<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The study demonstrates an application of genetic algorithms (GAs) in the optimization of the first core loading pattern. The Massachusetts Institute of Technology (MIT) BEAVRS pressurized water reactor (PWR) model was applied with PARCS nodal-diffusion core simulator coupled with GA numerical tool to perform pattern selection. In principle, GAs have been successfully used in many nuclear engineering problems such as core geometry optimization and fuel configuration. In many cases, however, these analyses focused on optimizing only a single parameter, such as the effective neutron multiplication factor (<italic>k</italic><sub>eff</sub>), and often limited to the simplified core model. On the contrary, the GAs developed in this work are equipped with multiple-purpose fitness function (FF) and allow the optimization of more than one parameter at the same time, and these were applied to a realistic full-core problem. The main parameters of interest in this study were the total power peaking factor (PPF) and the length of the fuel cycle. The basic purpose of this study was to improve the economics by finding longer fuel cycle with more uniform power/flux distribution. Proper FFs were developed, tested, and implemented and their results were compared with the reference BEAVRS first fuel cycle. In the two analysed test scenarios, it was possible to extend the first fuel cycle while maintaining lower or similar PPF, in comparison with the BEAVRS core, but for the price of increased initial reactivity.</p>
</abstract>

The study demonstrates an application of genetic algorithms (GAs) in the optimization of the first core loading pattern. The Massachusetts Institute of Technology (MIT) BEAVRS pressurized water reactor (PWR) model was applied with PARCS nodal-diffusion core simulator coupled with GA numerical tool to perform pattern selection. In principle, GAs have been successfully used in many nuclear engineering problems such as core geometry optimization and fuel configuration. In many cases, however, these analyses focused on optimizing only a single parameter, such as the effective neutron multiplication factor (keff), and often limited to the simplified core model. On the contrary, the GAs developed in this work are equipped with multiple-purpose fitness function (FF) and allow the optimization of more than one parameter at the same time, and these were applied to a realistic full-core problem. The main parameters of interest in this study were the total power peaking factor (PPF) and the length of the fuel cycle. The basic purpose of this study was to improve the economics by finding longer fuel cycle with more uniform power/flux distribution. Proper FFs were developed, tested, and implemented and their results were compared with the reference BEAVRS first fuel cycle. In the two analysed test scenarios, it was possible to extend the first fuel cycle while maintaining lower or similar PPF, in comparison with the BEAVRS core, but for the price of increased initial reactivity.

Optimization of the loading pattern of the PWR core using genetic algorithms and multi-purpose fitness function

Nukleonika

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The study demonstrates an application of genetic algorithms (GAs) in the optimization of the first core loading pattern. The Massachusetts Institute of...

Optimization of the loading pattern of the PWR core using genetic algorithms and multi-purpose fitness function

Article Category: Original Paper

Publicado en línea: 25 nov 2021

Páginas: 147 - 151

Recibido: 18 ene 2021

Aceptado: 07 feb 2021

DOI: https://doi.org/10.2478/nuka-2021-0022

Palabras clave
Genetic algorithms, Fuel loading, Loading optimization, PWR, Nuclear reactor technology

© 2021 Wojciech Kubiński et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Optimization of the loading pattern of the PWR core using genetic algorithms and multi-purpose fitness function

Article Category: Original Paper

Publicado en línea: 25 nov 2021

Páginas: 147 - 151

Recibido: 18 ene 2021

Aceptado: 07 feb 2021

DOI: https://doi.org/10.2478/nuka-2021-0022

Palabras claveGenetic algorithms, Fuel loading, Loading optimization, PWR, Nuclear reactor technology

© 2021 Wojciech Kubiński et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Palabras clave
Genetic algorithms, Fuel loading, Loading optimization, PWR, Nuclear reactor technology