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，一场大地震随后发生了15米的海啸，在福岛核电站发生了前所未有的事故。福岛戴奇（Fukushima Dai-ichi反应堆）在接下来的几天内遭受核心崩溃。 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核燃料解脱的表征。规定的视觉技术已经用于远程视觉评估福岛反应堆中的核心碎片。已经提出了一些能够在电磁光谱上收集数据的光谱方法，已经被提出为更强大的技术，可以通过各自的光谱指纹将含燃料的碎片与非刺激性核心平台区分开。这些包括激光诱导的分解光谱法（LIBS），拉曼，伽马射线光谱和中子测定法。但是，这些技术仅限于基于点的测量，这是一个关键限制，导致未捕获分析场景的空间信息。可以在空间位置进行多个基于点的测量，但要付出极度耗时的，在小区域内并且空间分辨率较差。实际上，这些技术无法扫描空间位置，并且只能收集光谱数据。为了解决这一限制，在这项研究中引入了HSI。HSI能够同时捕获分析中给定场景的空间和频谱内容。与传统的图像不同，在频谱的可见范围内捕获了三个通道（红色，绿色和蓝色），HSI捕获了数百个频道，不仅涵盖了可见的红外范围（通常为400-2500nm），超出了人类的眼睛。实际上，在高光谱图像中，每个像素的含量是一个矢量阵列，其中包含该像素捕获的材料的光谱响应或指纹。因此，HSI能够在空间位置捕获光谱信息，揭示现场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技术来产生空间图，在该空间图中产生燃料碎屑，在该空间图中可以与非filtile core的燃料核心区分开来，以避免在污泥中重新进行污染范围，以避免重新进行之前的污泥，以避免重新进行之前的销售。基于不同的光谱指纹，也可以预期，HSI可用于进一步表征某些废物类型并在精确的位置内自动检测它们。此外，HSI可以与其他传感器技术结合使用，在该技术可以将HSI用作指导LIBS基于点的伽马射线光谱中子中子测定的基于点的采集的预筛选工具。我们预计，拟议的研究将导致新的且高价的检查技术，这些技术可以支持日本，英国和世界各地的核退役。