Öz
Bu çalışmada, buhar sıkıştırmalı soğutma çevriminde kullanılan ve ozon tabakasına zararı olmayan çevre dostu
R410a soğutucu akışkanının doyma basıncı, sıvı ve buhar entalpileri, entropileri, kızgın buhar entalpisi ve sıcaklığı gibi termodinamik özellikleri Yapay Sinir Ağları (YSA) modeliyle belirlenmiştir. Bu modelde kullanılan veriler, literatürde mevcut olan ampirik ve deneysel veriler yardımıyla elde edilmiştir. Çalışmada; çok iyi eğitilmiş bir YSA modeliyle, istenilen her aralıkta soğutucu akışkanın termodinamik özelliklerinin tespiti için yeni formülasyonlar türetilmiştir. Bu formülasyonlar, ağ modeline ait ağırlık ve bias değerleri kullanılarak elde edilmiştir. Modelden elde edilen denklemlerle çok daha hızlı ve basit çözümlere ulaşılmıştır.
Anahtar Kelimeler
Yapay sinir ağları buhar sıkıştırmalı sistemler R410a termodinamik özellikler
Kaynakça
- Çengel, A.Y., Boles, A.M, Thermodynamics: An
- Engineering Approach, McGraw-Hill, New
- York, U.S., 1994.
- Özkol, N., Uygulamalı Soğutma Tekniği,
- TMMOB Makine Mühendisleri Odası Yayın No:
- , Ankara, 1999.
- Dossat, R.J., Principles of Refrigeration,
- Prentice Hall, New Jersey, U.S., 1997.
- Dupont. 2004. DuPont de Nemours and
- Company Inc. http://www.dupont.com/suva
- Kalogirou, S.A.,“Artificial Neural Networks in
- Renewable Energy Systems Applications: A
- Review”, Renewable and Sustainable Energy
- Reviews, 5, 373–401, 2000.
- Dorvlo, S.S., Jervase, J.A., Al-Lawati, A., “Solar
- Radiation Estimation Using Artificial Neural
- Network”, Applied Energy, 71, 307–319, 2002.
- Civalek, Ö., “Dairesel Plakların Nöro-Fuzzy
- Tekniği İle Analizi”, Dokuz Eylül Üniversitesi
- Fen ve Mühendislik Dergisi, 1(2), 13-31, 1999.
- Sözen, A., Arcaklıoğlu, E., Özalp, M.,
- Formulation based on Artificial Neural Network
- of Thermodynamic Properties of Ozone Friendly
- Refrigerant/Absorbent Couples”, Applied Thermal
- Engineering, 25(11-12): 1808-1820, 2005.
- Sözen, A., Akçayol, M.A., “Modelling (Using
- Artificial Neural-Networks) the Performance
- Parameters of a Solar-Driven Ejector-Absorption
- Cycle”, Applied Energy, 79(3): 309-325, 2004.
- Kalogirou, S. A., “Long-Term Performance
- Prediction of Forced Circulation Solar Domestic
- Water Heating Systems Using Artificial Neural
- Networks”, Applied Energy, 66, 63–74, 2000.
- Kalogirou, S.A, “Applications of Artificial Neural
- Networks in Energy Systems: A Review”, Energy
- Conversion&Management, 40: 1073-1087, 1999.
- Fu, L.M., Neural Networks in Computer
- Intelligence, Mc Graw-Hill International Editions,
- p., 1994.
- Tsoukalas, L.H., Uhrig, R.E., Fuzzy and Neural
- Approaches In Engineering, John Wiley&Sons
- Inc., 587p., 1997.
- Efe, Ö., Kaynak, O., Yapay Sinir Ağları ve Uygulamaları,
- Boğaziçi Üniversitesi, İstanbul, 2000.
- Şencan, A., Yakut, A.K., Dikmen, E., “A New
- Model for Determining the Thermodynamic
- Properties of LiBr-H2O Solution”, G.U. Journal
- of Science, 17(1):101-110, 2004.
- Sözen, A., Arcaklıoğlu, E., Özalp, M.,
- “Formulation based on Artificial Neural Network
- of Thermodynamic Properties of Ozone Friendly
- Refrigerant/Absorbent Couples”, Applied Thermal
- Engineering, 25(11-12): 1808-1820, 2005.
- Sözen, A., Akçayol, M.A., “Modelling (Using
- Artificial Neural-Networks) the Performance
- Parameters of a Solar-Driven Ejector-Absorption
- Cycle”, Applied Energy, 79(3): 309-325, 2004.
