@Article{ee.2026.076328,
AUTHOR = {Fabiano Thulu, Zeyun Wu},
TITLE = {Data-Driven and Physics-Informed Surrogate Modeling for Heat Conduction in the Pressurizer Wall of Pressurized Water Reactors under Severe Accident Scenarios},
JOURNAL = {Energy Engineering},
VOLUME = {123},
YEAR = {2026},
NUMBER = {5},
PAGES = {0--0},
URL = {http://www.techscience.com/energy/v123n5/67104},
ISSN = {1546-0118},
ABSTRACT = {Real-time prediction of temperature distribution in the pressurizer walls of Pressurized Water Reactors (PWRs) during severe accidents, such as Station Blackout (SBO) and Loss-of-Coolant Accident (LOCA) is vital for structural integrity assessment. However, conventional thermal-hydraulic simulations used for such predictions are computationally intensive, limiting their applicability for real-time analysis. This study develops and compares three surrogate models: Polynomial Regression, Deep Neural Network (DNN), and a Physics-Informed Neural Network (PINN). Thermal-hydraulic simulation data generated by RELAP5-3D are integrated with physics-constrained learning techniques to model transient heat conduction in the pressurizer wall. The internal wall temperature evolution is reconstructed using a one-dimensional transient heat conduction mode solved via the Finite Difference Method. The Polynomial Regression model, while achieving a relative high coefficient of determination (<mml:math id="mml-ieqn-1"><mml:msup><mml:mi>R</mml:mi><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mo>=</mml:mo><mml:mn>0.9780</mml:mn></mml:math>), exhibited an average Root Mean Squared Error (RMSE) of 7.50 K and a Maximum Absolute Error (MaxAE) of 193.6 K on unseen test data, indicating limited capability in capturing localized thermal stresses. In contrast, the purely data-drive DNN model demonstrated superior performance, achieving an overall test <mml:math id="mml-ieqn-2"><mml:msup><mml:mi>R</mml:mi><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math> of 0.9996, an RMSE of 1.04 K, and a significantly reduced MaxAE of 24.8 K. Finally, the PINN model yielded an overall physics-based test <mml:math id="mml-ieqn-3"><mml:msup><mml:mi>R</mml:mi><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math> of 0.9874, with an RMSE of 5.66 K, and a MaxAE of 89.7 K. Although the DNN achieves the highest statistical accuracy, the PINN offers a key advantage by enforcing adherence to the governing heat conduction equations. The embedded physical consistency makes PINN a more reliable and trustworthy surrogate for nuclear safety analysis, where maintaining physical fidelity is as critical as numerical accuracy.},
DOI = {10.32604/ee.2026.076328}
}