Annular Flow Regime Research Articles

Boiling heat transfer of water on smooth and square pin fins surfaces in minichannel

In this investigation, flow boiling heat transfer and hydrodynamic instability using deionized water in a minichannel with smooth and square pin fin surfaces was experimentally examined. The study is aimed to assess local and overall heat transfer coefficients, heat transfer coefficients, pressure drop and Nusselt number across various liquid mass flux ranges (50–93 kg m−2 s−1) and a constant heat flux of 46.91 kW/m2. The minichannel, with a hydraulic diameter of 2.85 mm, features a rectangular shape. Experimental testing was conducted on aluminum surface within a minichannel of dimensions 270 mm in length, 30 mm in width, and 1.5 mm height. The square pin fins are integrated at the base of the boiling surfaces, arranged in staggered form. The results revealed that the boiling heat transfer coefficient on square pin fin surfaces was approximately 88 % higher than that on smooth surfaces. Additionally, a notable increase in local heat transfer coefficient was observed for square pin fin surfaces (i.e., 34.64 kW/m2.K) compared to smooth surfaces (8.09 kW/m2.K) at the same mass flux of 50 kg/m2.s. Besides, the square pin fins surface exhibited a lower and stable pressure drop, particularly near annular flow regimes, compared to the smooth surface. Notably, the highest pressure drop observed was 4.8 kPa for the square pin fins surface, while the smooth surface experienced a higher pressure drop of 6.36 kPa, accompanied by more pronounced pressure fluctuations during annular flow.

Development of analytical equations for void fraction in biphasic systems using gamma radiation and MCNP6 code

This study presents the development of mathematical equations for calculating void fractions in pipes using gamma densitometry. A traditional measurement setup, consisting of a137Cs point source and a NaI(Tl) scintillator detector, was simulated using the Monte Carlo method via the MCNP6 code. To validate the proposed equations, water-gas biphasic models were simulated in tubes with square and cylindrical cross-sections, varying diameters, and radiation sources (241Am, 137Cs, 60Co) through gamma-ray transmission. A comparative analysis with existing equations from the literature was conducted. The void fractions, determined from the transmission photopeak, were in close agreement with the actual values. The proposed equations demonstrated a maximum mean relative error of 0.21% for cylindrical tubes in stratified and annular flow regimes.

Volume fraction detection in multiphase systems using neutron activation analysis and artificial neural network

This study presents an application of an Artificial Neural Network (ANN) to detect fluids in an annular flow regime using Prompt-Gamma Neutron Activation Analysis (PGNAA). The ANN was trained using gamma-ray spectra resulting from neutron interactions with chemical elements found in fluids typical of multiphase flow in oil exploration. These spectra were generated through mathematical simulation using the MCNP6 Monte Carlo computer code to model nuclear particle transport. A241Am-Be polyenergetic neutron source was simulated for these calculations. Several combinations of fluid fractions were developed to create a dataset used for both training and evaluation of the ANN. The ANN demonstrated robust generalization capabilities by accurately predicting the volume fraction of the three investigated fluids (saltwater, oil, and gas), even for cases not included in the training phase. The combination of ANN and PGNAA proved effective for analyzing multiphase systems, with over 92% of all showing errors of less than 5%.

Modeling and simulation of air-water upward annular flow characteristics in a vertical tube using CFD

