Effect of different cross-section data sets on reflectors of a typical material test research reactor

Optimization of the TiO2 nanofluid as a coolant in the VVER-1000 nuclear reactor based on the thermal reactivity feedback coefficients via the genetic algorithm

The purpose of this study is to display the neutronic simulation of nanofluid application to reactor core. The variations of VVER-1000 nuclear reactor primary neutronic parameters are investigated by using different volume fraction of nanofluid as coolant. The effect of using nanofluid as coolant on reactor dynamical parameters which play an important role in the dynamical analysis of the reactor and safety core is calculated. In this paper coolant and fuel temperature reactivity coefficients in a VVER-1000 nuclear reactor with nanofluid as a coolant are calculated by using various volume fractions and different sizes of TiO2 (Titania) nanoparticle. For do this, firstly the equivalent cell of the hexagonal fuel rod and the surrounding coolant nanofluid is simulated. Then the thermal hydraulic calculations are performed at different volume fractions and sizes of the nanoparticle. Then, using WIMS and CITATION codes, the reactor core is simulated and the effect of coolant and fuel temperature changes on the effective multiplication factor is calculated.

For doing optimization, an artificial neural network is trained in MATLAB using the observed data. The different sizes and various volume fractions are inputs, fuel and coolant temperature reactivity coefficients are outputs. The optimal size and volume fraction is determined using the neural network by implementing the genetic algorithms. In the optimization, volume fraction of 7% and size 77 nm are optimal values.