Vertical Upward Gas Research Articles

Characterization and visualization of gas–liquid two-phase flow based on wire-mesh sensor

Gas–liquid two-phase flow is prevalent in both industrial production and the natural environment, making the exploration of its flow behavior critically important. In this study, we design a wire-mesh sensor and measurement system to conduct dynamic experiments on vertical upward gas–liquid two-phase flow, obtaining multi-channel data under various conditions. We develop an integrated limited penetrable visibility graph network (ILPVGNet) to quantitatively assess the dynamic transition from slug flow to bubble flow using network metrics. The results demonstrate that this approach effectively integrates multi-channel measurements, revealing the evolution dynamics and internal coupling mechanisms of gas–liquid two-phase flow. To better observe fine flow structures and details, we perform visualization and super-resolution analysis on the multi-channel data, significantly enhancing image clarity and restoring detailed features, thereby improving our ability to intuitively analyze complex gas–liquid flow behaviors.

Structured bubbling in vibrated gas-fluidized beds of binary granular particles: experiments and simulations.

Mixing and segregation of granular particles on the basis of size and density from vertical vibration or upward gas flow is critical to a wide range of industrial, agricultural and natural processes. Recently, combined vibration and gas flow under certain conditions has been shown to create periodically repeating structured bubbling patterns within a fluidized bed of spherical, monodisperse particles. Here, we demonstrate with experiments and simulations that structured bubbling can form in binary mixtures of particles with different size and density, but with similar minimum fluidization velocities. Structured bubbling leads to particles mixing regardless of initial particle configuration, while exciting particles with only gas flow produces smaller unstructured bubbles which act to segregate particles. Discrete particle simulations match the experimental results qualitatively and, in some regards quantitatively, while continuum particle simulations do not predict mixing in the case of structured bubbling, highlighting areas for future model improvement.

Improved phase fraction measurement via non-intrusive optical sensing for vertical upward gas -liquid flow

This paper presents the design and application of a non-intrusive optical sensor for the accurate measurement of phase fractions under a vertical upward gas-liquid flow. The sensor was made of two pairs of emitter and photo receivers operated at a wavelength of 1480 nm. The measurement principle of the sensor is based on the disparity in refractive indexes of gas and liquid that provides a proportional relationship between the phase fraction and light intensity. A flow regime dependent calibration model was then developed which corrects for structural scattering derived from the calibrated sensor responses. The model was able to measure local and average phase fractions under varied flow regimes with errors of ± 1.25% and ± 2% relative to photography and swell level methods respectively with a combined uncertainty of ± 2.3%. Comparisons of the non-intrusive optical sensor with the homogenous, Drift flux and Armard phase fraction correlations showed reasonable agreements. The concept of slip as a dominant effect for flow regime in the slug, churn and annular flow regimes accounted for these disparities. The work demonstrates the efficacy of a low cost, non-intrusive method to determine phase fraction in two phase flow over a wide range of flow conditions.

Flow Regime Identification in Vertical Upward Gas–Liquid Flow Using an Optical Sensor With Linear and Quadratic Discriminant Analysis

Abstract The accurate identification of gas–liquid flow regimes in pipes remains a challenge for the chemical process industries. This paper proposes a method for flow regime identification that combines responses from a nonintrusive optical sensor with linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) for vertical upward gas–liquid flow of air and water. A total of 165 flow conditions make up the dataset, collected from an experimental air–water flow loop with a transparent test section (TS) of 27.3 mm inner diameter and 5 m length. Selected features extracted from the sensor response are categorized into feature group 1, average sensor response and standard deviation, and feature group 2 that also includes percentage counts of the calibrated responses for water and air. The selected features are used to train, cross validate, and test four model cases (LDA1, LDA2, QDA1, and QDA2). The LDA models produce higher average test classification accuracies (both 95%) than the QDA models (80% QDA1 and 45% QDA2) due to misclassification associated with the slug and churn flow regimes. Results suggest that the LDA1 model case is the most stable with the lowest average performance loss and is therefore considered superior for flow regime identification. In future studies, a larger dataset may improve stability and accuracy of the QDA models, and an extension of the conditions and parameters would be a useful test of applicability.

