Development and application of an artificial neural network for quick estimation of debris bed quenchingLim, Sokh Kong (Avtor) Uršič, Mitja (Mentor) Suikkanen, Heikki (Komentor) nuclear safetysevere accidentdebris bed coolabilityquenching modelingartificial neural networkIn the event of a severe nuclear accident, loss of core cooling can lead to core degradation and melting, which could finally result in the formation of a porous debris bed outside the reactor pressure vessel. Ensuring debris bed coolability through top-flooding is important for preventing the progression of molten core-concrete interaction and the associated generation of non-condensable gases. Although deterministic thermal-hydraulic codes can simulate debris bed quenching, their high computational time limits their application to rapid severe accident assessments. This thesis therefore investigates an Artificial Neural Network as a computationally efficient approach for estimating quenching time. Top-flooding experiments from the FLOAT facility (IKE, Germany), tests F.32 and F.33, were evaluated to investigate the effect of bottom air injection. Experiments were simulated using the MC3D code (ASNR, France). A two-dimensional axisymmetric computational model was developed, and a coarse mesh configuration with a uniform initial debris temperature was selected based on sensitivity analysis. Using Latin Hypercube Sampling, 38000 MC3D simulations were generated by varying the initial debris bed temperature, water volumetric flow rate, and air mass flow rate. The Artificial Neural Network was implemented using PyTorch, with its hyperparameters optimized using Optuna. An ensemble of five independently trained Artificial Neural Network models was used to improve prediction robustness. The results confirmed that air flow increases the total quenching time. Correlation analysis showed that the initial debris bed temperature had the strongest positive correlation with total quenching time. The Artificial Neural Network ensemble achieved a coefficient of determination of 0.903 on the unseen test dataset. While an individual MC3D simulation required several hours of computational time, the trained Artificial Neural Networks predict quenching time in less than 1 second.20262026-10-07 07:15:26Magistrsko delo/naloga189418VisID: 168192COBISS_ID: 294090755sl