Gas-liquid Flow Regime Research Articles

Gas liquid flow pattern prediction in horizontal and slightly inclined pipes: From mechanistic modelling to machine learning

This paper investigates the prediction of two-phase gas-liquid flow regimes in both horizontal and slightly inclined pipes. For this purpose, the mechanistic model of Taitel et al. (1976) and the machine learning approach have been adopted. First, the mechanistic model was implemented, tested and optimised by introducing factors in the transition equations to determine the configuration that gives the highest prediction accuracy for a specific two-phase system for which experimental data points are available. Second, several machine learning models are trained, tested and additionally validated. This is done by splitting the experimental data set corresponding to the pipe inclination range (−10∘ to 10∘) into training, test and validation sets. The best classifier achieved an accuracy of 95.5% after the test step and up to 98.9% after the validation step. Finally, the Taitel et al. model with the optimal configuration and the best machine learning classifier (XGB classifier) are used to generate the two-dimensional flow regime map.

Bubble generation in a 3-D flow-focusing microchannel: From squeezing to jetting

Fine bubbles have been applied in many fields; however, the controllable preparation of monodispersed fine bubbles remains a challenge. In this study, a three-dimensional (3-D) flow-focusing microfluidic device was designed. Compared with bubble formation in one-dimensional (1-D) and two-dimensional (2-D) flow-focusing, the 3-D flow-focusing microchannel owns the features of making bubbles with much smaller sizes and wider operation range. Four different gas–liquid flow regimes (squeezing, squeezing-dripping transition, dripping, and jetting) and three different gas–liquid dispersion states (mono-dispersion, bi-dispersion, and poly-dispersion) were observed in the 3-D flow-focusing microchannels. Importantly, the bi-dispersion in the squeezing-dripping transition regime experimentally verified that the squeezing and viscous shearing mechanisms separately control bubble formation in the transition region based on the time sequence. The intrinsic reason for poly-dispersion in the squeezing-dripping transition and jetting regimes was revealed. Finally, a mathematical model is proposed to predict the bubble size in the flow-focusing microchannels.

Gas-liquid flow regimes in a novel rocking and rolling flow loop

Predicting two-phase flow pattern characteristics and flow transition is fundamental to address several industrial problems, e.g. for the oil and gas industry. In order to fulfil the upscaling from the laboratory to the industrial scale, resource-efficient flow testing facilities which closely replicate industrial flow characteristics are needed. To address this, we introduce a new experimental device named as the Rocking and Rolling Ring Flow Loop (3RFL), which is size, cost, and time-efficient. In the present work, we employ the 3RFL apparatus at atmospheric pressure and temperature, with air and water as working fluids. However, the ultimate goal of our work is to build an experimental set-up capable of capturing the under-pressure reactive multiphase flow (gas-liquid–solid) dynamics typical of flow assurance in oil production and transportation. At the moment, the 3RFL can induce different flow regimes by adjusting the system control parameters such as rocking angle, rocking rate, and liquid volume fraction, all without requiring a pump. We observe three flow regimes, and analyse the impact of control parameters on their emergence. Our findings reveal that flow regime transitions are influenced by the competition between gravitational and centrifugal forces, which arise due to the curvature of the tube. Among three employed modelling strategies, namely mechanistic modelling, total energy minimization and a combined approach, we find that the total energy minimization model best compares to the experimental liquid height.

Hydrodynamics and wet pressure drop model based on flow regimes of the novel Flow-Divided Baffle-Bridge Tray

This study introduces the Flow-Divided Baffle-Bridge Tray (FDBBT), an innovative tray design featuring hole-bridge structures at the caps’ base for pre-distribution of gas and liquid, and stepped baffles configuration at the caps’ top to minimize entrainment. The pressure drop, weep and entrainment of FDBBT were evaluated in a Φ796 mm experimental setup. Compared to the sieve tray and Glitsch V-1 valve tray, FDBBT demonstrated lower pressure drops and significantly reduced entrainment, suggesting a higher operational ceiling. Furthermore, a specialized single-cap experimental apparatus was designed to assess the lifted liquid rate and uncover gas-liquid flow regimes within the cap, namely churn flow, free-surface flow, and periodic-free-surface flow. Employing data from the single-cap experiments, complemented by CFD-assisted analysis, FDBBT's pressure drop model was established and then validated by the Φ796 mm experiments, with most deviations falling within a 5 % threshold.

