Annular Regime Research Articles

Development of the signal‐to‐noise paradox in subseasonal forecasting models: When? Where? Why?

AbstractSubseasonal forecast models are shown to suffer from the same inconsistency in the signal‐to‐noise ratio evident in climate models. Namely, predictable signals in these models are too weak, yet there is a relatively high level of agreement with observed variability of the atmospheric circulation. The net effect is subseasonal forecast models show higher correlation with observed variability than with their own simulations; that is, the signal‐to‐noise paradox. Also, similar to climate models, this paradox is particularly evident in the North Atlantic sector. The paradox is not evident in week 1 or week 2 forecasts, and hence is limited to subseasonal time‐scales. The paradox appears to be related to an overly fast decay of northern annular mode regimes. Three possible causes of this overly fast decay and for the paradox in the Northern Hemisphere are identified: a too‐fast decay of polar stratospheric signals, overly weak downward coupling from the stratosphere to the surface in some models, and overly weak transient synoptic eddy feedbacks. Though the paradox is clearly evident in the North Atlantic, it is relatively muted in the Southern Hemisphere: southern annular mode regimes persist realistically, the stratospheric signal is well maintained, and eddy feedback is, if anything, too strong and zonal.

Euler–Euler modeling of turbulent annular regime

Euler–Euler methods are one of the many approaches available in literature for modeling droplet-laden flows. These models consider both the gas and droplet phases as inter-penetrating continua, thereby separate sets of transport equations are solved for these phases. The current work focuses on modeling of thin liquid films on solid surfaces in the context of the two-fluid approach to multiphase simulation. In majority of the literature, liquid films are modeled using a 2D Laminar approximation with a parabolic profile for the velocity across the film. However, a number of industrial applications demonstrate a turbulent behavior of the film which would affect the effective viscosity and thereby the thickness of the film. In the current work, a turbulent model for liquid films is proposed based on the Universal Velocity Profile. Additional models are employed to capture the deposition of droplets onto solid surface and stripping off of droplets from the film due to surface waves. The liquid film model is validated on experimental data from available literature. Finally, the newly implemented turbulent film model is applied to study the flow over a fuel rod assembly in a Boiling Water Reactor.

A correlation for U-value for laminar and turbulent flows in concentric tube heat exchangers

This paper introduces a novel empirical correlation designed to predict the overall heat transfer coefficient (U) in concentric tube heat exchangers. The study utilizes a comprehensive dataset of 2700 data points from CFD simulations, examining the impact of critical variables including hot and cold fluid Reynolds numbers (Reh, Rec), Prandtl numbers (Prh, Prc), and heat exchanger dimensions (tube hydraulic diameter Dh, annular space hydraulic diameter Dc, and overall length L). The analysis incorporates a range of fluid combinations in the tube and annular sections – air–air, air–water, water-air, and water–water – across Reynolds number ranges from 1000 to 20000, covering both laminar and turbulent flow regimes. The correlation was developed using the Adam optimizer to minimize the Weighted Mean Squared Error (WMSE), achieving an impressive convergence to an average prediction error of 6.7% compared to actual U values. The model categorizes flow into four distinct categories, each corresponding to a combination of flow patterns in the tube and annular spaces regimes – Laminar–Laminar, Laminar–Turbulent, Turbulent–Laminar, and Turbulent–Turbulent – integrating both hydrodynamic and thermal entry effects to enhance prediction accuracy. Rigorous error analysis revealed a median error of 5.18%, substantially outperforming existing models. Notably, 10% of the predictions deviate by less than 1%, with 90th percentile errors staying below 15%. Parameters were finely tuned through extensive numerical simulations, ensuring accurate application of the correlation across diverse operational conditions. The study concludes by highlighting the model’s potential to significantly enhance the design and efficiency of heat exchangers and proposes future enhancements to further improve its predictive accuracy.