A Deep Branch-Aggregation Network for Recognition of Gas–Liquid Two-Phase Flow Structure

Gas–liquid two-phase flow widely exists in petroleum, natural gas, and other industries. Recognition of flow structure is an important issue in the study of two-phase flow, and it is of great significance for the optimization of industrial processes. Therefore, how to recognize complex flow structures effectively represents a challenge. To cope with this problem, we develop a novel deep learning network to recognize flow structures under different flow conditions. In particular, we conduct vertical upward gas–liquid two-phase flow experiments to obtain the flow structure data set on the basis of images collected by a high-speed camera. Then, we design a branch-aggregation network (BAN), where the branch structure is utilized to increase the width of the network, and multilevel aggregation structure is used to fuse features of different levels. In recognition of flow structure, the proposed network achieves an accuracy of 99.60% with a fast convergence speed and shows the advantages in antinoise ability, which is significant for online recognition in industrial processes. The results indicate that BAN can be a feasible method to recognize complex flow structures.

Thermohydrodynamic analysis of the vertical gas wall and reheat gas wall in a 300 MW supercritical CO2 boiler

Supercritical CO2 (sCO2) Brayton cycle usually shows high efficiency and also has compact structure, which can be considered as a promising alternative in future coal-fired power plants. However, sCO2 boiler has different structures and configurations compared with the conventional steam boiler, especially for the arrangement of radiative heat transfer surfaces. Proper design of vertical gas wall and reheat gas wall is crucial to the success design of sCO2 boiler. In this study, a thermohydrodynamic model of radiative heat transfer surfaces was developed to predict pressure drops and wall temperature distributions. A sCO2 power cycle calculation model was employed to comprehensively analyze the influences of pressure drops on the thermal efficiency. A 300 MW sCO2 boiler was taken into account as the baseline case and thermohydrodynamic analyses of gas wall and reheat gas wall were carried out. The influences of flow directions and mass fluxes on wall temperature, pressure drops and thermal efficiency were obtained by the present models. Then, optimized structures of radiative heat transfer surfaces were proposed, i.e., vertical downward gas wall with tubes of φ34 × 4 mm and vertical upward reheat gas wall with tubes of φ57 × 6 mm. Accordingly, the net cycle efficiency achieved an optimized value of 50.07% under the conditions that the wall temperatures of radiative heat transfer surfaces did not exceed the allowable temperature.

Measurement of Water Velocity in Gas-Water Two-Phase Flow with the Combination of Electromagnetic Flowmeter and Conductance Sensor.

A method to measure the superficial velocity of the water phase in gas–water flow using an electromagnetic flowmeter (EMF) and rotating electric field conductance sensors (REFCSs) is introduced in this paper. An electromagnetic flowmeter instrument factor model is built and the correlation between electromagnetic flowmeter output and gas holdup in different flow patterns are explored through vertical upward gas–water flow dynamic experiments in a pipe with an inner diameter (ID) of 20 mm. Water superficial velocity is predicted based on pattern identification among bubble, churn, and slug flows. The experimental results show that water superficial velocity can be predicted fairly accurately for bubble, churn, and slug flows with a water cut higher than 60% (absolute average percentage deviation and absolute average deviation are 4.1057% and 0.0281 m/s, respectively). The output of the electromagnetic flowmeter is unstable and invalid in slug flows with a water cut below 60% due to the non-conducting gas slug is almost filling the pipe. Therefore, the electromagnetic flowmeter is not preferred to be used in such conditions.

