Modeling of the effects of plug tip angle on the performance of counter flow ranque hilsch vortex tubes using artificial neural networks
Yazarlar (4)
Kevser Dincer Selçuk Üniversitesi, Türkiye
Prof. Dr. Şakir TAŞDEMİR Selçuk Üniversitesi, Türkiye
Şenol Başkaya
Gazi Üniversitesi, Türkiye
Gazi Üniversitesi, Türkiye
B. Zühtü Uysal
Gazi Üniversitesi, Türkiye
Gazi Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SCOPUS dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Isi Bilimi Ve Teknigi Dergisi Journal of Thermal Science and Technology | ||
| Dergi ISSN | 1300-3615 Dergi Bilgileri (2008) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2008 |
| Cilt / Sayı / Sayfa | 28 / 2 / 1–7 | DOI | – |
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| In this study, by making use of experimental data, the effect of plug tip angle at the hot outlet section of a counter flow Ranque-Hilsch vortex tube (RHVT) on performance has been modeled using artificial neural network (ANN). In the modeling, data which were obtained from experimental studies in a laboratory environment have been used. In the system developed, ANN apply input parameters are P, and 0, output parameter is AT. When the results obtained from ANN and statistical analyses of experimental data have been compared, it has been determined that the two groups of data are coherent, and that there is not a significant difference between them. As a result, this study indicates that ANN can be safely used for RHVTs and thus it can decrease many experimental disadvantages to a minimum level. |
| Anahtar Kelimeler |
| Artificial neural network | Performance | Ranque-Hilsch vortex tube |