Experimental Study of Void Fraction Measurement Using a Capacitance-Based Sensor and ANN in Two-Phase Annular Regimes for Different Fluids

One of the most severe problems in power plants, petroleum and petrochemical industries is the accurate determination of phase fractions in two-phase flows. In this paper, we carried out experimental investigations to validate the simulations for water–air, two-phase flow in an annular pattern. To this end, we performed finite element simulations with COMSOL Multiphysics, conducted experimental investigations in concave electrode shape and, finally, compared both results. Our experimental set-up was constructed for water–air, two-phase flow in a vertical tube. Afterwards, the simulated models in the water–air condition were validated against the measurements. Our results show a relatively low relative error between the simulation and experiment indicating the validation of our simulations. Finally, we designed an Artificial Neural Network (ANN) model in order to predict the void fractions in any two-phase flow consisting of petroleum products as the liquid phase in pipelines. In this regard, we simulated a range of various liquid–gas, two-phase flows including crude oil, oil, diesel fuel, gasoline and water using the validated simulation. We developed our ANN model by a multi-layer perceptron (MLP) neural network in MATLAB 9.12.0.188 software. The input parameters of the MLP model were set to the capacitance of the sensor and the liquid phase material, whereas the output parameter was set to the void fraction. The void fraction was predicted with an error of less than 2% for different liquids via our proposed methodology. Using the presented novel metering system, the void fraction of any annular two-phase flow with different liquids can be precisely measured.

Fast magnetic reconnection and driven plasma rotation in reversed central magnetic shear configuration

Numerical calculations have been carried out to study the nonlinear growth of the double tearing mode (DTM) in the reversed central magnetic shear configuration for medium-size tokamak plasma parameters, based on two-fluid equations and large aspect ratio approximation. Three different regimes of the DTM growth are found. (a) annular crash regime, existing for a small distance but a not too large plasma rotation frequency difference between two resonant surfaces. In this regime the plasma pressure between two resonant surfaces is flattened due to the fast magnetic reconnection in tens of microseconds, in agreement with experimental observations. In addition, a large plasma rotation shear is generated around the edge of the pressure flattening region right after the fast magnetic reconnection. (b) Core crash regime, existing for a medium distance and a relatively low rotation frequency difference between two resonant surfaces, in which the plasma pressure is flattened over a large region up to the magnetic axis during the fast magnetic reconnection in tens of microseconds, in agreement with experimental observation too. (c) No crash regime, existing for a sufficiently large distance and/or frequency difference between two resonant surfaces. In this regime the mode grows slowly in the nonlinear phase and saturates at a finite amplitude, causing a local flattening of the plasma pressure at the resonant surface but without fast crashes.

Optimal experiment design for element specific atom counting using multiple annular dark field scanning transmission electron microscopy detectors

This paper investigates the possible benefits for counting atoms of different chemical nature when analysing multiple 2D scanning transmission electron microscopy (STEM) images resulting from independent annular dark field (ADF) detector regimes. To reach this goal, the principles of statistical detection theory are used to quantify the probability of error when determining the number of atoms in atomic columns consisting of multiple types of elements. In order to apply this theory, atom-counting is formulated as a statistical hypothesis test, where each hypothesis corresponds to a specific number of atoms of each atom type in an atomic column. The probability of error, which is limited by the unavoidable presence of electron counting noise, can then be computed from scattering-cross sections extracted from multiple ADF STEM images. Minimisation of the probability of error as a function of the inner and outer angles of a specified number of independent ADF collection regimes results in optimal experimental designs. Based on simulations of spherical Au@Ag and Au@Pt core–shell nanoparticles, we investigate how the combination of two non-overlapping detector regimes helps to improve the probability of error when unscrambling two types of atoms. In particular, the combination of a narrow low angle ADF detector with a detector formed by the remaining annular collection regime is found to be optimal. The benefit is more significant if the atomic number Z difference becomes larger. In addition, we show the benefit of subdividing the detector regime into three collection areas for heterogeneous nanostructures based on a structure consisting of three types of elements, e.g., a mixture of Au, Ag and Al atoms. Finally, these results are compared with the probability of error resulting when one would ultimately use a pixelated 4D STEM detector and how this could help to further reduce the incident electron dose.

Accurate Flow Regime Classification and Void Fraction Measurement in Two-Phase Flowmeters Using Frequency-Domain Feature Extraction and Neural Networks

Two-phase flow is very important in many areas of science, engineering, and industry. Two-phase flow comprising gas and liquid phases is a common occurrence in oil and gas related industries. This study considers three flow regimes, including homogeneous, annular, and stratified regimes ranging from 5–90% of void fractions simulated via the Mont Carlo N-Particle (MCNP) Code. In the proposed model, two NaI detectors were used for recording the emitted photons of a cesium 137 source that pass through the pipe. Following that, fast Fourier transform (FFT), which aims to transfer recorded signals to frequency domain, was adopted. By analyzing signals in the frequency domain, it is possible to extract some hidden features that are not visible in the time domain analysis. Four distinctive features of registered signals, including average value, the amplitude of dominant frequency, standard deviation (STD), and skewness were extracted. These features were compared to each other to determine the best feature that can offer the best separation. Furthermore, artificial neural network (ANN) was utilized to increase the efficiency of two-phase flowmeters. Additionally, two multi-layer perceptron (MLP) neural networks were adopted for classifying the considered regimes and estimating the volumetric percentages. Applying the proposed model, the outlined flow regimes were accurately classified, resulting in volumetric percentages with a low root mean square error (RMSE) of 1.1%.

