Optimal design of a VVER-1000 nuclear reactor core with dual cooled annular fuel based on the reactivity temperature coefficients using Thermal hydraulic and neutronic analysis by implementing the genetic algorithms. (1st December 2020) - Kianpour, R.; Ansarifar, G.R.; Fathi, M.
Highlights: Optimal design of a Nuclear Reactor with Annular Fuel is presented. Reactivity temperature coefficients are calculated with changes of internal radius. Effects of the fuel inner radius on the reactivity coefficients are investigated. Abstract: One of the approaches which can help the enhancement of a reactor power is changing its fuel geometry. For this purpose, as well as decreasing the maximum fuel temperature in PWR reactors, the technology of annular fuels with ability of internal and external cooling shows its importance and has been considered widely. Such fuels are investigated in western PWR and VVER-1000 reactors. Hence, in this study, this fuel in VVER-1000 reactors are considered and studied thoroughly. In this paper, fuel and coolant reactivity temperature coefficients are calculated for a VVER-1000 Nuclear Reactor with Dual Cooled Annular Fuel with changes of internal radius and different power levels and effects of the fuel internal radius on the reactivity temperature coefficients are investigated. By analyzing the fuel internal radius changes in a specific range in the neutronic code, the effects of effective multiplication factor are investigated. For purpose of data fitting, an artificial neural network is trained using the observed data. The input consists of different internal and external radiuses, outputs consist of pitch, fuel and coolant reactivity temperature coefficients. Finally, the optimal geometry of fuel is determined using the neural network by implementing the genetic algorithms based on these dynamic-coefficients. In the optimization process, it has been shown that having an internal radius 2.67 mm and external radius 6.95 mm provides an optimal geometry. Also, validation of the designed artificial neural network and genetic algorithm has been done using neutronic and Thermal hydraulic calculations.