Modeling of the effects of length to diameter ratio and nozzle number on the performance of counterflow Ranque Hilsch vortex tubes using artificial neural networks
Yazarlar (4)
K. Dincer
Selçuk Üniversitesi, Türkiye
Selçuk Üniversitesi, Türkiye
Prof. Dr. Şakir TAŞDEMİR Selçuk Üniversitesi, Türkiye
S. Baskaya Gazi Üniversitesi, Türkiye
B. Z. Uysal Gazi Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Applied Thermal Engineering (Q2) | ||
| Dergi ISSN | 1359-4311 Dergi Bilgileri (2008) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 12-2008 |
| Cilt / Sayı / Sayfa | 28 / 17 / 2380–2390 | DOI | 10.1016/j.applthermaleng.2008.01.016 |
| Makale Linki | https://linkinghub.elsevier.com/retrieve/pii/S1359431108000434 | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| In this study, the effect of length to diameter ratio and nozzle number on the performance of a counterflow Ranque–Hilsch vortex tube has been modeled with artificial neural networks (ANN), by using experimental data. In the modeling, experimental data, which were obtained from experimental studies in a laboratory environment have been used. ANN has been designed by MATLAB 6.5 NN toolbox software in a computer environment working with Windows XP operating system and Pentium 4 2.4GHz hardware. In the developed system outlet parameter ΔT has been determined using inlet parameters P, L/D, N and ξ. When experimental data and results obtained from ANN are compared by statistical independent t-test in SPSS, it was determined that both groups of data are consistent with each other for P>0.05 confidence interval, and differences were statistically not significant. Hence, ANN can be used as a … |
| Anahtar Kelimeler |
| Artificial neural network | Performance | Ranque-Hilsch vortex tube |
Science Direct
Modeling of the effects of length to diameter ratio and nozzle number on the performance of counterflow Ranque Hilsch vortex tubes using artificial neural networks
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 92 |
| Scopus | 102 |
| Google Scholar | 130 |