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Intermittent-annular flow transition analysis of R410A based on capacitance signal clustering.

Author(s) : CANIÈRE H., T'JOEN C., PAEPE M. de

Summary

To study the objectivity in flow pattern mapping of horizontal two-phase flow in macroscale tubes, a capacitance sensor is developed for use with refrigerants. Sensor signals are gathered with R410A in an 8 mm ID smooth tube at a saturation temperature of 15°C in the mass velocity range of 200 to 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. The fuzzy c-means clustering algorithm is used to classify the sensor signals. This soft clustering algorithm perfectly predicts the slug/intermittent flow transition compared to our visual observations. The intermittent/annular flow transition is very gradual. A probability approach can therefore better describe this transition. The membership grades of the cluster algorithm can be interpreted as flow probabilities to quantify this transition.

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Pages: 2009-5

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Details

  • Original title: Intermittent-annular flow transition analysis of R410A based on capacitance signal clustering.
  • Record ID : 2009-1929
  • Languages: English
  • Source: 3rd Conference on Thermophysical Properties and Transfer Processes of Refrigerants
  • Publication date: 2009/06/23

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