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 design. The new software was developed implementing novel GAs, and it was applied to show their capabilities by presenting an example solution...
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Autor*in:	Żurkowski Wojciech [verfasserIn] Sawicki Piotr [verfasserIn] Kubiński Wojciech [verfasserIn] Darnowski Piotr [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	genetic algorithms nuclear reactor optimization sodium-cooled fast reactor

Übergeordnetes Werk:	In: Nukleonika - Sciendo, 2015, 66(2021), 4, Seite 139-145
Übergeordnetes Werk:	volume:66 ; year:2021 ; number:4 ; pages:139-145

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.2478/nuka-2021-0021

Katalog-ID:	DOAJ018339034

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language	English
source	In Nukleonika 66(2021), 4, Seite 139-145 volume:66 year:2021 number:4 pages:139-145
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author	Żurkowski Wojciech
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topic_title	Application of genetic algorithms in optimization of SFR nuclear reactor design genetic algorithms nuclear reactor optimization sodium-cooled fast reactor
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title	Application of genetic algorithms in optimization of SFR nuclear reactor design
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title_full	Application of genetic algorithms in optimization of SFR nuclear reactor design
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title_sort	application of genetic algorithms in optimization of sfr nuclear reactor design
title_auth	Application of genetic algorithms in optimization of SFR nuclear reactor design
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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Application of genetic algorithms in optimization of SFR nuclear reactor design
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Application of genetic algorithms in optimization of SFR nuclear reactor design

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