Investigation of the Al2O3/ Water Nano-Fluid Concentration and Size Effects on the Neutronics and Thermal-Hydraulic Parameters in the VVER-1000 Nuclear Reactor Using Artificial Neural Networks

Nowadays, many researches have been done to improve the efficiency of the nuclear power plants, one of which is the use of the dual cooled annular fuel which is an internally and externally cooled annular fuel with many advantages in heat transfer characteristics. Also, some studies have suggested that the usage of the nanoparticles in a fluid as nano-fluid can provide dramatic improvements in the thermal properties of fluid. Howere, the  usage of neutronics and the thermal hydraulic codes in order to investigate the nano-fluid effects in the nuclear reactors is complex, uneconomical and time consuming. Therefore, in this paper, to investigate the nano-fluid effects on the important parameters of the VVER-1000 nuclear reactor with dual-cooled annular fuel, effects of Al2O3/ water nano-fluid concentration and size on neutronics and the thermal-hydraulic parameters in the VVER-1000 nuclear reactor are investigated using a proper Artificial Neural Network. Results show that the trained Neural Network has good convergence and accuracy in determination of the neutronics and the thermal-hydraulic parameters. Using the presented Neural Network, important reactor parameters can be determined without neutronics and the thermal hydraulic codes, thus saving time.