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%.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