Annular flow refers to a special type of two-phase flow pattern in which liquid flows as a thin film at the periphery of a pipe, tube, or conduit, and gas with relatively high velocity flows at the center of the flow section. This gas also includes dispersed liquid droplets. The liquid film flow rate continuously changes inside the tube due to two processes-entrainment and deposition. To determine the liquid holdup, pressure drop, the onset of dryout, and heat transfer characteristics in annular flow, it is important to have proper knowledge of flow characteristics. Especially a better understanding of entrainment fraction is important for the heat transfer and safe operation of two-phase flow systems operating in an annular two-phase flow regime. Therefore, the objective of this work is to develop a computational model for the simulation of the annular two-phase flow regime and assess the various existing models for the entrainment rate. In this work, Computational Fluid Dynamics (CFD) in ANSYS FLUENT has been applied to determine annular flow characteristics such as liquid film thickness, film velocity, entrainment rate, deposition rate, and entrainment fraction for various gas-liquid flow conditions in a vertical upward tube. The gas core with droplets was simulated using the Discrete Phase Model (DPM) which is based on the Eulerian-Lagrangian approach. The Eulerian Wall Film (EWF) model was utilized to simulate liquid film on the tube wall. Three different models of Entrainment rate were implemented and assessed through user-defined functions (UDF) in ANSYS. Finally, entrainment for fully developed flow was determined and compared with the experimental data available in the literature. From the simulations, it was obtained that the Bertodano correlation performed best in predicting entrainment fraction and the results were within the ±30 % limit when compared to experimental data.

Dynamic Simulation of the Stability of Annular Flows in Gas Wells

This paper provides a holistic view of the development and progression of multiphase gas-liquid annular flow in gas wells. With the help of a momentum balance, the stability of a fully developed annular flow is defined. The Euler-Euler model was used to model the multiphase flow system, while the Reynolds-Averaged Navier–Stokes (RANS) model was used to depict a time-dependent solution for a vertical fluid flow system, considering acceleration due to gravity and fluid compressibility. The resultant flow regime was tested for stability by gradually reducing the field velocity of flow and observing the effects using a computational fluid dynamics (CFD) software. The principal parameters considered are the superficial gas velocity and liquid film flow rate. The properties of the region where the flow becomes unstable are documented and related to the incidence of liquid loading in a typical gas well. The effect are critically observed and compared to flow regimes at lower superficial gas velocity. The resultant flow regime configurations are analyzed and compared to the annular flow system. This study will help researchers understand the behaviour of gas wells that have potential in developing liquid loading problems and the movement of liquid film on the wall of the tubing when in annular flow regime.

Liquid droplet entrainment in an annular flow boiling regime—A Bayesian regularization algorithm based study

Accurate prediction of the entrained liquid droplet fraction in an annular two-phase flow regime plays a crucial role for estimating the dryout type critical heat flux to identify the optimized flow characteristics in the thermal systems across different industries. Existing studies have provided different correlations based on the limited experimental data. However, these correlations are applicable to certain operating conditions. Therefore, the present study aims at applying a deep learning method, specifically an artificial neural network (ANN), to enhance the prediction of the entrained liquid droplet fraction. Experimental data from various works on annular flow, covering a wide spectrum of pressure and flow conditions, are utilized for training the ANN model. Eight input variables, viz, superficial gas velocity (JSG), superficial liquid velocity (JSL), gas viscosity (μG), liquid viscosity (μL), gas density (ρG), liquid density (ρL), pipe diameter (d) and liquid surface tension (σLV) are considered as input features. The entrained liquid droplet fraction is the single output feature. The present model employs the Bayesian regularization backpropagation algorithm for training. The present ANN model is compared against the performance of linear regression, decision tree and support vector machine algorithms, and found that the performance of the present Bayesian regularization neural network (BRNN) model is superior within ∼7.5% deviation. Further, the BRNN model is coupled with the film mass flow rate model to obtain the axial variation of the liquid film mass flow rate and good agreement is noticed when compared against the experimental data.

Research on void drift between rod bundle subchannels

Void drift between subchannels in a rod bundle is a crucial phenomenon affecting the calculation accuracy of thermal-hydraulic parameters in SMRs. It holds significant importance in enhancing the precision of safety analysis for SMRs. Existing research on experiment and model of void drift between rod bundle subchannels is relatively rare, and the accuracy of model calculations requires improvement. In this study, experiments on gas-liquid two-phase non-equilibrium flow were conducted to measure the redistribution of two-phase flow induced by void drift in a 1 × 2 rod bundle. The experiment results indicated that in bubby flow regime with void fraction less than 0.3, the void diffusion coefficient showed little variation with changes in void fraction. However, in slug flow and annular flow regimes with void fraction exceeding 0.3, the void diffusion coefficient significantly increased with an increase in void fraction. Furthermore, a new void drift model was developed and validated based on a subchannel code. The overall predicted uncertainty for the outlet void fraction in the rod bundle benchmark was less than 13%.

