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

<p>This work presents a demonstrational application of genetic algorithms (GAs) to solve sample optimization problems in the generation IV nuclear reactor core design. The new software was developed implementing novel GAs, and it was applied to show their capabilities by presenting an example solution of two selected problems to check whether GAs can be used successfully in reactor engineering as an optimization tool. The 3600 MWth oxide core, which was based on the OECD/NEA sodium-cooled fast reactor (SFR) benchmark, was used a reference design [<xref ref-type="bibr" rid="j_nuka-2021-0021_ref_001">1</xref>]. The first problem was the optimization of the fuel isotopic inventory in terms of minimizing the volume share of long-lived actinides, while maximizing the effective neutron multiplication factor. The second task was the optimization of the boron shield distribution around the reactor core to minimize the sodium void reactivity effect (SVRE). Neutron transport and fuel depletion simulations were performed using Monte Carlo neutron transport code SERPENT2. The simulation resulted in an optimized fuel mixture composition for the selected parameters, which demonstrates the functionality of the algorithm. The results show the efficiency and universality of GAs in multidimensional optimization problems in nuclear engineering.</p>
</abstract>

This work presents a demonstrational application of genetic algorithms (GAs) to solve sample optimization problems in the generation IV nuclear reactor core design. The new software was developed implementing novel GAs, and it was applied to show their capabilities by presenting an example solution of two selected problems to check whether GAs can be used successfully in reactor engineering as an optimization tool. The 3600 MWth oxide core, which was based on the OECD/NEA sodium-cooled fast reactor (SFR) benchmark, was used a reference design [1]. The first problem was the optimization of the fuel isotopic inventory in terms of minimizing the volume share of long-lived actinides, while maximizing the effective neutron multiplication factor. The second task was the optimization of the boron shield distribution around the reactor core to minimize the sodium void reactivity effect (SVRE). Neutron transport and fuel depletion simulations were performed using Monte Carlo neutron transport code SERPENT2. The simulation resulted in an optimized fuel mixture composition for the selected parameters, which demonstrates the functionality of the algorithm. The results show the efficiency and universality of GAs in multidimensional optimization problems in nuclear engineering.

Application of genetic algorithms in optimization of SFR nuclear reactor design

Nukleonika

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

{"article-title":"Application of genetic algorithms in optimization of SFR nuclear reactor design"}

This work presents a demonstrational application of genetic algorithms (GAs) to solve sample optimization problems in the generation IV nuclear reactor core...

Application of genetic algorithms in optimization of SFR nuclear reactor design

Article Category: Original Paper

Publicado en línea: 25 nov 2021

Páginas: 139 - 145

Recibido: 19 ene 2021

Aceptado: 21 abr 2021

DOI: https://doi.org/10.2478/nuka-2021-0021

Palabras clave
Genetic algorithms, Nuclear reactor, Optimization, Sodium-cooled fast reactor

© 2021 Wojciech Żurkowski et al., published by Sciendo

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

Application of genetic algorithms in optimization of SFR nuclear reactor design

Article Category: Original Paper

Publicado en línea: 25 nov 2021

Páginas: 139 - 145

Recibido: 19 ene 2021

Aceptado: 21 abr 2021

DOI: https://doi.org/10.2478/nuka-2021-0021

Palabras claveGenetic algorithms, Nuclear reactor, Optimization, Sodium-cooled fast reactor

© 2021 Wojciech Żurkowski et al., published by Sciendo

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

Palabras clave
Genetic algorithms, Nuclear reactor, Optimization, Sodium-cooled fast reactor