Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
In the first part of this article (Silva Lima and Thome 2012b), new experimental frictional pressure drop data in U-bends were presented and discussed. The experimental data were obtained for R410A and R134a at two saturation temperatures (5 degrees C and 10 degrees C [41 degrees F and 50 degrees F]) flowing at three mass fluxes (150, 300, and 500 kg s(-1) m(-2) [110.6.10(3), 221.2.10(3), and 368.7.10(3) lb h(-1) ft(-2)]) inside five different test sections with three different internal diameters (13.4, 10.7, and 7.8 mm [0.527, 0.421, and 0.307 in.]) and five different bend diameters (66.1, 54.8, 38.1, 31.7, and 24.8 mm [2.602, 2.157, 1.5, 1.248, and 0.976 in.]) in three different orientations (horizontal, vertical upflow, and vertical downflow) with vapor qualities ranging from 0.05 to 0.95. In this second part, first an update to the flow pattern based frictional pressure drop model for straight tubes of Moreno Quiben and Thome (2007a, 2007b) is proposed. Then, a new multi-orientation flow pattern based prediction method for frictional pressure drops in U-bends is presented and described. With this new model, more than 93% of the experimental data were predicted within an error window of less than 30%. The integration of the two models over the test section (straight tubes and U-bend) predicted the total pressure drop database with similar accuracy. The results showed that the combination of these two new prediction tools offers a powerful but simple way for one to obtain high accuracy pressure drop predictions.