Gas-liquid Two-phase Flow Regime Research Articles

Numerical Investigation Into the Gas–Liquid Two-Phase Flow Regime and Heat Transfer Characteristics in a Gravity Heat Pipe

Abstract In order to reveal the gas–liquid distribution, heat and mass transfer characteristics inside a gravity heat pipe, the bubble behavior and the flow regime transition during the phase-change process were examined by employing a copper-water heat pipe, with the length of 500 mm and Φ22 × 1.5 mm. The results indicate that in the process of phase change, the typical flow regimes of bubble flow, slug flow, and churn flow can be observed in the evaporator, and the presence of bubbles has an obvious disturbance on the flow field. In addition, the discontinuous liquid film plays an important role in the heat transfer mechanism in the condenser, which allows the vapor to contact the wall directly and reduces the heat transfer resistance. The temperature difference between the evaporator and the condenser can be reduced by adjusting the saturation temperature, so as to effectively improve the heat transfer performance of the heat pipe and contribute to the practical engineering design.

Flow electrification characteristics of liquid hydrogen in pipe flow

Flow electrification occurs when a dielectric liquid flows along the wall surface, and it may cause electrostatic hazards such as spark discharges. Flow electrification issues of liquid hydrogen should be carefully treated since its low electrical conductivity and dangerous flammability. In this study, flow electrification characteristics of liquid hydrogen in pipe flow are numerically investigated. A theoretical model coupling the electric field and the flow field is introduced to govern the behaviour and distribution of charges, and an interface discharge model is adopted to describe the charge transfer boundary condition. The theoretical model is validated by comparing the simulation with experiment data, and the results show a good agreement with the averaged related deviation below 10%. The result of a common case, with flow velocity u of 1 m/s, pipe radius a 0.01 m, and length L 1 m, shows that the charge density, streaming current, and the electric potential of liquid hydrogen flow are in the magnitude of 10−12 C·m−3, 10−16 A and 10−2 V, respectively. Flow parameters, including flow velocity and pipe size, have a certain effect on the flow electrification characteristics of liquid hydrogen flow. By dimensionless parameterization, it is found that Reynolds numbers (Re), r/a, and x/a are exactly three orthogonal parameters determining the charge density distribution. Furthermore, the effects of electrical conductivity on the distribution of charges and charge dissipation have also been discussed. Finally, the safety assessment of the liquid hydrogen transportation system has been discussed, with extra consideration of the impurities and gas-liquid two-phase flow regime. To sum up, this work presents an in-depth and comprehensive insight into the flow electrification of liquid hydrogen flow, which is of great significance to the transportation safety of liquid hydrogen.

Numerical Gas–Liquid Two-Phase Flow Regime Identification in a Horizontal Pipe Using Dynamic Pressure Data

Gas–liquid two-phase flow is very common in industrial pipelines. Flow regime identification is the first step to design, analyze, and operate the gas–liquid system successfully. The purpose of this study is to develop a methodology for identification of a two-phase flow regime using post signal processing techniques, namely Fast Fourier Transform (FFT) and Probabilistic Density Function (PDF). Three different flow regimes were simulated in a 6 m horizontal pipe with a 0.050 m inner diameter. A Level-Set (LS) method coupled with Volume of Fluid (VOF) method is used to model the air–water interface. After validation of the numerical method, dynamic pressure readings were collected with the intent to identify the associated flow regimes by post-processing of these signals. It was concluded that dynamic pressure signals of different flow regimes show different characteristics (like dominant frequency, FFT amplitude, PDF location and PDF magnitude) in the time and frequency domains. These characteristics can be potentially used as differentiating factors to distinguish different flow regimes. This research is limited to stratified, slug, and annular flow in the horizontal pipe. This paper uses a new approach to identify the flow regime in a horizontal pipe by Fast Fourier Transform and Probability Density Function of dynamic pressure readings obtained by using numerical simulation.

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.