Experimental study of the wave characteristics in annular gas-liquid flow along the wide side of a rectangular microchannel

The characteristics of gas-liquid annular flow in a microchannel with a rectangular cross-section were experimentally studied for the flow of a mixture of viscous silicone oil PMS 200 - nitrogen. To form the required annular flow regime, a side T-mixer was used at the microchannel inlet. High-speed visualization allowed to register flow areas of menisci and waves at the gas-liquid interface, located on short and wide microchannel sides for the given range of superficial gas and liquid velocities Jgas=24.3-93.8 m/s, Jliq=0.01 m/s. The processing of the flow images was performed using the Python program and areas, which are covered by various size waves on the microchannel wide side between the menisci, were identified and summarized. The dependence of the dimensionless wave area on Regas was obtained and compared with the dynamics of liquid layer thickness for the flow of the PMS200-nitrogen mixture, which was obtained previously.

Two-phase flow condensation heat transfer characteristics of R-134a inside three-dimensional cylindrical micropillar enhanced tube

This paper presents an experimental investigation for evaluation of heat transfer coefficient, frictional pressure drops and flow regimes with their transitions during the condensation of R-134a inside a smooth, two microfins (MF1 and MF2) and a newly developed micropillar (MP) tubes. The experiments are carried out for a range of mass flux at an average saturation temperature of 35°C. Micropillar tube has produced the maximum heat transfer coefficient, while the microfin (MF1) tube resulted the highest frictional pressure gradient compared to other tubes. The experimental observations of microfin and micropillar tubes are plotted on the mass flux versus vapor quality flow map and Taitel and Duckler flow map to discuss the transitions within different flow patterns achieved. Visual inspection of the flow regimes with varying mass flux and vapor quality showed stratified-wavy, intermittent, and annular flow regimes. The occurrence of annular flow is found to be more in MF1 and MP tube. A performance evaluation factor (PEF) is defined to evaluate the thermal-hydraulic performance of the tubes. Using the same, it has been observed that the newly developed MP tube has a PEF value greater than unity which suggest augmentation of heat transfer coefficient at the expense of lesser pressure drop penalty.

A Comparative Analysis of Conductance Probes and High-Speed Camera Measurements for Interfacial Behavior in Annular Air-Water Flow.

Different techniques are used to analyze annular flow, but the more interesting ones are those techniques that do not perturb the flow and provide enough resolution to clearly distinguish the interfacial phenomena that take place at the interface, especially the disturbance waves (DW) and the ripple waves (DW). The understanding of these events is important because it influences the heat and mass transfer taking place through the thin film formed near the walls in this flow regime. The laser-induced fluorescence (LIF) and the three-electrode conductance probe are two commonly used techniques to study experimentally annular flow phenomena. In this paper, a set of experiments at different temperatures of 20 °C, 30 °C and 40 °C and different liquid Reynolds numbers have been performed in the annular flow regime, the characteristic of the DW and RW as average height and frequency of these waves has been measured by both techniques LIF and conductance probes. In addition, we also measured the mean film thickness. It was found that the mean film thickness and the DW height are practically the same when measured by both techniques; however, the height of the RW is smaller when measured by the conductance probe and this difference diminishes when the temperature increases.

A new paradigm for the role of disturbance waves on film dryout and wall heat transfer in annular two-phase flow