Flow regime identification and gas volume fraction prediction in two-phase flows using a simple gamma-ray gauge combined with parallel artificial neural networks

Volume fraction and regime identification are considered as two main goals in the multiphase flow measurement. By developing the applications of artificial neural networks (ANN), new approaches have been introduced in the multiphase measurements. In the present work, prediction of gas volume fraction and identification of five different flow regimes including Bubble, Dispersed, Pluged, Annular, and Slug regimes were carried out using the simplest form of a radiation measurement system and proposing a simple structure of ANN including two independent MLP networks which are working in parallel. All data used for training and testing the networks were recorded using a simple radiation-based setup involving a137Cs gamma-ray source and one NaI(Tl) detector in the experimental setup with real dynamic conditions of fluids in a test loop. Extracted features from the recorded spectrum by the detector include full-energy peak and total counts. Overall results showed that all introduced flow regimes have been successfully identified by one of the proposed ANNs while GVFs have been predicted precisely using another one with a mean relative error of less than 3%.

Experimental analysis of pressure drops and heat transfer coefficient during upward two-phase flow of R-134a/R-245fa mixture in an offset-strip finned passage

The current study focuses on an experimental investigation of upward vertical two-phase flow of binary mixtures in complex geometries composed of offset-strip fins. The flow boiling of a mixture containing 52% mol/mol of R-134a and 48% mol/mol of R-245fa was visualized. The ranges of mass flux and heat flux investigated were 11 – 25 kg.m−2.s−1 and 9 – 23 kW.m−2 respectively. The test section is an offset-strip finned passage whose hydraulic diameter is 1.75 mm. The operating pressure ranges from 2 to 5 bar absolute. The temperature glide of the used binary mixture ranges from 14 to 16 °C. Different flow regimes were identified along the test section such as bubbly, slug and annular regimes. The impact of strip-fins and mass flux on flow maps was analysed in this study. The pressure drop was analyzed and the obtained data were gathered to propose a new correlation for R-134a/R-245fa mixture in offset-strip fins. The local heat transfer coefficient was calculated and it has been found that nucleate boiling was the predominant mode of heat transfer.

Effects of a flow obstacle on liquid film thickness in relation with CHF enhancement

Abstract There is evidence in literature that the insertion of flow obstacles in the coolant flow of a boiler flowing in an annular regime postpones the film dryout emergence on the heated surfaces. Specifically, the obstacle is believed to force the deposition of droplets to the wall, hence to increase the film flow rate, and therefore to delay dryout. The two objectives of the present research are (i) to confirm the increase in the film flow rate due to the obstacle, by (ii) building an appropriate flow-loop equipped with suited techniques for film thickness and film velocity measurements. A solid cylinder is inserted in the center of a vertical water-air annular tube flow. Variations in liquid film characteristics, e. g. film thickness and interfacial parameters (disturbance waves velocity and frequency), are measured through a local conductance probes technique. This work shows that the presence of an obstacle in an annular flow leads to an increase in the film thickness, specifically the substrate thickness, and does not affect the interfacial behavior of the film. An increase in the droplet deposition constant is observed and agrees reasonably well with predictions from the Windecker et al. (1999) correlation. The flow-loop and the measurements techniques are suited for extensive droplet deposition measurements.

Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas-Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries.

Measuring fluid characteristics is of high importance in various industries such as the polymer, petroleum, and petrochemical industries, etc. Flow regime classification and void fraction measurement are essential for predicting the performance of many systems. The efficiency of multiphase flow meters strongly depends on the flow parameters. In this study, MCNP (Monte Carlo N-Particle) code was employed to simulate annular, stratified, and homogeneous regimes. In this approach, two detectors (NaI) were utilized to detect the emitted photons from a cesium-137 source. The registered signals of both detectors were decomposed using a discrete wavelet transform (DWT). Following this, the low-frequency (approximation) and high-frequency (detail) components of the signals were calculated. Finally, various features of the approximation signals were extracted, using the average value, kurtosis, standard deviation (STD), and root mean square (RMS). The extracted features were thoroughly analyzed to find those features which could classify the flow regimes and be utilized as the inputs to a network for improving the efficiency of flow meters. Two different networks were implemented for flow regime classification and void fraction prediction. In the current study, using the wavelet transform and feature extraction approach, the considered flow regimes were classified correctly, and the void fraction percentages were calculated with a mean relative error (MRE) of 0.4%. Although the system presented in this study is proposed for measuring the characteristics of petroleum fluids, it can be easily used for other types of fluids such as polymeric fluids.