Identification of Horizontal Gas-Liquid Two-Phase Flow Regime using Deep Learning

Two-phase flow is of great importance in various industrial processes. A characteristic feature of two-phase flow is that it can acquire various spatial distribution of phases to form different flow patterns/regimes. The knowledge of flow regime is very important for quantifying the pressure drop, the stability and safety of two-phase flow systems and it holds great significance in petrochemical and thermonuclear industries today. The objective of this study is to develop a methodology for identification of flow regime using dynamic pressure signals and deep learning techniques. Stratified, slug and annular flow regimes were simulated using a Level-Set (LS) method coupled with Volume of Fluid (VOF) method in a 6 m horizontal pipe with 0.050 m inner diameter. Dynamic pressure signals were collected at a strategic location. These signals were converted to scalograms and used as inputs in deep learning architectures like ResNet-50 and ShuffleNet. Both architectures were effective in classifying different flow regime and recorded testing accuracies of 85.7% and 82.9% respectively. According to our knowledge no similar research has been reported in literature, where various Convolutional Neural Networks are used along with dynamic pressure signals to identify flow regime in horizontal pipe. This research provides a benchmark for future research to use dynamic pressure for identification of two-phase flow regimes. This research provides a benchmark for future research to use dynamic pressure for identification of two-phase flow regimes. This study can be extended by collecting data over broader range of flow parameters and different geometries.

Identification of gas-liquid two-phase flow regime in pipelines with low liquid holdup based on ResNet1D-34

Flow pattern identification is an important topic in multiphase flow research. To overcome the subjectivity of manual identification, intelligent identification of flow patterns has attracted much attention in recent years. Both traditional machine learning methods and deep learning methods have been utilized in this field. However, traditional machine learning methods lack accuracy, and existing deep learning methods mostly rely on artificial feature extraction or complex preprocessing. In this paper, we propose a new method with high accuracy and low preprocessing dependency to solve these issues. We modify ResNet, which has proven high performance in computer vision, to fit the data collected by the wire-mesh sensor system (WMS). Due to its outstanding feature extraction ability, the new model can reach high accuracy with simple normalization as the preprocessing step. Additionally, the model can directly process data at various scales without retraining or rebuilding, which gives it high usability and economic value. The experimental results show that the accuracy of this method can reach 99.58% on our dataset.

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.

Study on the gas-liquid two-phase flow regime in the power generation channel of a liquid metal MHD system

The characteristics of flow regime transition in the power generation channel of a liquid metal MHD system is numerically investigated in the present paper, under the flow regime of stratified flow and annular flow at the inlet, respectively, based on a cuboid channel model. The findings indicate that, after the stratified flow and annular flow pass through the power generation channel, jet flows will be generated in the region of high gas volume fraction, and the maximum jet flow velocity will increase with the external magnetic field. Despite the different inlet flow regimes, the flow regime in the power generation channel transforms towards a mixed flow. In addition, a power generation channel with a liquid metal inlet at the top was designed, which can effectively transform the stratified flow into the mixed flow, and the output power of the generation channel grows with the accelerated liquid metal at the inlet.

Identification of high-pressure two-phase flow regime transition using image processing and deep learning

The commercial production process of methane gas from methane hydrate has been developed by the MH21-S R&D consortium (MH21-S) in recent years. In order to establish the gas production method, an accurate gas-liquid two-phase flow regime identification under high-pressure conditions is indispensable. In the present study, a 28 m long test section with an inner diameter of 52.7 mm was newly constructed, and a nitrogen-water two-phase flow experiment was conducted at 3.0 MPa G. Two-phase flow images were successfully acquired using the high-speed camera from the visualization port. The sequential image of upward gas-liquid two-phase flow was merged into a single image by the time strip method. Then, flow regime identification using the convolutional neural network (CNN) trained under sequential images was carried out. The proposed method has shown an excellent capability to identify flow regime images obtained at the current high-pressure conditions. In addition, for the slug flow regime, it was found that the Taylor bubble length highly influences the identification performance, and the model proposed by Street and Tek showed good agreement for the present dataset. Furthermore, the bubbly-slug transition region was successfully evaluated and quantified through the present model. The current methods have shown a good correlation with the existing model. These results indicate that the gas void fraction at the transition region to be larger than the theoretical value for the elevated pressure system.

