| dc.contributor.author | Torun, Yunis | |
| dc.contributor.author | Akkoyun, Serkan | |
| dc.date.accessioned | 2022-05-13T11:28:31Z | |
| dc.date.available | 2022-05-13T11:28:31Z | |
| dc.date.issued | 2021 | tr |
| dc.identifier.citation | Serkan Akkoyun, Yunis Torun,
Neuro-fuzzy modeling of deformation parameters for fusion-barriers,
Nuclear Engineering and Technology,
Volume 53, Issue 5,
2021,
Pages 1612-1618,
ISSN 1738-5733,
https://doi.org/10.1016/j.net.2020.10.017.
(https://www.sciencedirect.com/science/article/pii/S1738573320309104)
Abstract: The fusion-barrier distribution is very sensitive to the structure of the colliding nuclei such as nuclear quadrupole and hexadecapole deformation parameters and their signs. If the nuclei that enter the fusion reaction are deformed, the barrier problem becomes complicated. Therefore the deformation parameters are taken into account in the calculations. In this study, Neuro-Fuzzy approach, ANFIS, method has been used for the estimation of ground-state quadrupole (ε2) and hexadecapole (ε4) deformation parameters for the nuclei. According to the results, the method is suitable for this task and one can confidently use it to obtain the data that is not available in the literature.
Keywords: Deformation deformation; Nilsson parameters; Artificial intelligence; ANFIS | tr |
| dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S1738573320309104?via%3Dihub | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12418/13053 | |
| dc.description.abstract | The fusion-barrier distribution is very sensitive to the structure of the colliding nuclei such as nuclear quadrupole and hexadecapole deformation parameters and their signs. If the nuclei that enter the fusion reaction are deformed, the barrier problem becomes complicated. Therefore the deformation parameters are taken into account in the calculations. In this study, Neuro-Fuzzy approach, ANFIS, method has been used for the estimation of ground-state quadrupole () and hexadecapole () deformation parameters for the nuclei. According to the results, the method is suitable for this task and one can confidently use it to obtain the data that is not available in the literature. | tr |
| dc.language.iso | eng | tr |
| dc.publisher | Science Direct | tr |
| dc.relation.isversionof | 10.1016/j.net.2020.10.017 | tr |
| dc.rights | info:eu-repo/semantics/closedAccess | tr |
| dc.subject | Deformation deformationNilsson parametersArtificial intelligenceANFIS | tr |
| dc.title | Neuro-fuzzy modeling of deformation parameters for fusion-barriers | tr |
| dc.type | article | tr |
| dc.relation.journal | NUCLEAR ENGINEERING AND TECHNOLOGY | tr |
| dc.contributor.department | Mühendislik Fakültesi | tr |
| dc.contributor.authorID | 0000-0002-6187-0451 | tr |
| dc.contributor.authorID | 0000-0002-6187-0451 | tr |
| dc.identifier.volume | 53 | tr |
| dc.identifier.issue | 5 | tr |
| dc.identifier.endpage | 1618 | tr |
| dc.identifier.startpage | 1612 | tr |
| dc.relation.publicationcategory | Uluslararası Hakemli Dergide Makale - Kurum Öğretim Elemanı | tr |
