A System-Level Framework For Fatigue Life Prediction of PWR Pressurizer-Surge-Line Nozzle under Design-Basis Loading Cycles. A complete tensile test based material properties database and preliminary results on 3D weld process modeling, thermal-mechanical stress analysis and environmental fatigue testing

Mohanty, Subhasish; Park, Jae Phil; Listwan, Joseph

doi:10.2172/1571258

Cited by 2 publications

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“…Note that all the yield and hardening parameters are based on 0.05% offset-strain rather than the conventional 0.2% offset-strain yield limit. This better captures the plasticity region of a component [8]. Figure 2.6 shows the comparison of expansion coefficients for different material used in the model.…”

Section: Finite Element Model For Thermal-mechanical Stress Analysismentioning

confidence: 99%

“…Figure 2.6 shows the comparison of expansion coefficients for different material used in the model. Note that all material properties and the expansion coefficients used in the FE models are directly based on our earlier tensile tests [8], which improve accuracy in the FE model for future experimental validation. We assume the manufacturing related residual stresses were incorporated in the FE model by using the material properties directly estimated based on the tensile test data of associated weld metals.…”

Section: Finite Element Model For Thermal-mechanical Stress Analysismentioning

confidence: 99%

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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

Mohanty¹

2022

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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...

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Section: Finite Element Model For Thermal-mechanical Stress Analysismentioning

confidence: 99%

Section: Finite Element Model For Thermal-mechanical Stress Analysismentioning

confidence: 99%

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

Mohanty¹

2022

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“…Figure 3.8 shows an example of laboratory test-based data that can be used for DT model development. This figure shows the fatigue cycle versus observed cyclic strain under thermal-mechanical fatigue loading [28]. Although the laboratory data presented here were obtained using a truncated cyclic period (of approximately 25 seconds), actual reactor loading cycles could vary from days to months or even more than a year (e.g.…”

Section: Heterogenous Source and Timescale Of Datamentioning

confidence: 99%

“…Figure 3. 8 Example of laboratory test-based measured cycle versus observed strain under fatigue loading, with cyclic period of 25 seconds [28]. The time scale of measurements can be significantly different from the time scale for ultrasound sensor measurements shown in Figure 3.5.…”

Section: Heterogenous Source and Timescale Of Datamentioning

confidence: 99%

Cited by 2 publications

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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

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

Physics-Infused AI/ML Based Digital-Twin Framework for Flow-Induced-Vibration Damage Prediction in a Nuclear Reactor Heat Exchanger

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