Modeling of the effects of plug tip angle on the performance of counter-flow Ranque-Hilsch vortex tubes using artificial neural networks;Karşιt akιşlι Ranque-Hilsch vorteks tüpünün performansιna tapa açιsι etkisinin yapay sinir aǧlarι yöntemi Ile modellenmesi

Isi Bilimi Ve Teknigi Dergisi/ Journal of Thermal Science and Technology

Volumn 28, Issue 2, 2008, Pages 1-7

  Dincer, Kevser ^a   Taşdemir, Şakir ^a   Başkaya, Şenol ^b   Uysal, B Zühtü ^b  

^a SELCUK UNIVERSITY   (Turkey)

^b GAZI UNIVERSITY   (Turkey)

Author keywords

Artificial neural network; Performance; Ranque Hilsch vortex tube

Indexed keywords

EID: 58649116927     PISSN: 13003615     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (26)

1. Abbasi, A. ve Bahar, L., Application of neural network for the modeling and control of vaporative condenser cooling load, Applied Thermal Engineering, 25, 3176-3186, 2005.
2. Akçayol, M.A., Çinar, C., Billbol, H.I. ve Kiliçarslan A., Artificial neural network based modeling of injection pressure in diesel engines, World Scientific and Engineering Academy and Society (WSEAS) Transactions on Computers, ISSN 1109-2750, 3(5), 1538-1544, 2004.
3. Abernethy, RB., Benedict, RP. ve Dowdell, RB. ASME Measurement Uncertainty, J Fluids Engineering, 107;161-4, 1985.
4. Allahverdi, N., Uzman Sistemler, Bir Yapay Zeka Uygulamasι, Atlas Yayιn Daǧιtιm, Istanbul, 2002.
5. Ayata, T., Çavuşoǧlu, A.ve Arcaklioǧlu, E., Predictions of temperature distributions on layered metal plates using artificial neural Networks, Energy Conversion and Management, 47, 2361-2370, 2006.
6. Başbuǧ, R., Yapay Sinir Aǧlari, Byte Dergisi, Istanbul, sayfa 167, Şubat 1994.
7. Caudill, M., Neural Networks Primer Part 1, AI Expert, 46-52, 1987.
8. Dincer, K., Baskaya S. ve B. Z. Uysal, Experimental investigation of the effects of length to diameter ratio and nozzle number on the performance of counterflow Ranque-Hilsch vortex tubes, Heat and Mass Transfer, 44, 367-373,2008.
9. Dincer, K., Karşit Akişli Ranque-Hilsch Vorteks Tüpün Performansιnιn Incelenmesi, Doktora Tezi, Gazi Üniversitesi, Fen Bil. Enst., Ankara, s. 1-229, 2005.
10. Elmas, Ç., Yapay Sinir Aǧlarι, Seçkin Yayιncιlιk, Ankara, 2003.
11. Islamoglu, Y. ve Kurt, A., Heat transfer analysis using ANNs with experimental data for air flowing in corrugated channels, International Journal of Heat and Mass Transfer, 47, 1361-1365, 2004.
12. Kline, SJ., The Purposes of Uncertainty Analysis, J. Fluids Engineering, 107;153-60, (1985).
13. Lee, C.S., Hwang, W., Park, H.C.ve Han, K.S., Failure of carbon/epoxy composite tubes under combined axial and torsional loading 1. Experimental results and prediction of biaxial strength by the use of neural networks, Composites Science and Technology, 59(12), 779-1788, 1999.
14. Moffat, RF., Contributions to the theory of single-sample uncertainty analysis, J Fluids Engineering, 104; 250-60, 1982.
15. Moffat, RF., Using Uncertainty Analysis in the Planning of an Experiment, J Fluids Engineering, 107;173-8, 1985.
16. Öztemel, E., Yapay Sinir Aǧlarι, Papatya Yayιncιlιk, Istanbul, 2003.
17. Rakesh K., Kaushikb S.C.ve Gargb S.N., Heating and cooling potential of an earth-to-air heat exchanger using artificial neural network, Renewable Energy, 31(8), 1139-1155, 2006.
18. Rumelhart, D.E., Hinton, G. E. ve McClelland, J. L. General Fremework for Parallel Distributed Processing, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol: 1, MIT Press Cambridge, Massachusetts, sayfa 51, 1986.
19. Soteris, A. K., Artificial intelligence for the modeling and control of combustion processes: a review, Progress in Energy and Combustion Science, Volume 29, 515-566, 2003.
20. Smith, Jr. RE. ve Wehofer, S., From Measurement Uncertainty to Measurement Communications, Credibility, and Cost Control in Propulsion Ground Test Facilities, J Fluids Engineering, 107; 165-72, 1985.
21. Şcalabrin, G., Condosta, M. ve Marchi, P., Mixtures flow boiling: modeling heat transfer through artificial neural Networks, International Journal of Thermal Sciences, 45(7), 664-680, 2006.
22. Şcalabrin, G., Condosta, M. ve Marchi, P., Mixtures flow boiling: modeling heat transfer through artificial neural Networks, International Journal of Thermal Sciences, 45 (7), 664-680, 2006.
23. Şencan, A., Artificial intelligent methods for thermodynamic evaluation of ammonia-water refrigeration systems, Energy Conversion and Management, 47, 3319-3332, 2006.
24. Şencan, A. ve Kalogirou, S.A., A new approach using artificial neural networks for determination of the thermodynamic properties of fluid couples, Energy Conversion and Management, 46(15), 2405-2418, 2005.
25. Tasdemir, S., Saritas, L, Ciniviz, M., Cinar, C. ve Allahverdi, N., Application of artificial neural network for definition of a gasoline engine performance, 4th International Advanced Technologies Symposium, Konya, Turkey, 1030-1034, 28-30 Sept, 2005.
26. Yigit, K.S. ve Ertunc, H.M., Prediction of the air temperature and humidity of a cooling coil using neural Networks, International Communications in Heat and Mass Transfer, 33(7), 898-907, 2000.