Abstract
This work proposes a method to distinguish between various flow patterns in a multiphase gas-liquid system. The complete discrimination between different flow patterns can be achieved by mapping the corresponding frequency and statistical parameters. These parameters are usually obtained from further analysis conducted on the signal data of the utilized sensor. The proposed technique is based on establishing interrelationships between these parameters, namely the mean (m), the standard deviation (sigma</mml:mover>), power spectral density (PSD), the width of the characteristic frequency peaks (Delta integral), the skewness (gamma 1) and the kurtosis (gamma 2). Therefore, a relatively simple electrical capacitance sensor with two electrodes was designed and implemented on a two-phase flow apparatus with a circular pipe. The experimental operating conditions comprised of different combinations of air-water superficial velocities at three inclinations (i.e., horizontal, upward 15 degrees and upward 30 degrees). This research discusses in specific the analysis underlying flow patterns identification method and the rationale for selecting the proposed approach. The results showed that some parameters found to be more valuable than others such as m, sigma</mml:mover> and Delta integral. Besides, combining two sets of these statistical graphs which are (a) sigma</mml:mover> vs. Delta integral with Delta integral vs. m (or Delta integral vs. total power), (b) Delta integral vs. total power with gamma 1 vs. <mml:mover accent="true">sigma</mml:mover> (or gamma 2 vs. <mml:mover accent="true">sigma</mml:mover>), and (c) <mml:mover accent="true">sigma</mml:mover> vs. m with Delta integral vs. m (or Delta integral vs. total power), allowed all flow patterns field to be identified clearly at all inclinations. It is therefore concluded that for any gas-liquid multiphase flow system, the reported approach can be used reliably to discriminate between different generated flow patterns.