Experimental study on gas-liquid flow regimes of coaxial mixers equipped with a Rushton/pitched blade turbine and anchor

In view of the close relationship between gas-liquid mass transfer performance and flow regime, the coaxial mixer was selected as the research object, and the effects of rotation mode, inner impeller diameter, gas flow rate, and viscosity on flow regimes were experimentally investigated. The results showed that the main growth interval of the mass transfer coefficient was the loading regime, and the critical complete dispersion regime was an efficient and economic operation condition. In this state, the co-rotation mode had a low-power advantage compared to the counter-rotation mode. The anchor speed and gas flow rate could improve mass transfer performance, but at the expense of higher power consumption. Although it was easier to achieve the complete dispersion regime with a larger diameter impeller, the mass transfer performance was reduced at this time. The viscosity had a significant negative influence on gas-liquid dispersion and mass transfer performance. In terms of impeller combinations, PBTD+Anchor and RT+Anchor performed well in low and high viscous systems, respectively. In addition, applying the artificial neural network model to estimate the Ncd for the coaxial mixer correlated with the studied parameters to recognize the complete dispersion regime.

Smoothed particle hydrodynamics modelling of multiphase flows: an overview

AbstractSmoothed particle hydrodynamics (SPH) is a meshless, particle-based approach that has been increasingly applied for modelling of various fluid-flow phenomena. Concerning multiphase flow computations, an advantage of the Lagrangian SPH over Eulerian approaches is that the advection step is straightforward. Consequently, the interphasial surface can be explicitly determined from the positions of particles representing different phases; therefore, there is no need for the interface reconstruction step. In this review paper, we briefly recall the basics of the SPH approach, and in particular the physical modelling and numerical implementation issues. We also mention the weaknesses of the approach and some remedies to overcome them. Then, we demonstrate the applicability of SPH to selected interfacial flow cases, including the liquid column break-up, gas–liquid flow regimes in a channel capturing the transitions between them and the wetting phenomena. Concerning the two-fluid modelling, it is illustrated with sediment transport in the presence of surface waves. Various other applications are briefly recalled from the rich and growing literature on the subject, followed by a tentative list of challenges in multiphase SPH.

Machine Learning for Two-Phase Gas-Liquid Flow Regime Evaluation Based on Raw 3D ECT Measurement Data

Machine Learning for Two-Phase Gas-Liquid Flow Regime Evaluation Based on Raw 3D ECT Measurement Data

Flow regime transition and bubble behavior in gas-liquid two-phase impinging jet reactor

The gas-liquid flow regimes transition mechanism and bubble behavior in gas-liquid two-phase impinging jet reactor are studied by numerical method. The liquid phase velocity, gas-liquid flow regimes, bubble behavior in the impinging reactor under different gas and liquid velocities are analyzed to explore. The vortex in flow field changes periodically, and the period is 7.5 s. According to the dispersion mode of bubbles in the reactor, the gas-liquid flow regimes can be divided into three regimes: cluster upflow, gradual dispersion and complete dispersion. The critical condition equations of flow regimes transition are established combination of Flow number (Fl) and Froude number (Fr). The distribution range of bubble diameter in the reactor increases with the increase of gas velocity and decreases with the increase of liquid velocity. The turbulent kinetic energy, turbulent dissipation rate and the velocity in the flow field are gradually weakens from the impact center to the radial direction. Based on dimensionless analysis, the mathematical model of bubble Sauter diameter is established to predict bubble Sauter diameter accurately. This research provides a theoretical basis for the amplification and optimization of reactors.

