| dc.creator |
KAPLAN, Abdullah |
|
| dc.creator |
ŞEKERCİ, Mert |
|
| dc.creator |
ÜNCÜ, YİĞİT ALİ |
|
| dc.creator |
Özdoğan, Hasan |
|
| dc.date |
2023-02-01T00:00:00Z |
|
| dc.date.accessioned |
2025-02-25T10:40:22Z |
|
| dc.date.available |
2025-02-25T10:40:22Z |
|
| dc.identifier |
e41d35ec-408b-40b4-895c-0a1c91e48359 |
|
| dc.identifier |
10.1016/j.apradiso.2022.110609 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/e41d35ec-408b-40b4-895c-0a1c91e48359/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/101721 |
|
| dc.description |
Prediction of neutron-induced reaction cross-sections at around the 14.5 MeV neutron energy is crucial to calculate nuclear transmutation rates, nuclear heating, and radiation damage from gas formation in fusion reactor technology In this research, the new approach of (n,α) reaction cross-section is presented. It has been assessed by utilizing the artificial neural network (ANN) when compared to more advanced algorithms, the Levenberg-Marquardt algorithm-based ANN can be exceedingly fast. The correlation coefficients for a training R-value of 0.99283, a validation R-value of 0.991190, a testing R-value of 0.97337, and an overall R-value of 0.98515 demonstrate that Levenberg-Marquardt algorithm-based ANN is well suited for this purpose. The obtained results were compared to theoretical calculations of TALYS 1.95 nuclear code. As a consequence, it has been demonstrated that the ANN model can be used to determine the systemic study for (n, α) reaction cross-sections. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Estimations for (n,α) reaction cross sections at around 14.5MeV using Levenberg-Marquardt algorithm-based artificial neural network |
|
| dc.type |
info:eu-repo/semantics/article |
|