Optimizing the small modular reactor core loading with dual-cooled fuel via Henry gas solubility optimization algorithm

Abstract

An investigation into the influence of innovative dual-cooled fuel geometric structural characteristics on critical reactor core parameters, such as temperature reactivity coefficients and convective heat transfer coefficient, is essential for accurately assessing its neutronic and thermohydraulic performance and estimating safety margins. This study calculated the fuel and coolant/moderator temperature reactivity coefficients in the NuScale-type reactor using WIMS-CITATION codes. Additionally, the impact of increasing the size of dual-cooled fuel rods on these coefficients was analyzed. The study also calculated the hot rod convective heat transfer coefficient for proposed fuel rods using a Computational Fluid Dynamics (CFD) and sub-channel model method. The results demonstrated that increasing the internal radius of the fuel decreases the temperature reactivity feedback coefficient of fuels, while the feedback coefficient of coolant exhibits a parabolic trend. All proposed fuel rods exhibited negative coolant and fuel temperature reactivity coefficients, and increasing the internal radius resulted in a reduction in the convective heat transfer coefficient. Furthermore, the study explored the use of Artificial Neural Network (ANN) and Gene Expression Programming (GEP) models to develop an optimal method with low computational cost. Based on statistical indicators, the ANN was found to outperform GEP. Finally, the designed ANN, coupled with the Henry Gas Solubility Optimization (HGSO) algorithm, was employed to determine the optimal dual-cooled fuel based on desired parameters.