Development of artificial neural networks to analyze micrographs of zirconium-based alloys and hydrides for nuclear power applications

开发人工神经网络来分析核电应用中锆基合金和氢化物的显微照片

• 批准号: 549836-2020
• 负责人: Béland, LaurentKarimLK
• 金额: $ 1.48万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759786
• 关键词: Development; artificial; neural; networks; analyze

Nuclear power provides 60% of Ontario's electricity. This energy source is carbon-free and has a small environment footprint. In order to ensure safe, reliable, and economical operation of our reactors, the structural materials of which they are made are regularly inspected. Notably, as the neutrons flowing through the reactor hit the pressure tubes made of zirconium alloys, they displace atoms out of their normal positions, creating nanoscale microstructure that is not observed in other conditions. Also, since the hot pressure tubes are in contact with water, they tend to pick-up hydrogen, and form zirconium hydrides, which also changes the material's mechanical properties. Characterizing radiation-induced microstructures and hydrides necessitates very high resolution instruments, including transmission electron microscopes, which have a resolution of a couple of nanometers (i.e. a few atoms wide). However, analyzing the micrographs produced by these instruments is a tedious, time-consuming task. Currently, the Canadian Nuclear Laboratories (CNL) perform this characterization for the benefit of its clients, including Canadian utilities. Skilled scientists spend countless hours painstakingly analysing these images manually, since the images are often too noisy for standard image filters to be of much help. This Alliance industrial partnership aims at developing neural networks to largely automate the image analysis, and let scientist focus on solving other important challenges in the field of materials science.

核电提供了安大略省60％的电力。该能源无碳，环境足迹较小。为了确保我们的反应堆的安全，可靠和经济的操作，可以定期检查它们制造的结构材料。值得注意的是，当中子流过反应堆击中由锆合金制成的压力管时，它们将原子置于正常位置，从而产生了在其他条件下未观察到的纳米级微结构。同样，由于热压管与水接触，因此它们倾向于拾起氢，并形成锆氢化物，这也改变了材料的机械性能。表征辐射诱导的显微结构和氢化物需要很高的分辨率仪器，包括透射电子显微镜，它们具有几个纳米的分辨率（即几个原子宽）。但是，分析这些仪器产生的显微照片是一项繁琐的任务。目前，加拿大核实验室（CNL）执行此特征，以使其客户受益，包括加拿大公用事业。熟练的科学家花了无数小时的时间手动分析这些图像，因为这些图像通常太嘈杂了，以至于标准图像过滤器无法提供很多帮助。这种联盟工业伙伴关系旨在开发神经网络以在很大程度上自动化图像分析，并让科学家专注于解决材料科学领域的其他重要挑战。