Machine learning-based noise diagnostics for water-cooled SMRs: proof of principle on 2-dimensional systems

This study explores a core monitoring approach for two-dimensional Small Modular Reactors (SMRs) using neutron noise analysis and machine learning (ML) methods. Absorber of Variable Strength (AVS) perturbations are simulated in the frequency domain to analyze reactor noise behavior differences between large reactors and SMRs. It is demonstrated that SMRs exhibit stronger point-kinetic characteristics, complicating perturbation diagnosis. Thermal-group neutron noise is found to carry more diagnostic information than fast-group neutron noise. This makes thermal-group neutron noise more effective for localizing perturbations. A convolutional neural network (CNN) is trained on a dataset that contains only one or two AVS sources per sample. Despite this limited training dataset, the model can accurately localize up to 10 sources in a sample. The results demonstrate the model's strong generalization capability and high nodal accuracy. To address sparse detector scenarios, a two-stage pipeline is designed to reconstruct full reactor noise fields from limited data points prior to source localization. The pipeline demonstrates effective reconstruction and localization with 50 % detector coverage, accurately capturing both global and local noise components. For reduced instrumentation scenarios of 11 %, 6 %, and 3 % coverage, the model retains reasonable performance, with proximity-based metrics indicating robust localization capabilities. The results highlight the importance of strategic detector placement to balance global and local noise components for effective anomaly detection. The research demonstrates that ML techniques can enhance neutron noise analysis, even under limited data availability. This work contributes to enhancing the safety and operational reliability of SMRs, emphasizing the importance of advanced monitoring methods and data-informed instrumentation layouts to optimize performance, safety, and efficiency.