PREDICTION OF NH3 CONDENSATION HEAT TRANSFER COEFFICIENT WITH ARTIFICIAL NEURAL NETWORK AND MULTIPLE REGRESSION METHODS

Hakan Aydoğan; Mehmed Rafet Özdemir

Article Details

PREDICTION OF NH3 CONDENSATION HEAT TRANSFER COEFFICIENT WITH ARTIFICIAL NEURAL NETWORK AND MULTIPLE REGRESSION METHODS

Authors:

Hakan Aydoğan | Mehmed Rafet Özdemir

Publisher:

Istanbul Ticaret University

Main Topics:

İstatistiksel Analiz | Akışkan Akışı | Isı ve Kütle Transferinde Hesaplamalı Yöntemler (Hesaplamalı Akışkanlar Dinamiği Dahil) | Makine Mühendisliğinde Sayısal Yöntemler

Journal:

İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi Vol.22 Issue 44, pp.434-444

Document Type:

Article

Publication Date:

12/2023

Document Language:

Turkish

Document ID (Org No.):

2001305100

pISSN:

1305-7820

Keywords:

Abstract:

 The utilization of halocarbon or synthetic-based compounds in the refrigeration sector is limited by international protocols due to their adverse effects on the environment. For this reason, using organic-based refrigerants with low global warming and ozone depletion factors such as NH3 has come to the fore. NH3 has been preferred in the air conditioning industry in recent years due to its advantageous thermo-physical properties, such as high thermal capacity and low viscosity. While developing air conditioning systems, the systems heat transfer coefficient and pressure drop should be predicted with specific correlations. However, since NH3 has different thermo-physical properties from other fluids, no model in the literature gives the heat transfer coefficient and pressure drop in the condensation regime with high accuracy. In this study, heat transfer coefficient prediction was conducted with multiple regression and artificial neural network methods by taking data from literature studies with NH3 fluid in condensation regimes in horizontal and straight pipes. The results were discussed in light of the information in the literature and presented with suggestions.

 The utilization of halocarbon or synthetic-based compounds in the refrigeration sector is limited by international protocols due to their adverse effects on the environment. For this reason, using organic-based refrigerants with low global warming and ozone depletion factors such as NH3 has come to the fore. NH3 has been preferred in the air conditioning industry in recent years due to its advantageous thermo-physical properties, such as high thermal capacity and low viscosity. While developing air conditioning systems, the systems heat transfer coefficient and pressure drop should be predicted with specific correlations. However, since NH3 has different thermo-physical properties from other fluids, no model in the literature gives the heat transfer coefficient and pressure drop in the condensation regime with high accuracy. In this study, heat transfer coefficient prediction was conducted with multiple regression and artificial neural network methods by taking data from literature studies with NH3 fluid in condensation regimes in horizontal and straight pipes. The results were discussed in light of the information in the literature and presented with suggestions.

 The utilization of halocarbon or synthetic-based compounds in the refrigeration sector is limited by international protocols due to their adverse effects on the environment. For this reason, using organic-based refrigerants with low global warming and ozone depletion factors such as NH3 has come to the fore. NH3 has been preferred in the air conditioning industry in recent years due to its advantageous thermo-physical properties, such as high thermal capacity and low viscosity. While developing air conditioning systems, the systems heat transfer coefficient and pressure drop should be predicted with specific correlations. However, since NH3 has different thermo-physical properties from other fluids, no model in the literature gives the heat transfer coefficient and pressure drop in the condensation regime with high accuracy. In this study, heat transfer coefficient prediction was conducted with multiple regression and artificial neural network methods by taking data from literature studies with NH3 fluid in condensation regimes in horizontal and straight pipes. The results were discussed in light of the information in the literature and presented with suggestions.

 Soğutma sektöründe kullanılan halokarbon veya sentetik içerikli bileşiklerin çevreye olumsuz etkilerinden dolayı günümüzde kullanımları uluslararası protokollerle sınırlandırılmıştır. Bu sebeple, NH3 gibi düşük küresel ısınma ve ozon tüketme faktörlerine sahip organik bazlı soğutucu akışkanların kullanımı ön plana çıkmıştır. NH3, yüksek ısıl kapasitesi ve düşük viskozite gibi avantajlı termo-fiziksel özelliklerinden dolayı son yıllarda iklimlendirme endüstrisinde tercih edilmektedir. İklimlendirme sistemleri geliştirilirken, belli bağıntılarla sistemin ısı transfer katsayısı ve basınç düşüşü tahmin edilmelidir. Ancak NH3ün diğer akışkanlardan farklı termo-fiziksel özelliklere sahip olması sebebiyle, literatürde NH3 için yoğuşma rejiminde ısı transfer katsayısını ve basınç düşüşünü yüksek doğruluk oranında veren bir model bulunmamaktadır. Bu çalışmada, yatay ve düz borularda yoğuşma rejiminde bulunan NH3 akışkanı ile yapılmış literatür çalışmalarından veriler alınarak çoklu regresyon ve yapay sinir ağları metotları ile ısı transferi katsayısı tahmini yapılmıştır. Sonuçlar literatürdeki bilgiler ışığında tartışılmış ve öneriler sunulmuştur.

Article Details

PREDICTION OF NH3 CONDENSATION HEAT TRANSFER COEFFICIENT WITH ARTIFICIAL NEURAL NETWORK AND MULTIPLE REGRESSION METHODS

Authors:

Hakan Aydoğan | Mehmed Rafet Özdemir

Publisher:

Istanbul Ticaret University

Main Topics:

İstatistiksel Analiz | Akışkan Akışı | Isı ve Kütle Transferinde Hesaplamalı Yöntemler (Hesaplamalı Akışkanlar Dinamiği Dahil) | Makine Mühendisliğinde Sayısal Yöntemler

Journal:

İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi Vol.22 Issue 44, pp.434-444

Document Type:

Article

Publication Date:

12/2023

Document Language:

Turkish

Document ID (Org No.):

2001305100

pISSN:

1305-7820

Multiple regression

Artificial neural network

Ammonia

Condensation.Çoklu regresyon

Yapay sinir ağları

Amonyak

Yoğuşma.

Issues

Article Details

Authors: Hakan Aydoğan | Mehmed Rafet Özdemir

Publisher: Istanbul Ticaret University

Main Topics: İstatistiksel Analiz | Akışkan Akışı | Isı ve Kütle Transferinde Hesaplamalı Yöntemler (Hesaplamalı Akışkanlar Dinamiği Dahil) | Makine Mühendisliğinde Sayısal Yöntemler

Journal: İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi Vol.22 Issue 44, pp.434-444

Document Type: Article

Publication Date: 12/2023

Document Language: Turkish

Document ID (Org No.): 2001305100

pISSN: 1305-7820

Issues

Authors:

Hakan Aydoğan | Mehmed Rafet Özdemir

Publisher:

Istanbul Ticaret University

Main Topics:

İstatistiksel Analiz | Akışkan Akışı | Isı ve Kütle Transferinde Hesaplamalı Yöntemler (Hesaplamalı Akışkanlar Dinamiği Dahil) | Makine Mühendisliğinde Sayısal Yöntemler

Journal:

İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi Vol.22 Issue 44, pp.434-444

Document Type:

Article

Publication Date:

12/2023

Document Language:

Turkish

Document ID (Org No.):

2001305100

pISSN:

1305-7820