Gravitational instabilities in binary granular materials

The motion and mixing of granular media are observed in several contexts in nature, often displaying striking similarities to liquids. Granular dynamics occur in geological phenomena and also enable technologies ranging from pharmaceuticals production to carbon capture. Here, we report the discovery of a family of gravitational instabilities in granular particle mixtures subject to vertical vibration and upward gas flow, including a Rayleigh-Taylor (RT)-like instability in which lighter grains rise through heavier grains in the form of "fingers" and "granular bubbles." We demonstrate that this RT-like instability arises due to a competition between upward drag force increased locally by gas channeling and downward contact forces, and thus the physical mechanism is entirely different from that found in liquids. This gas channeling mechanism also generates other gravitational instabilities: the rise of a granular bubble which leaves a trail of particles behind it and the cascading branching of a descending granular droplet. These instabilities suggest opportunities for patterning within granular mixtures.

Multi-Scale Morphological Analysis of Conductance Signals in Vertical Upward Gas–Liquid Two-Phase Flow

Abstract The multi-scale analysis is an important method for detecting nonlinear systems. In this study, we carry out experiments and measure the fluctuation signals from a rotating electric field conductance sensor with eight electrodes. We first use a recurrence plot to recognise flow patterns in vertical upward gas–liquid two-phase pipe flow from measured signals. Then we apply a multi-scale morphological analysis based on the first-order difference scatter plot to investigate the signals captured from the vertical upward gas–liquid two-phase flow loop test. We find that the invariant scaling exponent extracted from the multi-scale first-order difference scatter plot with the bisector of the second-fourth quadrant as the reference line is sensitive to the inhomogeneous distribution characteristics of the flow structure, and the variation trend of the exponent is helpful to understand the process of breakup and coalescence of the gas phase. In addition, we explore the dynamic mechanism influencing the inhomogeneous distribution of the gas phase in terms of adaptive optimal kernel time–frequency representation. The research indicates that the system energy is a factor influencing the distribution of the gas phase and the multi-scale morphological analysis based on the first-order difference scatter plot is an effective method for indicating the inhomogeneous distribution of the gas phase in gas–liquid two-phase flow.

The measurement of gas–liquid two-phase flows in a small diameter pipe using a dual-sensor multi-electrode conductance probe

We design a dual-sensor multi-electrode conductance probe to measure the flow parameters of gas–liquid two-phase flows in a vertical pipe with an inner diameter of 20 mm. The designed conductance probe consists of a phase volume fraction sensor (PVFS) and a cross-correlation velocity sensor (CCVS). Through inserting an insulated flow deflector in the central part of the pipe, the gas–liquid two-phase flows are forced to pass through an annual space. The multiple electrodes of the PVFS and the CCVS are flush-mounted on the inside of the pipe wall and the outside of the flow deflector, respectively. The geometry dimension of the PVFS is optimized based on the distribution characteristics of the sensor sensitivity field. In the flow loop test of vertical upward gas–liquid two-phase flows, the output signals from the dual-sensor multi-electrode conductance probe are collected by a data acquisition device from the National Instruments (NI) Corporation. The information transferring characteristics of local flow structures in the annular space are investigated using the transfer entropy theory. Additionally, the kinematic wave velocity is measured based on the drift velocity model to investigate the propagation behavior of the stable kinematic wave in the annular space. Finally, according to the motion characteristics of the gas–liquid two-phase flows, the drift velocity model based on the flow patterns is constructed to measure the individual phase flow rate with higher accuracy.

