Hybrid AI-ML and FE-based Digital Twin Predictive Modeling Framework for a PWR Coolant System Components: Updates on Multi-Time-Series-3D-Location Dependent Usages Factor Prediction

Abstract

The long-term operation (LTO) of nuclear power plants (NPP) beyond their original design life of 40 years can lead to more material damage associated with cyclic fatigue under thermal-mechanical loading cycles and associated long-term exposure of reactor material to the deleterious reactor-coolant environments. However, under this LTO condition, the reactor components can still safely operate but may require more frequent Nondestructive Evaluation (NDE) of reactor components. Requiring frequent NDE inspections may lead to frequent NPP shutdowns which can lead to power outages and additional NDE inspection cost-related economic loss. The economic loss can be minimized by reducing uncertainty in life estimation of safety-critical pressure boundary components and by implementing a more digital approach such as using upcoming digital-twin (DT) technology for predicting the structural states (e.g., time and location dependent inside/outside thickness temperature, stress, strain, plastic deformation, etc.) and associated fatigue life of a component in real time. The DT framework is based on limited experimental data, Artificial-intelligence (AI)-Machine-Learning (ML) and multiphysics-computationalmechanics such as finite element-(FE) based models. Given the real-time thermal-hydraulic process measurements from several existing plant sensors, the overall goal of the DT framework is to predict the cumulative usages factors or equivalent fatigue lives in real time and at any random 3D location of the components. This includes inaccessible locations such as inside the thickness or location of a component. This prediction can be at thousands to millions of 3D point clouds or locations like conventional FE-based models, but without running an FE model in real time. Towards this goal, some of the major contributions made during FY22 follow: a. Based on earlier developed system-level FE model of a reactor coolant system (RCS) and associated stress analysis results, we estimated the fatigue lives of different components. Based on these results, we determined that the hot-leg side nozzle of surge line can be an issue, particularly for long-term operation of nuclear reactors. The simulated component-level strain profile (under realistic multi-axial multi-physics connected system boundary conditions) can guide the selection of appropriate test inputs for conducting laboratory-scale environmental-assistedfatigue (EAF) tests for further evaluating the fatigue life of a component, which is an objective of future work. These results are geometry-specific and qualitative. But since most of NPPs have very similar configurations, we can expect similar qualitative results. Nevertheless, the reported results are representative and can be used as a guideline to focus NDE-related inspections for a specific region rather than the entire RCS. Additionally, the resulting FE-simulated structural states can be used as virtual sensor data for training the AI-ML based data-driven models of the overall DT framework.b. A preliminary MySQL-based datab...

Light Water Reactor Sustainability: Program Materials Research Pathway (FY22 Technical Program Plan)

“…Based on these results, it was determined that the hot-leg side nozzle of surge line can be an issue, particularly for long-term operation of nuclear reactors [34]. The simulated component-level strain profile (under realistic multi-axial multi-physics connected system boundary conditions) can guide the selection of appropriate test inputs for conducting laboratory-scale environmental-assisted-fatigue (EAF) tests for further evaluating the fatigue life of a component, which is an objective of future work.…”