Quantitative Validation of Gas-Liquid Flow Regime Transition Using Eulerian-Eulerian CFD Models

Quantitative validation of a computational fluid dynamics (CFD) code is about making point-to-point comparisons over fields of data to make statements about predictive fidelity. By the very nature of CFD, these comparisons are high-performance computing and data intensive. This presentation provides an overview of workflow development toward quantitative development of two-phase CFD codes. The focus here is on multifield two-fluid model capability implemented in the commercial CFD code ANSYS CFX applicable to boiling gas-liquid two-phase flows. Applications to validating three-dimensional predictions that span two-phase flow regimes are discussed.

Investigation of boiling hydrogen heat transfer characteristics under low-pressure conditions

Understanding the heat transfer characteristics of boiling hydrogen is a critical issue in developing of liquid hydrogen system. However, there has been insufficient systematic research on boiling hydrogen flow characteristics. This report investigates the heat transfer characteristics of boiling hydrogen, following our previously reported gas-liquid mixing characteristics and flow regime transition characteristics. This study generated an arbitrary gas-liquid two-phase flow state in 15 mm tube by experimentally heating liquid hydrogen with an electric current. The experimental conditions were a pressure of 250 – 300 ▪, a mass flux of 50 – 110 ▪, and a heat flux of 0 – 12 ▪. Based on the experimentally obtained heat transfer coefficients, a heat transfer prediction model for boiling hydrogen was constructed, considering the change in the heat transfer process from liquid single-phase to saturated boiling. A one-dimensional flow simulator was also constructed that applies the proposed model and confirms that the model can be reduced the wall temperature error to about 1/10 against the conventional models.

Nature and characteristics of gas–liquid flow regimes in a micro‐packed bed reactor

AbstractMicro‐packed bed reactors (μPBRs) have the advantages of high heat and mass transfer efficiency and excellent safety, and they have been successfully applied to hydrogenation and oxidation reactions. However, the study of gas–liquid flow regimes in the μPBR, which is essential for the mass transfer modeling and reactor scale‐up, is still insufficient due to the limitation of micro‐scale and complexity of capillary force. In this work, the flow regimes in the two‐dimensional μPBR were systematically studied by visual method utilizing a high‐performance camera. Four typical flow regimes and characteristics were captured, and flow regime transition was revealed. Effects of gas and liquid superficial velocities, liquid physical properties, and particle sizes on liquid spreading areal fraction and pressure drop were investigated. Flow regime transition correlation of churn flow and pseudo‐static flow in the μPBR was provided for the first time based on the summary of the current and previous published results.

Comparative Performance of Machine-Learning and Deep-Learning Algorithms in Predicting Gas–Liquid Flow Regimes

Gas–liquid flow is a significant phenomenon in various engineering applications, such as in nuclear reactors, power plants, chemical industries, and petroleum industries. The prediction of the flow patterns is of great importance for designing and analyzing the operations of two-phase pipeline systems. The traditional numerical and empirical methods that have been used for the prediction are known to result in a high inaccuracy for scale-up processes. That is why various artificial intelligence-based (AI-based) methodologies are being applied, at present, to predict the gas–liquid flow regimes. We focused in the current study on a thorough comparative analysis of machine learning (ML) and deep learning (DL) in predicting the flow regimes with the application of a diverse set of ML and DL frameworks to a database comprising 11,837 data points, which were collected from thirteen independent experiments. During the pre-processing, the big data analysis was performed to analyze the correlations among the parameters and extract important features. The comparative analysis of the AI-based models’ performances was conducted using precision, recall, F1-score, accuracy, Cohen’s kappa, and receiver operating characteristics curves. The extreme gradient boosting method was identified as the optimum model for predicting the two-phase flow regimes in inclined or horizontal pipelines.

