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

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。为了确保我们的反应堆安全、可靠和经济地运行，我们对制造它们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撞击由锆合金制成的压力管时，它们会将原子从正常位置移位，从而产生在其他条件下无法观察到的纳米级微观结构。此外，由于热压管与水接触，它们倾向于吸收氢，形成氢化锆，这也改变了材料的机械性能。表征辐射诱导的微观结构和氢化物需要非常高分辨率的仪器，包括透射电子显微镜，其分辨率为几纳米（即几个原子宽）。

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.