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Accessible Unlicensed Requires Authentication Published by De Gruyter December 13, 2021

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

Optimierung des TiO2-Nanofluids als Kühlmittel im Reaktor VVER-1000 auf der Grundlage der Rückkopplungskoeffizienten der thermischen Reaktivität mit Hilfe eines genetischen Algorithmus
R. Kianpour and G. R. Ansarifar
From the journal Kerntechnik

Abstract

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.

Abstract

Das Ziel dieser Untersuchungen ist die Darstellung von Neutronenberechnungen bei der Anwendung von Nanofluiden im Reaktorkern. Die Variationen der primären Parameter des WWER-1000-Kernreaktors werden durch die Verwendung verschiedener Volumenanteile von Nanofluid als Kühlmittel untersucht. Die Auswirkungen der Verwendung von Nanofluid als Kühlmittel auf die dynamischen Parameter des Reaktors, die eine wichtige Rolle bei der dynamischen Analyse des Reaktors und des Sicherheitskerns spielen, werden berechnet. In dieser Arbeit werden die Reaktivitätskoeffizienten von Kühlmittel und Brennstofftemperatur in einem WWER-1000-Kernreaktor mit Nanofluid als Kühlmittel unter Verwendung verschiedener Volumenanteile und unterschiedlicher Größen von TiO2 (Titandioxid)-Nanopartikeln berechnet. Dazu wird zunächst die Äquivalenzzelle aus dem hexagonalen Brennstab und dem umgebenden Kühlmittel-Nanofluid simuliert. Dann werden die thermohydraulischen Berechnungen für verschiedene Volumenanteile und Größen der Nanopartikel durchgeführt. Anschließend wird mit den Codes WIMS und CITATION der Reaktorkern simuliert und die Auswirkung von Kühlmittelund Brennstofftemperaturänderungen auf den effektiven Multiplikationsfaktor berechnet. Für die Optimierung wird ein künstliches neuronales Netz in MATLAB anhand der beobachteten Daten trainiert. Die verschiedenen Größen und Volumenanteile sind Eingaben, die Reaktivitätskoeffizienten für die Brennstoffund Kühlmitteltemperatur sind Ausgaben. Die optimale Größe und der optimale Volumenanteil werden mithilfe des neuronalen Netzes durch die Anwendung genetischer Algorithmen bestimmt. Bei der Optimierung sind ein Volumenanteil von 7% und eine Größe von 77 nm die optimalen Werte.

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Received: 2021-02-06
Published Online: 2021-12-13
Published in Print: 2021-12-13

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