Gas-liquid flow regimes and effective interfacial area in a solid foam block stirred tank

Recently, a solid foam block (SFB) stirred tank has attracted wide attention owing to its excellent mass transfer efficiency. However, more fundamental information is needed to deepen the understanding of the strengthening mechanism and application of this novel stirrer. Therefore, in this work, the flow regimes and the effective interfacial area as the crucial information for the mass transfer process in the SFB stirred tank are investigated by high-speed imaging and the Danckwerts-plot technique. Four typical flow regimes are revealed in the SFB stirred tank, which corresponds to the three changes in the effective interfacial area from 30 to 1490 m − 1 when adjusting the operating conditions. Correlations are respectively developed with the deviation of ±10% and ±15% to distinguish the flow regimes and predict the effective interfacial area in SFB stirred tank. Compared with the Rushton stirred tank, the SFB stirred tank is able to generate two times effective interfacial areas at fixed power input, showing superior performance and the promising prospect in some certain multiphase application fields.

Development of Gas-Liquid Flow Regimes Identification Using a Noninvasive Ultrasonic Sensor, Belt-Shape Features, and Convolutional Neural Network in an S-Shaped Riser.

The problem of classifying gas-liquid two-phase flow regimes from ultrasonic signals is considered. A new method, belt-shaped features (BSFs), is proposed for performing feature extraction on the preprocessed data. A convolutional neural network (CNN/ConvNet)-based classifier is then applied to categorize into one of the four flow regimes: 1) annular; 2) churn; 3) slug; or 4) bubbly. The proposed ConvNet classifier includes multiple stages of convolution and pooling layers, which both decrease the dimension and learn the classification features. Using experimental data collected from an industrial-scale multiphase flow facility, the proposed ConvNet classifier achieved 97.40%, 94.57%, and 94.94% accuracy, respectively, for the training set, testing set, and validation set. These results demonstrate the applicability of the BSF features and the ConvNet classifier for flow regime classification in industrial applications.

Effect of N2 and CO2 bubbles on gas volume fraction and flow pattern in a quasi-2D bubble column by shadow imaging

This study reports the effect of N2 and CO2 bubbles on dilute to dense gas-liquid two-phase bubbly flow. A shadowgraph imaging technique captured bubble images at a high spatiotemporal resolution. The recordings of bubble images allow us to compute gas fraction distribution. It requires challenging segmentation and gas-liquid interface detection approaches in image processing. Hence a novel gas contour characterization technique has been introduced in this study that analyses light intensity per pixel for quantifying the effect of local gas volume fraction. The dominant gas structure and repetitive gas pattern have also been determined here using Fourier transform-based power spectral density and 2D cross-correlation functions, respectively. Gas-liquid flow regimes of dissolved CO2 bubbles are found quite different than that of N2 bubbles. The plausible reasons are that gas fraction distribution at the sparger region may inhibit bubble coalescence and the positive surface charge of CO2 bubbles acts as a barrier to the interface deformation.

Gas-liquid flow regime identification via a non-intrusive optical sensor combined with polynomial regression and linear discriminant analysis

The identification of gas-liquid flow regimes for co-current upward flow in a vertical pipe is investigated using Polynomial regression (PR) and Linear Discriminant analysis (LDA) on the response from a non-intrusive optical sensor. The average voltage and associated standard deviation can be extracted as features from the sensor and used as inputs to establish the coefficients of an optimal regression model. Further to this, due to the structural similarity between the slug and churn flow regimes, further discrimination was required in the form of a Linear discriminant analysis to give accurate classification between these two flow regimes. According to the offline test and real-time flow conditions considered, a classification accuracy of 100% is achieved. The results of this paper show the capability of applying a relatively simple supervised regression model and LDA to the response from a non-intrusive optical sensor to identify gas-liquid flow regimes efficiently and accurately.

