Artificial Neural Network Techniques for the Determination of Condensation Nusselt Number in Horizontal Smooth Tubes

Year 2019, Volume: 23 Issue: 3, 871 - 877, 25.12.2019

Mustafa Kemal Sevindir , Alişan Gönül Alican Çebi Hatice Mercan

https://doi.org/10.19113/sdufenbed.503829

Cited By: 1

Abstract

In
this study, using readily available experimental data in the literature,
artificial neural networks (ANN) method is adopted to specify condensation
Nusselt number in horizontal smooth tubes. Condensation heat transfer of R22,
R134a and 50/50 and 60/40 of the R32/ R125 azeotropic refrigerant mixtures were
examined with four different ANN methods. The experimental data is taken from
the study of Dobson et al. [1]. The input parameters are mass flux, quality,
hydraulic diameter, Soliman's modified Froude number, density of fluid phase
and dynamic viscosity of liquid phase where the output parameter is the
condensation Nusselt number. In this study the interval for tube diameters is
between 3.14-7.04 mm, and the interval for mass flux is between 50-800 kg/m²s.  The training algorithms are tested using
different neuron numbers and the best algorithm was found as Bayesian
regularization having 8 neurons. It is observed that the Nu number evaluated
using ANN is ± 15% error margin compared to experimental results. Furthermore,
for increasing mass flux rates the error margin is around ± 5%.

Keywords

Condensation, Artificial neural networks, Refrigerant, Nusselt number, Horizontal smooth tube

Yatay Pürüzsüz Borularda Yoğuşmadaki Nusselt Sayısının Belirlenmesi için Yapay Sinir Ağ Teknikleri

Abstract

Bu
çalışmada, literatürdeki hazır deneysel veriler kullanılarak, yatay pürüzsüz borularda
yoğuşmadaki Nusselt sayısını belirlemek için yapay sinir ağları (ANN) yöntemi
kullanılmıştır. R32, R134a ve %50/%50 ve %60/%40 R32/R125 azeotropik soğutucu
karışımlarının yoğuşma ısı transferi dört farklı ANN yöntemi ile incelendi;
Levenberg-Marquardt, Bayes düzenlenmesi, ölçeklenmiş eşlenik değişim ve esnek
geri yayılımı. Deneysel veriler Dobson ve ark.[1]’nın çalışmalarından
alınmıştır. Giriş parametreleri kütle akısı, kalite, hidrolik çap, Soliman'ın değiştirilmiş
Froude sayısı, akışkan faz yoğunluğu ve çıkış parametresinin yoğuşmadaki
Nusselt sayısının olduğu sıvı fazın dinamik viskozitesidir. Bu çalışmada, boru
çapları aralığı 3,14-7,04 mm arasında ve kütle akı aralığı 50-800 kg/m²
arasındadır. Eğitim algoritmaları farklı nöron sayıları kullanılarak test
edildi ve en iyi algoritma 8 nörona sahip Bayes düzenlenmesi olarak bulundu. ANN
kullanılarak değerlendirilen Nu sayısının deney sonuçlarına göre ±%15 hata payı
olduğu gözlenmiştir. Ayrıca, artan kütle akı oranları için hata payı ±%5
civarındadır.

Keywords

Yoğuşma, Yapay sinir ağları, Soğutucu akışkan, Nusselt sayısı, Yatay pürüzsüz boru

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