Gas-liquid flow regimes identification using non-intrusive Doppler ultrasonic sensor and convolutional recurrent neural networks in an s-shaped riser

The problem of gas-liquid (two-phase) flow regime identification in an S-shaped riser using an ultrasonic sensor and convolutional recurrent neural networks (CRNN) is addressed. This research systematically evaluates three different schemes with four CRNN-based classifiers over fourteen experiments. Four metrics are used as the evaluation criteria: categorical accuracy, categorical cross-entropy, mean square error (MSE), and computation graph complexity. Compared with existing results, a compatible performance is achieved while considerably reducing the model complexity. The testing and validation accuracies were 98.13% and 98.06%, while the complexity decreased by 98.4% (only 117,702 parameters). The proposed approach is i) accurate, low complexity, and non-intrusive and hence suitable for industry, and ii) could provide a benchmark for flow regime identification.

Gas-Liquid Two-Phase Flow Regime in a Horizontal Channel Under Transverse Vibration

This paper presents experiments carried out to study the influences of transverse vibration on horizontal gas-liquid two-phase flow. The experiments considered the transverse vibration over a wide range of vibration frequencies and vibration amplitudes. Five typical flow regimes were observed. The fluid flow conditions in steady state and transverse vibration were compared. The traditional flow regime transition lines did not match the gas-liquid flow in channel under transverse vibration. The results suggest that vibration frequency mainly affected the flow regime by promoting the aggregation of gas slug, while vibration amplitude mainly influenced the fluctuation height of the gas and liquid interface.

МОДЕЛИРОВАНИЕ ВЛИЯНИЯ ПОЛОЖЕНИЯ ТРУБОПРОВОДА НА РЕЖИМ МНОГОФАЗНОГО ПОТОКА

The paper investigates the effect of an inclination angle of a straight pipeline on the gas-liquid two-phase flow regime at the same inlet conditions. The homogeneous Volume of Fluid (VOF) model in Ansys Fluent is used in this study for simulating the spread of the gas phase, changes in the entrance region and establishing of the multiphase flow regime. The authors consider a horizontal, vertical and inclined (60 degrees) pipelines where the stratified, bubbly and churn flow regimes are formed respectively, and the results are presented in terms of the gas phase distribution in the volume of the carrying liquid and the velocity field.

Study on the Gas-Liquid Two-Phase Flow Regime in the Power Generation Channel of a Liquid Metal Mhd System

Study on the Gas-Liquid Two-Phase Flow Regime in the Power Generation Channel of a Liquid Metal Mhd System

Identification of Upward Two-Phase Flow Regime Transition Region under High-Pressure Condition Using Deep Learning

Currently, the MH21-S R&D consortium (MH21-S), supported by the Ministry of Economy, Trade, and Industry (METI), is developing the commercial production process of methane gas from methane hydrate. In order to develop a gas production method, the objective flow regime identification technique of gas-liquid two-phase flow under high-pressure conditions would be useful. Therefore, in the present research, a new method for identifying the flow regime by a convolutional neural network (CNN) using high-speed images obtained at a narrow view was proposed, and its performance was evaluated. Specifically, the upward gas-liquid two-phase flow images were recorded using a high-speed camera, and they were merged into a single image containing information from entire frames. The CNN was created and trained with these newly merged images, and it was utilized to identify flow regimes of unlabeled test images. As a result, the proposed method using the CNN model was capable of identifying the flow regime with high accuracy. Thus, the present study has shown that the CNN model with the proposed image conversion method can properly identify the upward gas-liquid two-phase flow regime. In addition, by utilizing the developed network, the bubbly-slug transition region, which has only been studied through visual observation or void fraction signals in the past, was successfully evaluated quantitatively.

