Fuzzy flow pattern identification in horizontal air-water two-phase flow based on wire-mesh sensor data

Abstract

Online monitoring of two-phase flow patterns is an essential need in various chemical engineering applications, since the reliability of prediction methods is limited. Therefore, the present study aims at developing a practically applicable algorithm for identifying flow patterns in horizontal gas-liquid flows on the basis of wire-mesh sensor data. Experiments were conducted in a 50 mm i. d. pipe over a wide range of superficial velocities of an air-water mixture. Characteristic features involving the influence of gravity and the spatio-temporal behavior of the flows were derived from tomographic phase fraction data and used as input for fuzzy clustering. Three differently determined sets of cluster centers are compared against a reference classification by human specialist through reclassifying the measurements with the aid of defuzzyfication and, alternatively, by means of a novel visualization technique, that retains the fuzziness of the results. With respect to the latter one, best agreement is reached with cluster centers from fuzzy c-means clustering using all recorded measurements. As a special emphasis is put to the identification of transitional flow patterns, the performance of the algorithm at pseudo-dynamic operation is demonstrated, finally.