Paper presented at the 7th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Turkey, 19-21 July, 2010.
A capacitive void fraction sensor was developed to study the objectivity in flow pattern mapping of horizontal refrigerant two-phase flow in macro-scale tubes. Sensor signals were gathered with R410A and R134a in an 8mm I.D. smooth tube at a saturation temperature of 15°C in the mass velocity range of 200 to 500 kg/m2s and vapour quality range from 0 to I in steps of 0.025. A visual classification based on high speed camera images is
made for comparison reasons. A statistical analysis of the sensor signals shows that the average, the variance and a high frequeNcy contribution parameter are suitable for flow regime classification into slug flow, intermittent flow and annular flow by using a the fuzzy c-means clustering algorithm. This soft clustering algorithm perfectly predicts the slug/intermittent flow transition compared to our visual observations. The intermittent/annular flow transition is found at slightly higher vapour qualities for R410A compared to the prediction of [Barbieri et al., 2008, Flow patterns in convective boiling of refrigerant R-134a in smooth tubes of several diameters, 5th European Thermal-Sciences Conference, The Netherlands]. An excellent agreement was obtained with R134a. This intermittent/annular flow transition is very gradual. A probability approach can therefore better describe such a transition. The membership grades of the cluster algorithm can be interpreted as flow regime probabilities. Probabilistic flow pattern maps are presented for R41OA and R134a in an 8mm
I.D. tube.