The entire liquid-film dryout process in vertical annular two-phase boiling flow is characterized experimentally, from inception to completion, in a newly defined flow regime between annular two-phase flow and mist flow, called ‘intermittent dryout’. Experiments are conducted using saturated R245fa flowing vertically through a heated transparent channel. Liquid-film thickness measurements are taken during dryout events. The state (wet or dry) of the heated surface is determined using a laser reflectance measurement, and the signal is used to calculate the time-averaged dry fraction, fdry. The local heat transfer coefficient (HTC) is characterized as a function of fdry. For all the investigated mass fluxes, optimum boiling conditions (OBC), corresponding to the heat flux that results in maximum HTC, consistently occur when the time-averaged dry fraction is fdry≈0.05. Subsequently, the critical boiling transition (CBT), corresponding to the heat flux that results in a significantly lower HTC, can occur for dry fractions of fdry>0.1. Further insight into the liquid film behavior within the intermittent dryout regime is obtained by combining analyses of high-speed videos, time-resolved liquid-film thickness signals, and statistics about the duration of and time between dryout events. In general, the dryout mechanism is dominated by disturbance waves.Mechanistic model frameworks are presented for the prediction of two-phase HTC and heat flux associated with dryout. The model inputs are local flow quantities (pressure, mass flux, vapor quality, and liquid-film thickness) and output a prediction for local heat transfer coefficient. The model framework accurately predicts the local two-phase HTC for a range of mass flux and vapor quality in the annular flow regime. The model framework captures the observed initial increase in the local HTC and subsequent decrease that is associated with liquid-film dryout. The mechanistic approach provides predictions for time-averaged HTC and OBC heat flux that both agree well with experimentally measured values.

Prediction of fluids volume fraction and barium sulfate scale in a multiphase system using gamma radiation and deep neural network

In the oil industry, during the production of oil and gas, barium sulfate (BaSO4) scale may occur on the inner walls of the pipelines leading to the reduction of the internal diameter, making the fluids' passage difficult and complicating the calculation of the fluids volume fraction. This paper presents a methodology to predict volume fraction of fluids and BaSO4 scale thickness from obtaining spectra of two NaI(Tl) detectors that record the transmitted and scattered beams of gamma-rays. Theoretical models for a multiphase annular flow regime (gas-saltwater-oil-scale) were developed using MCNP6 code, which is a mathematical code based on the Monte Carlo method. The simulated data was used to train a deep neural network (DNN) to predict the volume fraction of gas, saltwater and oil, and the concentric scale thickness. A Python optimization library called Optuna was used for the hyperparameters search to design the DNN architecture. The methodology presented great results, especially for scale thickness prediction. Although the results for saltwater did not reach the same level, it was still possible to predict approximately 70% of the patterns up to 10% relative error. This achievement indicates the possibility to calculate the volume fraction of fluids and the concentric scale thickness in the offshore oil industry using gamma densitometry and deep learning models.

Void fraction prediction using prompt gamma neutron activation analysis and artificial intelligence

This paper presents the simulation of a water-gas biphasic system in annular and stratified flow regimes using a neutron beam from a241Am–Be radiation source. The objective is to evaluate the potential of prompt gamma neutron activation analysis (PGNAA) and artificial intelligence-based algorithms in flow measurements. A measurement geometry consisted of a neutron flat source with parallel emission and a spherical detector was developed using the MCNP6 code. Prompt gamma rays spectra were used as input data of a feed-forward multilayer perceptron (MLP) artificial neural network aiming to predict void fraction and to identify the investigated flow regimes. The results showed that the MLP was able to predict void fraction, for both flow regimes, with mean absolute percentage error within 1.58%. In addition, the same MLP achieved 100% of success in flow regime identification. Therefore, it is possible to combine PGNAA with MLP in order to investigate flow characteristics.

Effects of liquid viscosity on interfacial and wall friction factors of swirling annular flows in a vertical pipe

Experiments of gas–liquid swirling annular flows in a vertical pipe of diameter D = 30 mm were carried out to obtain interfacial and wall friction factors, fi and fw, based on a three-fluid model. The liquid phase was two kinds of glycerol-water solutions with the kinematic viscosity νL of two or four times larger than that of water. Experimental data were obtained in two axial sections with short and long axial distances from the swirler, which corresponds to swirling and non-swirling annular flow regimes, respectively. Both the azimuthal and axial components of interfacial velocity decayed with increasing νL. The interfacial swirl number si, which is the ratio of the azimuthal component to the axial component of interfacial velocity, was not affected by νL for z*(=z/D) ≤ 3.8, where z is the axial coordinate in the test section. The mean interfacial swirl number si∗, which is the arithmetic mean of si in z* ≤ 3.8, was expressed in terms of dimensionless numbers based on the gas and liquid volumetric fluxes without νL. The interfacial friction factor decreased with increasing νL in swirling flows. The functional form of the Colebrook equation had a prospect of correlating fi in both swirling and non-swirling annular flows. The wall friction factor ratio of fw in the swirling annular flow regime to that in the non-swirling annular flow regime decreased as νL increased for si∗ > 0.33. The wall friction factor ratio was well correlated in terms of si∗ and νL.