Öz
In this study, thermodynamic properties as saturation pressure, saturated liquid enthalpy and entropy,
superheated vapor enthalpy and temperature of R410a refrigerant, which are harmful to the ozone layer, used in
the vapor compression refrigeration systems were determined with Artificial Neural Network (ANN) model.
Data of thermodynamic properties used in the study were obtained from empirical and experimental data that is
available in the literature. In order to determine thermodynamic properties of refrigerant for all spans, new
formulations were derived by ANN model which was very well trained. These formulations were derived with
using weights and bias values of network. With these formulations, faster and simple solutions can be obtained.
Anahtar Kelimeler
Artificial neural network vapor compression systems R410a thermodynamic properties
Kaynakça
- Çengel, A.Y., Boles, A.M, Thermodynamics: An
- Engineering Approach, McGraw-Hill, New
- York, U.S., 1994.
- Özkol, N., Uygulamalı Soğutma Tekniği,
- TMMOB Makine Mühendisleri Odası Yayın No:
- , Ankara, 1999.
- Dossat, R.J., Principles of Refrigeration,
- Prentice Hall, New Jersey, U.S., 1997.
- Dupont. 2004. DuPont de Nemours and
- Company Inc. http://www.dupont.com/suva
- Kalogirou, S.A.,“Artificial Neural Networks in
- Renewable Energy Systems Applications: A
- Review”, Renewable and Sustainable Energy
- Reviews, 5, 373–401, 2000.
- Dorvlo, S.S., Jervase, J.A., Al-Lawati, A., “Solar
- Radiation Estimation Using Artificial Neural
- Network”, Applied Energy, 71, 307–319, 2002.
- Civalek, Ö., “Dairesel Plakların Nöro-Fuzzy
- Tekniği İle Analizi”, Dokuz Eylül Üniversitesi
- Fen ve Mühendislik Dergisi, 1(2), 13-31, 1999.
- Sözen, A., Arcaklıoğlu, E., Özalp, M.,
- Formulation based on Artificial Neural Network
- of Thermodynamic Properties of Ozone Friendly
- Refrigerant/Absorbent Couples”, Applied Thermal
- Engineering, 25(11-12): 1808-1820, 2005.
- Sözen, A., Akçayol, M.A., “Modelling (Using
- Artificial Neural-Networks) the Performance
- Parameters of a Solar-Driven Ejector-Absorption
- Cycle”, Applied Energy, 79(3): 309-325, 2004.
- Kalogirou, S. A., “Long-Term Performance
- Prediction of Forced Circulation Solar Domestic
- Water Heating Systems Using Artificial Neural
- Networks”, Applied Energy, 66, 63–74, 2000.
- Kalogirou, S.A, “Applications of Artificial Neural
- Networks in Energy Systems: A Review”, Energy
- Conversion&Management, 40: 1073-1087, 1999.
- Fu, L.M., Neural Networks in Computer
- Intelligence, Mc Graw-Hill International Editions,
- p., 1994.
- Tsoukalas, L.H., Uhrig, R.E., Fuzzy and Neural
- Approaches In Engineering, John Wiley&Sons
- Inc., 587p., 1997.
- Efe, Ö., Kaynak, O., Yapay Sinir Ağları ve Uygulamaları,
- Boğaziçi Üniversitesi, İstanbul, 2000.
- Şencan, A., Yakut, A.K., Dikmen, E., “A New
- Model for Determining the Thermodynamic
- Properties of LiBr-H2O Solution”, G.U. Journal
- of Science, 17(1):101-110, 2004.
- Sözen, A., Arcaklıoğlu, E., Özalp, M.,
- “Formulation based on Artificial Neural Network
- of Thermodynamic Properties of Ozone Friendly
- Refrigerant/Absorbent Couples”, Applied Thermal
- Engineering, 25(11-12): 1808-1820, 2005.
- Sözen, A., Akçayol, M.A., “Modelling (Using
- Artificial Neural-Networks) the Performance
- Parameters of a Solar-Driven Ejector-Absorption
- Cycle”, Applied Energy, 79(3): 309-325, 2004.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Mimarlık |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 1 Mart 2013 |
| Gönderilme Tarihi | 4 Haziran 2014 |
| Yayımlandığı Sayı | Yıl 2006 Cilt: 21 Sayı: 2 |