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International Communications In Heat And Mass Transfer


In this work, electrical capacitance tomography (ECT) and neural networks were used to automatically identify two-phase flow patterns for refrigerant R-134a flowing in a horizontal tube. In laboratory experiments, high- speed images were recorded for human classification of liquid–vapor flow patterns. The corresponding permittivity data obtained from tomograms was then used to train feedforward neural networks to recognize flow patterns. An objective was to determine which subsets of data derived from tomograms could be used as input data by a neural network to classify nine liquid–vapor flow patterns. Another objective was to determine which subsets of input data provide high identification success when analyzed by a neural network. Transitional flow patterns associated with common horizontal flow patterns were considered. A unique feature of the current work was the use of the vertical center of mass coordinate in pattern classification. The highest classification success rates occurred using neural network input which included the probability density functions (in time) for both spatially averaged permittivity and center of mass location in addition to the four statistical moments (in time) for spatially averaged permittivity data. The combination of these input data resulted in an average success rate of 98.1% for nine flow patterns. In addition, 99% of the experimental runs were either correctly classified or misclassified by only one flow pattern.

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Pergamon-Elsevier Science Ltd



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