Artificial Intelligence Improvement of Rate Theory Models

安大略的CANDU核反应堆产生了60%以上的电力，在一个支持76，000个加拿大工作岗位的行业中，二氧化碳的产量可以忽略不计。 CANDU核电厂（NPP）的主要财务和CO2成本在其初始建造期间产生，因此通过实现准确的维护预测来最大限度地延长寿命，从而提供经济和环境效益。核电厂的安全运行取决于结构材料在高温、腐蚀性和辐射等恶劣环境中的性能。在役材料的一个主要老化机制是辐射诱发偏析（RIS）。RIS是溶质元素在材料中的空间重新分布到特定位置（例如晶界）-这导致对其他降解机制（例如应力腐蚀开裂）的敏感性。 虽然已经收集了关于放射性同位素信息系统的实验数据，但由于处理放射性材料的限制，数据的范围有限。 因此，需要模型来弥补实验数据的局限性，并帮助进行预测。 然而，目前，我们的模型是有限的，缺乏知识的基本材料参数，需要作为输入的模型。 该提案的目标是开发一个物理信息的人工智能计算平台，以应用于RIS。 使用基于物理的模型（速率理论扩散）和分析现有实验数据的人工智能方法的组合，我们将确定用于模型输入的优化材料参数。这将直接支持改进核电厂部件寿命预测的努力。

Ontario's CANDU nuclear reactors generate more than 60% of its electricity, with negligible CO2 production in an industry supporting 76,000 Canadian jobs. A major financial and CO2 cost of CANDU nuclear power plants (NPPs) arises during their initial build, hence maximizing lifetimes by enabling accurate maintenance predictions provides economic and environmental benefits. Safe operation of an NPP depends on the performance of structural materials in a harsh environment of high temperatures, corrosive conditions and radiation. One major aging mechanism of materials in-service is radiation induced segregation (RIS). RIS is the spatial redistribution of solute elements to specific locations in a material (e.g. grain boundaries) - this then leads to susceptibility to other degradation mechanisms such as stress corrosion cracking. While experimental data has been collected on RIS, the breadth of data is limited by the constraints of dealing with radioactive materials. Therefore models are required, to bridge the limits of experimental data and assist in making predictions. However at present, our models are limited by a lack of knowledge of fundamental material parameters that are required as inputs to the models. The goal of this proposal is to develop a physics-informed artificial-intelligence computational platform for application to RIS. Using a combination of a physics-based model (rate theory diffusion) and an Artificial Intelligence approach analysing existing experimental data we will determine optimised material parameters for model input. This will directly support efforts to improve lifetime prediction of nuclear power plant components.