A prediction model of artificial neural networks in development of thermoelectric materials with innovative approaches
Yazarlar (5)
Şeyma Kökyay
Karabük Üniversitesi, Türkiye
Karabük Üniversitesi, Türkiye
Dr. Öğr. Üyesi Enes KILINÇ Karabük Üniversitesi, Türkiye
Dr. Öğr. Üyesi Fatih Uysal Sakarya University Of Applied Sciences, Türkiye
Prof. Dr. Erdal Çelik Yükseköğretim Kurulu, Türkiye
Prof. Dr. Muharrem DÜĞENCİ Karabük Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
|
||
| Dergi Adı | Engineering Science and Technology an International Journal (Q1) | ||
| Dergi ISSN | 2215-0986 Dergi Bilgileri (2020) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 12-2020 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-12-2020 |
| Cilt / Sayı / Sayfa | 23 / 6 / 1476–1485 | DOI | 10.1016/j.jestch.2020.04.007 |
| Makale Linki | https://doi.org/10.1016/j.jestch.2020.04.007 | ||
| UAK Araştırma Alanları |
Veri Madenciliği
|
||
| Özet |
| The fact that the properties of thermoelectric materials are to be estimated with Artificial Neural Networks without production and measurement will help researchers in terms of time and cost. For this purpose, figure of merit, which is the performance value of thermoelectric materials, is estimated by Artificial Neural Networks without an experimental study. P-and n-type thermoelectric bulk samples were obtained in 19 different compositions by doping different elements into Ca2.7Ag0.3Co4O9- and Zn0.98Al0.02O-based oxide thermoelectric materials. The Seebeck coefficient, electrical resistivity and thermal diffusivity values of the bulk samples were measured from 200 °C to 800 °C with an increase rate of 100 °C, and figure of merit values were calculated. 7 different Artificial Neural Network models were created using 123 measured results of experimental data and the molar masses of the doping elements. In this … |
| Anahtar Kelimeler |
| Artificial neural network | Figure of merit | Prediction model | Thermoelectric material |
Science Direct
A prediction model of artificial neural networks in development of thermoelectric materials with innovative approaches
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 26 |
| Scopus | 27 |
| Google Scholar | 34 |