Publication: Influence of geometrical factors on performance of thermoelectric material using numerical methods
dc.contributor.author | Derebaşı, Naim | |
dc.contributor.author | Eltez, Muhammed | |
dc.contributor.author | Güldiken, Fikret | |
dc.contributor.author | Sever, Aziz | |
dc.contributor.author | Kallis, Klaus | |
dc.contributor.author | Kılıç, Halil | |
dc.contributor.buuauthor | DEREBAŞI, NAİM | |
dc.contributor.buuauthor | Güldiken, Fikret | |
dc.contributor.department | Fen Edebiyat Fakültesi | |
dc.contributor.department | Fizik Bölümü | |
dc.contributor.orcid | 0000-0003-2546-0022 | |
dc.contributor.researcherid | AAI-2254-2021 | |
dc.contributor.researcherid | CRO-8755-2022 | |
dc.date.accessioned | 2024-08-12T07:57:18Z | |
dc.date.available | 2024-08-12T07:57:18Z | |
dc.date.issued | 2015-06-01 | |
dc.description | Bu çalışma, 06-10, Temmuz 2014 tarihlerinde Nashville[Amerika]’da düzenlenen International Conference on Thermoelectrics (ICT) Kongresi‘nde bildiri olarak sunulmuştur. | |
dc.description.abstract | Prediction of the performance of thermoelectric cooling material (figure of merit, ZT) was carried out by simulated results obtained from the finite element method (FEM) as a training dataset with an artificial neural network. A total of 87 input vectors for the ZT obtained from the four thermoelectric cooling (TEC) modules modeled using the FEM analysis were available in the training set to a back-propagation artificial neural network. An average correlation and maximum prediction error were found to be 100% and 0.01%, respectively, for the ZT after training. The standard deviation of the values was 0.05%. A set of test data, different from the training dataset was used to investigate the network performance. The average correlation and maximum prediction error were found to be 99.92% and 0.07%, respectively, for the tested TEC module. A thermoelectric module produced based on the numerical results was shown to be a promising device for use in cooling systems. | |
dc.description.sponsorship | LENA Energy & Technology GmbH | |
dc.description.sponsorship | ALDO BW Energy Co. | |
dc.identifier.doi | 10.1007/s11664-015-3657-0 | |
dc.identifier.eissn | 1543-186X | |
dc.identifier.endpage | 2073 | |
dc.identifier.issn | 0361-5235 | |
dc.identifier.issue | 6 | |
dc.identifier.startpage | 2068 | |
dc.identifier.uri | https://doi.org/10.1007/s11664-015-3657-0 | |
dc.identifier.uri | https://link.springer.com/article/10.1007/s11664-015-3657-0 | |
dc.identifier.uri | https://hdl.handle.net/11452/43896 | |
dc.identifier.volume | 44 | |
dc.identifier.wos | 000353813700100 | |
dc.indexed.wos | WOS.SCI | |
dc.indexed.wos | WOS.ISTP | |
dc.language.iso | en | |
dc.publisher | Springer | |
dc.relation.journal | Journal of Electronic Materials | |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası | |
dc.rights | info:eu-repo/semantics/closedAccess | |
dc.subject | Thermoelectric cooling | |
dc.subject | Cooling performance | |
dc.subject | Figure of merit | |
dc.subject | Artificial neural network | |
dc.subject | Science & technology | |
dc.subject | Technology | |
dc.subject | Physical sciences | |
dc.subject | Engineering, electrical & electronic | |
dc.subject | Materials science, multidisciplinary | |
dc.subject | Physics, applied | |
dc.subject | Engineering | |
dc.subject | Materials science | |
dc.subject | Physics | |
dc.title | Influence of geometrical factors on performance of thermoelectric material using numerical methods | |
dc.type | Article | |
dc.type | Proceedings Paper | |
dspace.entity.type | Publication | |
local.contributor.department | Fen Edebiyat Fakültesi/Fizik Bölümü | |
relation.isAuthorOfPublication | 0c85f61f-70fa-4f0d-83a0-a3a0ac50e069 | |
relation.isAuthorOfPublication.latestForDiscovery | 0c85f61f-70fa-4f0d-83a0-a3a0ac50e069 |