Artificial Intelligence Improvement of Rate Theory Models

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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.