ANALYSIS OF OUTCOMES OF THE FRICTIONAL PRESSURE DROP PREDICTION USING DIFFERENT DATA SOURCE
Predictions of the frictional pressure drop using friction factor correlations that have been developed based on past experimental data have always been found to disagree with recent experimental data. Thus, new correlations are continuously being developed to generalize their applications across refrigerants and flow regimes. The friction factor is dependent on the Reynolds number and relative roughness, therefore consequently depends on the applied equation and fluid data. This research shows
... This research shows the outcome of the analysis of the frictional pressure drop prediction when different data source as well as different friction factor equations for smooth and rough pipes are utilized. The R-22 data used for comparison are experimental data from a past report, NIST (Standard Reference Database), and experimental data from University of Indonesia. The used e friction factor equations are Blasius and Fang et al. (2011) in smooth and rough pipe respectively. The mass flux is ranging from 200 to 600 kg/m2s and vapor quality from 0.0001 to 0.5, the latter of which is assumed constant along the pipe length of 2000 mm at the saturation temperature of 10°C. The pipe material is stainless steel with an absolute roughness of 0.03 mm. The minimization of the friction factor and two-phase flow frictional pressure drop is achieved by applying Genetic Algorithm (GA). The comparisons reveal that the differences are an indication of the appropriate data source necessary so that the frictional pressure drop can be accurately predicted. The results showed that in 1.5 mm pipe diameter, the Blasius equation gives the lower percentage of differences in the range of 0.69 – 1.47 % when the data from NIST and UI are used. While the lower percentage of differences gives Fang et al. (2011) equation in the range of 1.47 – 2.61% when data from Pamitran et al. (2010) and UI are used. In the 3 mm inner diameter, also Blasius equation gives the lower percentage of differences in the range of 0.89 – 2.52% when the data from Pamitran et al. (2010) and UI are used. While Fang et al. (2011) gives the lower percentage of differences in the range of 1.56 – 1.33% when the data from Pamitran et al. (2010) and UI are used. The proposed method is predictable to raise the accuracy of the prediction and decrease the time of testing. The results are compared between each other's for different data sources. For most situations, the percentage difference, as well as for laminar and turbulent flows are between 91 – 97% and 88 – 95% in 1.5 and 3 mm pipe diameter respectively.