The measurement of local flow parameters for gas–liquid two-phase bubbly flows using a dual-sensor probe array

Conductivity methods are commonly used in two-phase flow measurement due to their high sensitivity to the conductivity contrast between both phases. In this study, a dual-sensor probe array is newly designed to measure the local parameters of vertical upward gas–liquid two-phase flows in a small inner diameter pipe. Firstly, the measurements of gas volume fraction and gas velocity profiles are conducted using the dual-sensor probe array. Then series of bubble chord lengths at different locations are derived based on the rising velocities of the bubbles, and are compared with a model of maximum bubble diameter. Additionally, based on the simultaneously measured signals from the dual-sensor probe array, the structures of multiple bubble groups are visualized using a technology of phase density image. In view of the existence of the multiple bubble groups, the transformation from bubble chord length distribution to bubble size distribution is conducted based on the decomposition of the multiple bubble groups in the chord length distribution, and the evolution characteristics of the bubble size distributions at the pipe cross section are investigated as the flow condition changing.

Gas–liquid two-phase flow structure in the multi-scale weighted complexity entropy causality plane

The multi-scale weighted complexity entropy causality plane (MS-WCECP) is proposed for characterizing the physical structure of complex system. Firstly we use the method to investigate typical nonlinear time series. Compared with the multi-scale complexity entropy causality plane (MS-CECP), the MS-WCECP can not only uncover the dynamic information loss of complex system with the increase of scale, but also can characterize the complexity of nonlinear dynamic system. In particular, the algorithm of MS-WCECP performs strong anti-noise ability. Then we calculate the MS-WCECP for the conductance fluctuating signals measured from vertical upward gas–liquid two-phase flow experiments in a small diameter pipe, the results demonstrate that the MS-WCECP is a useful approach for exploring the stability and complexity in gas–liquid two-phase flows.

Assessment of a hybrid CFD model for simulation of complex vertical upward gas–liquid churn flow

Gas–liquid multiphase flow can be observed within different industrial processes, and Computational Fluid Dynamics (CFD) can be utilized as a tool for scrutiny of this kind of flows. Although the CFD simulations of multiphase are computationally-demanding, they can deliver a great deal of information. But, the larger point is whether the available CFD multiphase flow models are able to deliver a realistic solution for a complex flow pattern like churn flow? And if yes, to what extent are the results accurate?To shed light on these issues, the Eulerian–Eulerian MultiFluid VOF model offered by ANSYS FLUENT 15 (2015. 15.0 User's Guide, ANSYS Inc.) was used to simulate high flow rate air–water multiphase flow in a 76.2mm-diameter pipe upstream of an elbow in the vertical-horizontal configuration. In the simulations, superficial gas velocity ranged from 10.3m/s to 33.9m/s, and two superficial liquid velocities of 0.3, and 0.79m/s were employed.From the CFD simulations, data such as phase distributions, mean void fractions, and average void fraction time series were extracted. They were then compared to experimental Wire Mesh Sensor (WMS) data formerly obtained. Interestingly, evaluation of the model revealed that it was successful in terms of capturing different liquid structures present within the flow and delivering void fraction data which were in agreement with those of experiments.

Modified liquid entrainment fraction correlation for varying pipe orientation and system pressure

