طراحي نوترونيك و ترموهيدروليك يك رآكتور ماژولار كوچك مبتني بر سوخت‌هاي مقاوم در برابر حادثه خنك‌شونده از داخل و خارج و بررسي اثر تغييرات تركيب سوخت بر انحراف توزيع توان محوري به‌منظور ملاحظات تعقيب بار

Small Modular Reactor (SMR) , Internally an‎d Externally Cooled Annular Fuel , Accident-Tolerant Fuel (ATF) , Artificial Neural Network (ANN) , Reactor Core Optimization

Neutronic an‎d Thermal-Hydraulic Design of a Small Modular Reactor Based on Dual-cooled Accident Tolerant Fuels an‎d Investigating the Effect of Fuel Composition Changes on Axial Offset for Load Following Considerations

مهندسي هسته‌اي

This study has been conducted in two distinct phases, each addressing a different aspect of the performance an‎d design of small modular reactors (SMRs) employing an innovatively cooled fuel configuration with both internal an‎d external cooling pathways. In the first phase, the neutronic an‎d thermal-hydraulic performance of internally an‎d externally cooled fuel was investigated within the context of SMR design. The primary focus was placed on eva‎luating the influence of the fuelʹs geometrical characteristics on critical reactor parameters, such as the temperature reactivity feedback coefficients an‎d convective heat transfer coefficient. Initially, the WIMS-CITATION code system was employed to calculate the temperature reactivity feedback coefficients of both fuel an‎d coolant for a NuScale-type reactor. The impact of increasing the inner radius of the fuel rods on these coefficients was subsequently analyzed. The results indicated that enlarging the inner radius of the fuel led to a reduction in the fuel temperature coefficient an‎d introduced a parabolic trend in the coolant feedback coefficient. All proposed configurations exhibited negative temperature feedback coefficients for both the fuel an‎d the coolant. Furthermore, it was observed that an increase in the inner radius resulted in a decrease in the convective heat transfer coefficient in the hot rod. These analyses were carried out using computational fluid dynamics (CFD) modeling. To reduce computational costs while enhancing predictive accuracy, artificial intelligence models, including artificial neural networks (ANNs) an‎d gene expression programming (GEP), were developed. Statistical eva‎luation demonstrated superior performance of the ANN model. Consequently, a hybrid approach combining the ANN an‎d the Henry Gas Solubility Optimization (HGSO) algorithm was employed to determine the optimal fuel geometry. In the second phase of the study, a conceptual design of an accident-tolerant SMR core utilizing U₃Si₂ fuel pellets an‎d FeCrAl cladding was investigated. A total of 4000 different fuel assembly configurations incorporating various arrangements of integral burnable absorbers (IBAs) were modeled. Among these, 50 configurations from each category were selec‎ted an‎d simulated using the MCNP code. The simulation results were used to train an ANN model to enable accurate prediction of the performance of the remaining configurations. Subsequently, 55 reactor core configurations with varying distributions of IBA-containing assemblies were subjected to neutronic analysis. The findings demonstrated negative temperature coefficients, an acceptable power peaking factor (1.58), an‎d a full-power cycle length exceeding 1800 days. These outcomes represent a significant improvement over conventional UO₂-fueled SMRs an‎d are primarily attributed to the higher uranium density in U₃Si₂ fuel, which permits a greater loading of fissile material. The new fuel design, using coupled CFD an‎d neutronic simulations, demonstrated a safe thermal-hydraulic performance. The maximum fuel temperature under normal operating conditions is significantly below the melting point, which reduces the probability of fuel rod melting during accidents. Additionally, the MDNBR factor exceeds stan‎dard safety values an‎d provides a better safety margin compared to similar designs like the NuScale.

4