中，每个像素的内容都是一个矢量阵列，其中包含该像素捕获的物质的光谱响应或指纹。因此，HSI能够捕获跨空间位置的光谱信息，暴露出被分析场景中存在的材料的不同指纹及其分布。该项目的核心思想是使用HSI技术生成空间地图，在空间地图中可以区分含燃料碎片和非裂变堆芯散落，避免在碎片提取过程中的重新临界。基于不同的光谱指纹，还有望使用HSI进一步表征某些废物类型，并在准确的位置自动检测它们。此外，HSI可以与其他传感器技术相结合，其中HSI可以作为预筛选工具来指导基于点的LIBS采集，即伽马射线能谱中子分析。我们预计，拟议的研究将带来新的、非常有价值的检查技术，可以支持日本、英国和世界各地的核退役。