2015 Elsevier Ltd. All rights reserved. Review Cioncolini and Thome (2012) proposed a correlation to predict the liquid entrainment fraction in vertical upward gas–liquid annular two phase flow. Their correlation is based on a two-step method of predictor and corrector technique. This technique calculates an approximate value of liquid entrainment fraction in the first (predictor) step and then recalculates its corrected value in the second (corrector) step. In the predictor step, as shown in Eq. (1), the predicted value of gas core Weber number (Wec,p) is calculated using gas density (qG) [kg/m], superficial gas velocity (USG) [m/s], pipe diameter (D) [m] and the gas–liquid interface surface tension (r) [N/m]. Based on this predicted Weber number, the first estimate of liquid entrainment fraction (Ep) is calculated from Eq. (2) where the multiplying factor (n) of 279.6 and exponents of 0.8395 and 2.209 are determined from the experimental data for vertical upward flow by solving for a nonlinear regression problem and are based on least absolute residuals. Next, the gas core density (qc) [kg/m] based on two phase flow quality (x) and the gas and liquid phase density (qG and qL) is calculated using Ep in Eq. (3) and the corrected values of gas core Weber number (Wec) and liquid entrainment fraction (E) are calculated in Eqs. (4) and (5), respectively. Wec;p 1⁄4 DqGU 2 SG r ð1Þ Ep 1⁄4 ð1þ n We 0:8395 c;p Þ 2:209 where n 1⁄4 279:6 ð2Þ qc 1⁄4 xþ Epð1 xÞ ðx=qGÞ þ Epð1 xÞ=qL ð3Þ Wec 1⁄4 DqcU 2 SG r ð4Þ E 1⁄4 ð1þ n We 0:8395 c Þ 2:209 where n 1⁄4 279:6 ð5Þ The correlation of Cioncolini and Thome (2012) is based on the experimental data set consisting of 2293 data points for circular pipes and 71 data points for non-circular pipes in vertical upward flow. Their correlation also used 96 data points in horizontal two phase flow for preliminary comparison and to validate their correlation (based on data in vertical upward two phase flow). For horizontal flow, Cioncolini and Thome (2012) found a significant scatter between the measured data and the predicted values however; they concluded that the general trend of the liquid entrainment in horizontal flow was correctly captured by their correlation.

Analysis of dynamic of two-phase flow in small channel based on phase space reconstruction combined with data reduction sub-frequency band wavelet

A new method of nonlinear analysis is established by combining phase space reconstruction and data reduction sub-frequency band wavelet. This method is applied to two types of chaotic dynamic systems (Lorenz and Rössler) to examine the anti-noise ability for complex systems. Results show that the nonlinear dynamic system analysis method resists noise and reveals the internal dynamics of a weak signal from noise pollution. On this basis, the vertical upward gas–liquid two-phase flow in a 2mm×0.81mm small rectangular channel is investigated. The frequency and energy distributions of the main oscillation mode are revealed by analyzing the time–frequency spectra of the pressure signals of different flow patterns. The positive power spectral density of singular-value frequency entropy and the damping ratio are extracted to characterize the evolution of flow patterns and achieve accurate recognition of different vertical upward gas–liquid flow patterns (bubbly flow: 100%, slug flow: 92%, churn flow: 96%, annular flow: 100%). The proposed analysis method will enrich the dynamics theory of multi-phase flow in small channel.

Liquid holdup correlation for conditions affected by partial flow reversal

A new method to predict liquid holdup in vertical upward gas–liquid flows is presented based on large scale steady-state experiments in a transparent 42-m long, 0.048-m ID vertical tube system. This work focuses on situations of particular significance in natural gas producing wells, when annular to churn flow-pattern transition brings about drastic holdup increase, leading to a rich group of phenomena in the field, known as “liquid loading”. Under circumstances, believed to precede liquid loading, the still steady-state and stable liquid holdup may be several folds larger than the inlet volumetric fraction of the liquid, due to partial flow reversal. Standard two-phase correlations have difficulties in reproducing the observations in such situations. The proposed method is actually flow-pattern independent and relies on a measure of nearness of the flow condition to the specific condition when partial flow reversal starts to appear. Interestingly, the actual form of this correlation is quite simple, relying on the gas and liquid superficial velocities and mass fractions – in addition to the key parameter – the “critical” superficial gas velocity.

Measurement of gas and liquid flow rates in two-phase pipe flows by the application of machine learning techniques to differential pressure signals

A new method for the determination of gas and liquid flow rates in vertical upward gas–liquid pipe flows has been proposed. This method consists of an application of machine learning techniques on the probability density function (PDF) and the power spectral density (PSD) of the normalized output of a differential pressure transducer connected to two axially separated wall pressure taps in the pipe. The two-phase flow regime was first identified by the application of the elastic maps method on the differential pressure PDF. The transducer signal was then pre-processed using Principal Component Analysis, and independent features were extracted using Independent Component Analysis. The extracted features were used as inputs to multi-layer back-propagation neural networks, which gave the phase flow rates as output. The present method was used to calibrate a differential pressure sensor to estimate the flow rates of both phases in air–water flow in a vertical pipe of diameter 32.5mm and in the pressure range from 100 to 140kPa. Predictions of the present method were in good agreement with direct flow rate measurements. Compared to previously used methods of feature extraction from differential pressure signals, the present method was the only one to have a good, consistent performance over all flow regimes and for all flow conditions encountered in this study.