Signal optimization for recognition of gas–liquid two-phase flow regimes in a long pipeline-riser system

Accurate and fast recognition of multiphase flow regimes is an urgent requirement for the flow assurance of oil/gas pipelines. Experiments on gas–liquid flow regimes are conducted on a 1657 m horizontal pipeline system with a 16.7 m S-shaped riser. The flow regimes are classified as severe slugging, oscillating flow and stable flow, based on quantitative criteria of riser pressure difference. Data from the accumulation stage of severe slugging is selected for training the model, and a new optimization scheme is proposed for the recognition process from feature extraction and selection to model construction and testing. The influence of the measurement distance and location on the recognition rate is revealed, and a new set of signal evaluation and optimization criteria is proposed, covering the recognition rate (higher than 90%), reliability and practicality. An accurate recognition rate over 90% is yielded by using above-water signals with a sample duration of 6.2 s.

Liquid mixing time and gas distribution in aerated multiple-impeller stirred tanks

Gas-liquid fluid dynamics and mass transfer are crucial aspects of aerobic fermentation and robust methodologies for their determination in industrial bioreactors are expected to provide significant improvements in many production processes. In this work, a gas-liquid stirred tank of high aspect ratio, that replicates the geometry of typical industrial aerated fermenters, is investigated. In particular, the liquid phase homogenization dynamics and the gas phase spatial distribution are determined. The selected methodology is based on the analysis of the conductivity measurements obtained by Electrical Resistance Tomography. The gas-liquid flow regimes and the mixing time are identified at various gas flow rates and impeller speeds, thus covering different gas-liquid regimes. Data collected with vertical and horizontal arrangements of the electrodes allow to obtain a detailed picture of the equipment working mode and to gain insight into the gas-liquid flow dynamics under optically inaccessible conditions. Quantitative evaluation of the reliability of the collected data is attempted by comparing the results obtained with the vertical and horizontal arrangements in the same locations.

Recognition of gas-liquid flow regimes in helically coiled tube using wire-mesh sensor and KNN algorithm

• Flow patterns in helically coiled tube are classified by visual observation. • Void fraction for entire cross-section is measured by wire-mesh sensor. • k -Nearest Neighbors algorithm is utilized to identify flow patterns objectively. • Influence of centrifugal force and secondary flow on phase distribution and flow transition is revealed. Helically coiled tube (HCT) is used extensively in compact types of heat exchangers. Complete knowledge of gas-liquid flow behavior in HCT is essential in designing the heat transfer equipment involving phase change. Although the gas-liquid flow patterns in straight tubes have been widely studied, investigations on flow pattern recognition in HCT are still scarce. In this paper, based on the backlight imaging tomography and self-designed wire-mesh sensor, air-water experiments are carried out to study the characteristics of gas-liquid flow in a transparent HCT. Bubble, plug, slug, wavy and annular flow are observed, and their void fraction at the cross-section of the tube are measured and analyzed using the statistic parameters of time-trace, PDF and CPDF. Owing to the simplicity of implementation, the void fraction data are subsequently utilized along with the k -Nearest Neighbors algorithm (KNN algorithm) to identify the flow patterns objectively. Finally, a flow regime map is proposed and compared with the previous straight flow map and HCT map in the literatures. Accordingly, the influence of curvature-induced centrifugal force and secondary flow on the phase distribution and flow-regime transition is revealed in the present study.

Investigation of the gas-liquid flow hydrodynamics in minichannels of solar collectors under isothermal conditions

In the paper the new experimental data are presented to study the gas-liquid flow in minichannels with a thickness of 1 mm and 1.2 mm in isothermal conditions. The design (fluid input conditions) and the width of the channels were changed. The boundaries between the patterns of the gas-liquid flow were determined. Gas-liquid flow regimes maps were created. The patterns of a gas-liquid flow and transition between flow patterns were analyzed in detail. It made possible to find out the features of the gas-liquid flow in the solar collectors minichannels.