Design of the CAREM nuclear reactor core with dual cooled annular fuel and optimizing the thermal-hydraulic, natural circulation, and neutronics parameters

Abstract

The small modular nuclear reactor (SMR) with annular fuel which is cooled internally and externally has the potential to maintain high power density with a sufficient safety margin. The benefit of the dual cooled annular fuel design is that heat can be transferred to the coolant from both the outer and inner surfaces.In this paper, design of the Argentina small modular reactor (CAREM) core with hexagonal assemblies using dual-cooled annular fuels with internal and external cooling is presented. Neutronics and thermal-hydraulic parameters are evaluated and analyzed by changing internal fuel radius. For this purpose, at the first, the dual-cooled annular fuel under clean and cold conditions is modeled and the effective multiplication factor has been calculated for different inner clad diameter. Indeed, the internal and external radius have been changed in such a way to maintain the reactor under moderated.Then, these annular fuels under full power conditions are modeled and power peaking factor has been calculated. Finally, natural circulation parameters are performed for a simulated fuel rod in the hot channel using computational fluid dynamics (CFD) simulation codes. These results are compared with the conventional CAREM reactor. One of the most prominent advantages of the annular fuels is the ability to make softer neutrons in which reduce maximum power peaking factor and improves fuel management and safety parameters in the reactor core.For data fitting, an artificial neural network is trained using the observed data. The input consists of different internal and external radiuses and output consists of fuel rods’ pitch, thermal-hydraulic and neutronic parameters. Finally, the optimal geometry of fuels is determined using the neural network by implementing the genetic algorithms.Indeed, developed Artificial Neural Network (ANN) utilizing the obtained data, predicts the thermal-hydraulic and neutronic parameters of the CAREM reactor core with dual-cooled annular fuels. Presented optimization algorithm, which has a significant ability to attain the best solutions, also determines the optimal values of natural circulation parameters (Vmax/Vavg, Vout-Vin, and pressure drops), heat transfer coefficients, MDNBR, RPPF, and excess reactivity of the reactor. Also, the designed artificial neural network and genetic algorithm has been validated using neutronic and thermal hydraulic calculations. Results indicate that by using annular fuels, better reactor safety and thermal efficiency are achieved.