Effect of flow direction on evaporation flow regime of R32/R1234ze(E) mixture inside multiple rectangular minichannels

This study visualized the evaporation flow regime of a non-azeotropic mixture R32/R1234ze(E) inside multiple vertical-upward and horizontal rectangular minichannels with a hydraulic diameter of 2 mm. The effects of the mass flux, vapor quality, heat flux, flow direction, and test refrigerant on the flow regime characteristics were explored. The flow regimes of the mixture were compared to those of R1234ze(E). The effect of flow direction on the flow regime was examined under low mass flux conditions. In the vertical-upward flow, three flow regimes: slug, slug-annular, and annular flow regimes were observed. The flow regime transition boundary of R1234ze(E) shifted to lower mass flux and vapor quality conditions compared to the mixture because of the higher vapor velocity of R1234ze(E). Boiling bubbles were generated in the liquid slug and channel corners around the vapor plug, and the number of boiling bubbles increased with an increasing heat flux. The size of the boiling bubble of the mixture was smaller than that of R1234ze(E), which is attributed to the mass transfer resistance of the non-azeotropic mixture. Only in the vertical-upward flow, the reverse flow was observed in some channels, where the slug flow was observed, because of the effect of gravity. The number of channels with reverse flow decreased with increasing vapor velocity. The annular flow did not appear in the horizontal flow, and the flow regime was observed only in two regimes: slug and slug-annular flow regimes. No difference in flow regime transition boundary between R1234ze(E) and R32/R1234ze(E) was observed.

Experimental investigation on the condensation of R32 flowing inside alternating cross-section flattened tubes with different aspect ratios

In this experiment, we investigated the condensation of R32 flowing inside alternating cross-section flattened (ACF) tubes with various aspect ratios. ACF tubes were made from a copper circular tube with a 4.31-mm inner diameter. Three ACF tubes with aspect ratios of 5.82 (ACF1), 4.11 (ACF2), and 3.23 (ACF3) were used as test tubes. The experimental conditions covered a mass flux of 280–580 kg/m2s, a heat flux of 10–20 kW/m2, a saturation temperature of 40 °C, and a vapor quality of 0.1–0.8. The results revealed that most experimental data fell into the annular flow regime when using existing flow patterns. In comparison, the ACF1 tube obtained the highest heat transfer coefficient (HTC) and pressure drop (ΔP). The HTC of ACF tubes outperformed those of the circular tube by about 1.7–6.9 (for ACF1), 1.1–2.8 (for ACF2), and 1.2–2.1 (for ACF3) times. In addition, ACF tubes provided a greater frictional pressure drop than circular tubes, around 1.0–2.3 (for ACF1), 1.2–1.9 (for ACF2), and 1.0–1.4 (for ACF3) times. However, the ACF1 achieved the best overall performance by approximately 2.1 times, superior to the circular tube. Since the existing correlations failed to predict the Nusselt number and two-phase friction factor, we propose new correlations for the ACF tube with mean deviations of 14.9% and 7.3%, respectively.

