Mapping of horizontal refrigerant two-phase flow patterns based on clustering of capacitive sensor signals |
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| Authors: | H Canière B Bauwens C T’Joen M De Paepe |
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| Affiliation: | 1. Department of Flow, Heat and Combustion Mechanics, Ghent University – UGent, St.-Pietersnieuwstraat 41, 9000 Gent, Belgium;2. Department of Electrical Engineering, Systems and Automation, Ghent University – UGent, Gebouw Regeltechniek, Technologiepark 913, 9052 Zwijnaarde, Belgium;3. Department of Radiation, Radionuclides and Reactors, Delft University of Technology, Mekelweg 15, 2629 JB Delft, The Netherlands;1. School of Oil & Natural Gas Engineering, Southwest Petroleum University, Chengdu 610500, China;2. Oil & Gas Fire Protection Key Laboratory of Sichuan Province, Chengdu 611731, China;3. Chengdu Verification Branch of National Oil Gas Large Flow Rate Measurement Station, Chengdu 610213, China;4. Shaanxi Yanchang Petroleum (Group) Co., Ltd, Xi’an 710075, China;1. Technical University of Koszalin, Faculty of Mechanical Engineering, Department of Energy, ul. Raclawicka 15-17, 75-620 Koszalin, Poland;2. Department of Mechanical and Aeronautical Engineering, Faculty of Engineering, Built Environment and Information Technology, University of Pretoria, South Africa;1. ISTENER Research Group, Department of Mechanical Engineering and Construction, Campus de Riu Sec s/n, University Jaume I, E12071 Castellón Spain;2. Departamento de Ingeniería Química y Nuclear, Universidad Politécnica de Valencia, Camino de Vera 14, Valencia Spain;3. DuPont Fluorochemicals R&D, Chestnut Run Plaza, P.O.Box 2915, Wilmington, DE 19805, USA;1. Institute of Air Conditioning and Refrigeration, Nanjing University of Aeronautics and Astronautics, 29 Yudao St., Nanjing 210016, China;2. R&D Center, AVIC Aircraft Co., Ltd., Yanliang, Xian 710089, China |
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| Abstract: | A capacitive void fraction sensor was developed to study the objectivity in flow pattern mapping of horizontal refrigerant two-phase flow in macroscale tubes. Sensor signals were gathered with R410A and R134a in a smooth tube with an inner diameter of 8 mm at a saturation temperature of 15 °C in the mass velocity range of 200–500 kg/m2 s and vapour quality range from 0 to 1 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 the fuzzy c-means clustering algorithm. This soft-clustering algorithm predicts the slug/intermittent flow transition very well 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) 20]. 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 R410A and R134a in an 8 mm ID tube. |
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