Flow pattern map and time–frequency spectrum characteristics of nitrogen–water two-phase flow in small vertical upward noncircular channels

We experimentally investigate the vertical upward gas–liquid two-phase flow in a 2×0.81mm small rectangular channel and an equilateral triangle channel with a 2mm side length. The two channels have same hydraulic diameters (Dh=1.15mm). We first present an experimental flow pattern map with nitrogen and water superficial velocities ranging from 0.08m/s to 11.82m/s and 0.12m/s to 1.52m/s, respectively. We also compare the flow pattern transition criteria between the triangle and rectangular cross sections by using the same hydraulic radius. We employ the influence rule that small passage geometry limits the flow pattern transition criteria. Thereafter, we comparatively analyze the transition boundaries of experiment flow patterns with other results in literature and classical prediction models. Results show that the cross-sectional shapes and experimental conditions of the experimental pipeline significantly affect the changes in the flow regime. Given the differential pressure signal of the two-phase flow, we propose two effective quadric time–frequency representations, namely, the adaptive optimal kernel time–frequency representation (AOK TFR) and data reduction sub-frequency band wavelet (DA SFBW) to investigate the complex behavior of vertical upward gas–liquid flows. We extract the positive power spectral density of the singular value–frequency entropy, singular value–frequency entropy, damping ratio, and vibration mode to characterize the evolution of flow patterns. The results suggest that the AOK TFR method can reveal flow dynamic details, whereas the DA SFBW based method is a powerful tool for characterizing the dynamical characteristics of different vertical upward gas–liquid flow patterns.

Identification of flow regime in vertical upward air–water pipe flow using differential pressure signals and elastic maps

A new method for the identification of flow regime in vertical upward gas–liquid flows has been proposed. It consists of an application of the elastic maps algorithm, which is a machine learning method, to the probability density function of differential pressure measurements in pipes. The proposed method was found to be insensitive to axial location of the measurements, pipe diameter within the range 13.9–49.2mm and absolute pressure within the range 100–240kPa; it is therefore amenable to relatively simple calibration in a calibration rig. Compared to three previously suggested machine learning algorithms, the present one had a superior performance in identifying the gas–liquid flow regime.

Effect of Void Fraction and Two-Phase Dynamic Viscosity Models on Prediction of Hydrostatic and Frictional Pressure Drop in Vertical Upward Gas–Liquid Two-Phase Flow

In gas–liquid two-phase flow, the prediction of two-phase density and hence the hydrostatic pressure drop relies on the void fraction and is sensitive to the error in prediction of void fraction. The objectives of this study are to analyze dependence of two-phase density on void fraction and to examine slip ratio and drift flux model-based correlations for their performance in prediction of void fraction and two-phase densities for the two extremes of two-phase flow conditions, that is, bubbly and annular flow or, alternatively, the low and high region of the void fraction. It is shown that the drift flux model-based correlations perform better than the slip ratio model-based correlations in prediction of void fraction and hence the two-phase mixture density. Another objective of this study is to verify performance of different two-phase dynamic viscosity models in prediction of two-phase frictional pressure drop. Fourteen two-phase dynamic viscosity models are assessed for their performance against 616 data points consisting of 10 different pipe diameters in annular flow regime. It is found that none of these two-phase dynamic viscosity models are able to predict the frictional pressure drop in annular flow regime for a range of pipe diameters. The correlations that are successful for small pipe diameters fail for large pipe diameters and vice versa.