Flow Regimes Identification-based Multidomain Features for Gas–Liquid Two-Phase Flow in Horizontal Pipe

Accurate flow regimes identification is of great significance for the deep understanding of the flow structure and ensuring the safe and economic operation of actual production in the multiphase flow process. For the flow regimes identification of the gas-liquid two-phase flow in the horizontal pipe, a multidomain features processing scheme is proposed. The water holdup data are acquired by conductance rings sensor, and processed by the improved empirical wavelet transform (EWT) to obtain the Fourier spectrum in the frequency domain and decomposition components in the multiscale domain. The obtained multidomain feature information is quantified to get the high-dimensional feature vectors of different flow regimes. To make the features more representative and realize the visualization of the structural relationship between the flow regime samples, the isometric feature mapping (Isomap) method in manifold learning is used to synthesize the feature structure relationships and map them to low-dimensional space. The obtained low-dimensional feature vectors of training samples are input into the support vector machine (SVM) to acquire the flow regimes identification model. Compared with other flow regimes identification schemes that feature reduction uses different manifold learning methods, or feature extraction is not in multidomain, or data preprocessing uses traditional wavelet transform, the proposed scheme can better retain the global structural relationship of the flow regimes, has the characteristics of strong stability, and the identification rate of the gas-liquid two-phase flow regimes in the horizontal pipe is over 95%.

Identification of Rising Gas-Liquid Two-Phase Flow Regimes Using Ultrasound with Machine Learning

Identification of Rising Gas-Liquid Two-Phase Flow Regimes Using Ultrasound with Machine Learning

Numerical Study of Gas–Liquid Two-Phase Flow Regimes for Upward Flow in a Helical Pipe

This study presents numerical investigations of air–water two-phase flows for upward flow in a vertical helical pipe. The numerical simulations were done in accordance with the experimental work of...

Detection of the gas–liquid two-phase flow regimes using non-intrusive microwave cylindrical cavity sensor

Gas–liquid two-phase flow phenomenon occurs in various engineering applications and the measurement of it is important. A microwave sensor in the form of a cylindrical cavity has been designed to operate between 5 and 5.7 GHz. The aim is to analyse a two phase gas–liquid flow regime in a pipeline. LabVIEW software is utilised to capture the data, process them and display the results in real time. The results have shown that the microwave sensor has successfully detected the two-phase flow regimes in both the static and dynamic flow environments with reasonable accuracy. The study has also shown the independence of the technique and its accuracy to the temperature change (28–83 °C). Several flow regimes of the gas–liquid two-phase flow have been discussed. The system is also able to detect the stratified, wavy, elongated bubbles and homogeneous flow regimes.

Experimental identification of flow regimes in gas–liquid two phase flow in a vertical pipe

In this experimental study a new flow regime map for gas–liquid two-phase flow regime detection is presented based on interpretation of transient flow pressure signals and utilizing neural networks. In this regard, the pressure signals of an air–water two phase flow at different location on a vertical pipe with an inside diameter of 50 mm and a total length of 6 m are measured. Averaged pressure and pressure fluctuation data were analyzed in different flow regimes along the upriser pipe to recognize the effects of flow regimes on pressure response. A set of pressure parameters are introduced based on averaged pressure data to present meaningful criteria for flow regime identification. Pressure fluctuations also used for flow regime recognition by applying two common signal processing methods. The processed signals are used as the inputs of a feed forward back propagation neural network to predict the flow regime. The neural network outputs are then used to present a flow regime map. The proposed neural network flow map is compared with the experimental data by photographic flow visualization technique. This comparison shows an acceptable agreement between the neural network outputs and experimental results.

Application of gas/liquid two-phase flow in cross-flow microfiltration of oil-in-water emulsion; permeate flux and fouling mechanism analysis

The effects of gas–liquid two-phase flow regimes on permeate flux and fouling mechanisms were investigated in a gas sparging assisted microfiltration of oil-in-water emulsion. Different two-phase flow patterns were attained by introducing gas stream into the liquid phase. It was found that the permeate flux was increased due to disruption of the local deposited cake layer and concentration polarization as the gas velocity increased. Flux enhancement of up to 35% was observed at slug-flow pattern and it was also found that gas sparging is less efficient at high liquid velocities in which turbulence was high. Fouling mechanisms were examined through four individual blocking laws. Membrane resistance curves were used to find dominant fouling mechanism during filtration and as a result, cake formation showed the best agreement with the experimental data in flux decline prediction. The effect of oil droplets on the membrane surface, pore blockage, and accumulation of fouling layer were also analyzed through scanning electron microscopy images.