<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...

Generation	$\sum_{i} f_{1} * 10^{6}$ \sum\limits_i {{f_1}*{{10}^6}}	Max(f_i) * 10⁶	k_normal	K_void	SVR (pcm)	Error (SVR) (pcm)
1	609.6	50.2	1.00376	1.02770	2422	12
2	681.7	60.6	0.99744	1.02136	2348	12
3	692.7	66.4	0.99703	1.02059	2316	12
4	782.0	70.1	0.99526	1.01849	2292	12
5	795.4	67.1	0.99798	1.02157	2314	12
6	989.3	65.9	0.99654	1.02009	2317	12
7	1048.3	66.8	0.99709	1.02063	2314	12
8	1059.2	74.3	0.99548	1.01850	2270	12
9	1088.5	93.7	0.99222	1.01402	2167	12
10	1177.2	95.1	0.99391	1.01576	2164	12
11	1331.2	101.3	0.99355	1.01508	2134	12

Application of genetic algorithms in optimization of SFR nuclear reactor design

Article Category: Original Paper

Pubblicato online: 25 nov 2021

Pagine: 139 - 145

Ricevuto: 19 gen 2021

Accettato: 21 apr 2021

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

Parole chiave
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.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Plutonium isotope compositions

Selected parameters of the best individual from each generation

Pearson’s linear correlation coefficient values of plutonium isotopes

Application of genetic algorithms in optimization of SFR nuclear reactor design

Article Category: Original Paper

Pubblicato online: 25 nov 2021

Pagine: 139 - 145

Ricevuto: 19 gen 2021

Accettato: 21 apr 2021

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

Parole chiaveGenetic 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.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Plutonium isotope compositions

Selected parameters of the best individual from each generation

Pearson’s linear correlation coefficient values of plutonium isotopes

Parole chiave
Genetic algorithms, Nuclear reactor, Optimization, Sodium-cooled fast reactor