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。这种能源是无碳的，对环境的影响很小。为了确保反应堆的安全、可靠和经济运行，我们对制造反应堆的结构材料进行定期检查。值得注意的是，当流经反应堆的中子击中由锆合金制成的压力管时，它们会将原子移出正常位置，产生在其他条件下没有观察到的纳米级微结构。此外，由于热压力管与水接触，它们往往会吸收氢气，形成氢化锆，这也会改变材料的机械性能。表征辐射诱导的微结构和氢化物需要非常高分辨率的仪器，包括具有几个纳米(即几个原子宽)分辨率的透射式电子显微镜。然而，分析这些仪器产生的显微图像是一项乏味、耗时的任务。目前，加拿大核实验室(CNL)为其客户(包括加拿大公用事业公司)提供这种描述。熟练的科学家花费了无数个小时艰苦地手动分析这些图像，因为这些图像往往太过嘈杂，标准的图像滤镜没有多大帮助。该联盟的工业合作伙伴关系旨在开发神经网络，以在很大程度上实现图像分析的自动化，并让科学家专注于解决材料科学领域的其他重要挑战。