A Machine Learning Approach for Gas Kick Identification

Summary Warning signs of a possible kick during drilling operations can either be primary (flow rate increase and pit gain) or secondary (drilling break and pump pressure decrease). Drillers rely on pressure data at the surface to determine in-situ downhole conditions while drilling. The surface pressure reading is always available and accessible. However, understanding or interpretation of this data is often ambiguous. This study analyzes significant kick symptoms in the wellbore annulus both under static (shut in) and dynamic (drilling/circulating) conditions. We used both supervised and unsupervised learning techniques for flow regime identification and kick prognosis. These include an artificial neural network (ANN), support vector machine (SVM), K-nearest neighbor (KNN), decision trees, K-means clustering, and agglomerative clustering. We trained these machine learning models to detect kick symptoms from the gas evolution data collected between the point of kick initiation and the wellhead. All the machine learning techniques used in this work made excellent predictions with accuracy greater than or equal to 90%. For the supervised learning, the decision tree gave the overall best results, with an accuracy of 96% for air influx cases and 98% for carbon dioxide influx cases in both static and dynamic scenarios. For unsupervised learning, K-means clustering was the best, with Silhouette scores ranging from about 0.4 to 0.8. The mass rate per hydraulic diameter and the mixture viscosity yielded the best types of clusters. This is because they account for the fluid properties, flow rate, and flow geometry. Although computationally demanding, the machine learning models can use the surface/downhole pressure data to relay annular flow patterns while drilling. There have been several recent advances in drilling automation. However, this is still limited to gas kick identification and handling. This work provides an alternative and easily accessible primary kick detection tool for drillers based on data at the surface. It also relates this surface data to certain annular flow regime patterns to better tell the downhole story while drilling.

Void Fraction of Air-Water Two-Phase Flows in a Vertically Placed Horizontal U-Bend

Experiments on air-water two-phase flows in a horizontal U-bend placed in the vertical plane were carried out to investigate effects of pipe bending on the void fraction. The inner diameter and the bend radius of curvature were 8 and 24.3 mm, respectively, so that the dimensionless bend diameter was 6. The flow patterns dealt with were plug, slug and annular flows. The void fractions in the bend and the straight section were measured by using quick-closing valves and the void fraction distribution was evaluated with an image processing method. The applicability of available correlations for the void fraction in the straight pipe was also discussed. For the straight pipe, the void fractions of the plug and slug flows can be accurately evaluated using the Smith correlation, whereas in the annular flow regime the Cioncolini and Thome correlation and the Mauro et al. liquid film thickness model give better agreement with the data. The bend void fractions in the downward and upward flows agree well with those for the pipe diameter of 26 mm given in literature, implying that the diameter effect on the void fraction is weak. The Usui et al. void fraction correlation gives reasonable evaluation for downward flows.

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.

Condensation heat transfer of binary and ternary mixtures inside multiport tubes

This study investigated the condensation heat transfer of pure refrigerants and binary and ternary mixtures inside horizontal multiport tubes with multiple square minichannels. The effects of mass flux, vapor quality, heat flux, channel size, and channel shape were evaluated using pure refrigerants R32 and R1234yf; binary mixtures R32/R1234yf, R32/R1123, R32/R13I1, and R410A; and ternary mixtures R1123/R32/R1234yf and CO2/R32/R1234yf. For pure refrigerants, the heat transfer coefficient in the annular and churn flow regimes decreased monotonically with decreasing vapor quality. By contrast, the heat transfer coefficient in the plug flow regime was higher over a wide vapor quality range because of the thin condensate film formed by surface tension. Smaller channels exhibited higher heat transfer coefficients. The square minichannels exhibited higher heat transfer coefficients than the circular minichannels, except in the region with high mass flux and high vapor quality. For binary and ternary mixtures, the heat transfer coefficients significantly decreased with decreasing mass flux and vapor quality and were strongly deteriorated at lower mass fluxes. Compared with the annular and churn flow regimes, in the plug and slug-annular flow regimes, where heat transfer through the thin liquid film was dominant, the effect of mass diffusion resistance in deteriorating heat transfer was the most pronounced. A new heat transfer model for pure refrigerants inside multiport tubes with multiple rectangular minichannels was developed considering the contributions of vapor shear stress and surface tension. The proposed model was verified using a dataset of pure refrigerants, and a mean absolute percentage error (MAPE) of 11.5% was achieved. Furthermore, a new heat transfer model that considers the heat transfer deterioration of non-azeotropic refrigerant mixtures was proposed, which could predict 508 datapoints with an MAPE of 13.3%.