Experiments of gas–liquid flow in a circular pipe for horizontal and inclined positions (upward/downward) are reported. The characteristics of two-phase flow in terms of liquid holdup (ε(L)) and induced flow patterns are studied using three experimental techniques; time-averaged ε(L) from permittivity profiles using electrical capacitance tomography (ECT), instantaneous ε(L) using two fast-closing valves (TFCV), and high-speed camera images (HSCI) to capture/identify the formed flow patterns. Thus, this experimental setup enables the development of more well-defined flow patterns in gas–liquid two-phase flow and allows for multi-technique verification of the results. Taken from experimental measurements, a model is proposed to predict ε(L) for high and low situations. The correlations are a function of the hydrodynamic dimensionless quantities which provide hydrodynamic similarity. Regarding different pipe orientations, ε(L) predictions are comparable to ε(L) from experimental measurements with accepted accuracy: 88% of the predictions are within ±5–15% and 98% are below ±20%. The correlations also were validated by reported results and against correlations available in the literature and show higher prediction accuracy. It is confirmed that the kinematic similarity which is achieved by the gas–liquid velocity ratios and the inertial forces influence the flow pattern and the liquid holdup.

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 ( σ ¯ ), power spectral density (PSD), the width of the characteristic frequency peaks (Δƒ), the skewness ( γ 1 ) and the kurtosis ( γ 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° and upward 30°). 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, σ ¯ and Δƒ. Besides, combining two sets of these statistical graphs which are (a) σ ¯ vs. Δƒ with Δƒ vs. m (or Δƒ vs. total power), (b) Δƒ vs. total power with γ 1 vs. σ ¯ (or γ 2 vs. σ ¯ ), and (c) σ ¯ vs. m with Δƒ vs. m (or Δƒ 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.

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This work involved the simulation of both a multiphase gas–liquid flow and the electromagnetic field representing a two-electrode capacitance sensor in a circular pipe. The simulation investigates in particular the sensitivity of the sensor orientation around the pipe (i.e., top-to-bottom or side-to-side) that best capture the induced flow patterns. The presented numerical work is a simplified simulation by COMSOL multi-physics which was validated by a systematic and an extensive experimental work, using (a) a specially designed simple capacitance sensor (i.e., concave two electrodes), (b) different gas–liquid superficial velocity combinations, (c) different flow section inclinations, and (d) high-speed camera images. The numerical modelling capacitance values were validated against the experimentally measured values which shows a satisfactory level of agreement with a deviation of less than ±2%. The quantity of finite points was between 280,000 and 340,000, which was influenced by the simulated flow pattern. The simulated cases captured the generated flow patterns and their variation inside the pipe, which was in a good agreement when compared to the experimental work as time-dependent values. It was found that the best orientation for the utilized two-electrode capacitance sensor was the top-to-bottom configuration. This is because the sensor’s electrical field distribution was more sensitive, and capable of detecting a greater range of capacitance values. The sensitivity of the top-to-bottom configuration was 1.25–1.64 times greater than that of the side-to-side configuration. Therefore, for design purposes and performance optimization, it is recommended to use the top-to-bottom configuration.

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