Rifai, Hanan; Kabach, Ouadie; Sadoune, Zouhair; Chakir, El Mahjoub; Uzun, Sinem; Amsil, Hamid; Banni, Fadi El

This study presents a comprehensive neutronic and safety evaluation of different thorium-based fuel compositions (Th-235U)O2, (Th-233U)O2, and a new proposed composition, namely, (Th-233U-235U)O2 fuel, compared to conventional UO2 in solid and dual-cooled annular assembly configurations in the context of Small Modular Reactors (SMR) applications. Results demonstrate that (Th-233U)O2 achieves the highest cycle length in both solid configurations, significantly exceeding UO2 performance. The proposed, however, (Th-233U-235U)O2 composition exhibits favorable safety characteristics, including enhanced negative temperature reactivity coefficients and favorable kinetic parameters. Annular fuel configurations also demonstrate superior thermal performance with centerline temperature reductions of approximately 50 °C compared to solid configurations and consistently lower power peaking factors throughout burnup. The findings indicate that thorium-based fuels, particularly (Th-233U-235U)O2 in dual-cooled annular configuration, offer compelling advantages for SMR applications, including extended fuel cycles, improved safety characteristics, enhanced proliferation resistance, and superior thermal performance, supporting their viability as sustainable alternatives to conventional uranium-based fuels.

UZUN, Sinem

This study comprehensively evaluated five widely used clad materials in nuclear reactors: Zircaloy, FeCrAl (Iron-chromium-aluminium), TiC (titanium carbide), ZrC (zirconium carbide), and SiC (silicon carbide). Neutronic analyses conducted using OpenMC revealed that ZrC achieved the highest keff value of 1.34 with uranium-based fuel, indicating superior neutron economy. Zircaloy performed effectively with thorium-based fuel, achieving a keff value of 1.32. Radiation shielding analyses using Phy-X software showed that ZrC exhibited the lowest Half-Value Layer and Tenth-Value Layer values at 15 MeV, measuring 2.94 cm, indicating its superior radiation absorption capability with a thinner layer. Conversely, SiC required the thickest layer for effective shielding due to its highest HVL and TVL values. Modal analysis confirmed the mechanical suitability of all examined clad materials, with frequency values falling within acceptable limits, ensuring structural integrity and vibration resistance without compromising reactor safety. In conclusion ZrC emerged as the optimal clad material due to its superior neutronic performance with uranium-based fuel, exceptional radiation shielding capabilities, and mechanical reliability. Zircaloy also proved effective for thorium-based applications, highlighting the importance of selecting clad materials that enhance both reactor performance and safety.

Uzun, Sinem; Yildiz, Eyyüp; Arslantaş, Hatice

This study emphasizes how important accurate prediction of channel temperatures in nuclear reactors is for safety and operational efficiency. While traditional methods require long and complex processes such as kernel modeling and mathematical simulations, artificial neural networks (ANN) provide more efficient predictions by accelerating this process. The superior ability of ANNs to process large data sets is intended to demonstrate that this study will provide a valuable alternative compared to conventional methods and increase the accuracy of reactor temperature predictions. In this study, the training performances of Artificial Neural Network (ANN) developed to determine the nuclear reactor channel temperature with different hyperparameter combinations were analysed. It was conducted several experimental studies to assess the influence of hyperparameters on our model for nuclear reactor parameter data prediction. The training and validation results indicates that learning rate, hidden layer sizes and number have critical effects for the more precisive prediction. It was observed that models with a learning rate of 0.05 and 0.5 achieved successful learning with less fluctuation in training and validation errors. When looking at hidden layer sizes, networks with 32 and 64 neurons performed better than networks with 16 neurons. For the test phase our model can successfully predict data with slight error margin. As a result, we demonstrated that neural networks are a powerful tool in nuclear reactor channel temperature prediction through our proposed model.