Proposing an Intelligent Dual-Energy Radiation-Based System for Metering Scale Layer Thickness in Oil Pipelines Containing an Annular Regime of Three-Phase Flow

Deposition of scale layers inside pipelines leads to many problems, e.g., reducing the internal diameter of pipelines, damage to drilling equipment because of corrosion, increasing energy consumption because of decreased efficiency of equipment, and shortened life, etc., in the petroleum industry. Gamma attenuation could be implemented as a non-invasive approach suitable for determining the mineral scale layer. In this paper, an intelligent system for metering the scale layer thickness independently of each phase’s volume fraction in an annular three-phase flow is presented. The approach is based on the use of a combination of an RBF neural network and a dual-energy radiation detection system. Photo peaks of 241Am and 133Ba registered in the two transmitted detectors, and scale-layer thickness of the pipe were considered as the network’s input and output, respectively. The architecture of the presented network was optimized using a trial-and-error method. The regression diagrams for the testing set were plotted, which demonstrate the precision of the system as well as correction. The MAE and RMSE of the presented system were 0.07 and 0.09, respectively. This novel metering system in three-phase flows could be a promising and practical tool in the oil, chemical, and petrochemical industries.

Flow-rate prediction independent of the regime in a dynamic two-phase flow system using a simple pulse height spectrum of a detector and Artificial Neural Networks

In this work, the air and water flow-rates were accurately predicted within a two-phase flow loop using features extracted from a simple detector spectrum, independently of the changes in the flow regime. In this regard, a new method based on a single beam-single detector using single-energy of gamma-rays was proposed. The gamma-ray attenuation setup combined with Artificial Neural Network (ANN) was used to predict the flow-rates in various regime of gas–liquid two-phase flows such as bubble, plug, slug, annular and dispersed regimes. Moreover, the ANN was developed based on four features extracted from the recorded pulse height spectrum in the dynamic condition of the fluids. The results showed that the air and water flow-rates can be measured with an average of Mean Relative Error (MRE) less than 4.5%. Overall results revealed that using the proposed method, prediction of the flow-rates can be successfully carried out independent of their regimes.

Application of Artificial Intelligence and Gamma Attenuation Techniques for Predicting Gas–Oil–Water Volume Fraction in Annular Regime of Three-Phase Flow Independent of Oil Pipeline’s Scale Layer

To the best knowledge of the authors, in former studies in the field of measuring volume fraction of gas, oil, and water components in a three-phase flow using gamma radiation technique, the existence of a scale layer has not been considered. The formed scale layer usually has a higher density in comparison to the fluid flow inside the oil pipeline, which can lead to high photon attenuation and, consequently, reduce the measuring precision of three-phase flow meter. The purpose of this study is to present an intelligent gamma radiation-based, nondestructive technique with the ability to measure volume fraction of gas, oil, and water components in the annular regime of a three-phase flow independent of the scale layer. Since, in this problem, there are several unknown parameters, such as gas, oil, and water components with different amounts and densities and scale layers with different thicknesses, it is not possible to measure the volume fraction using a conventional gamma radiation system. In this study, a system including a 241Am-133Ba dual energy source and two transmission detectors was used. The first detector was located diametrically in front of the source. For the second detector, at first, a sensitivity investigation was conducted in order to find the optimum position. The four extracted signals in both detectors (counts under photo peaks of both detectors) were used as inputs of neural network, and volume fractions of gas and oil components were utilized as the outputs. Using the proposed intelligent technique, volume fraction of each component was predicted independent of the barium sulfate scale layer, with a maximum MAE error of 3.66%.

Feasibility Study of Using X-ray Tube and GMDH for Measuring Volume Fractions of Annular and Stratified Regimes in Three-Phase Flows

In this paper, the feasibility of using an X-ray tube instead of radioisotope sources for measuring volume fractions of gas, oil, and water in two typical flow regimes of three-phase flows, namely, annular and stratified, is evaluated. This study’s proposed detection system is composed of an X-ray tube, a 1 inch × 1 inch NaI detector, and one Pyrex-glass pipe to model different volume fractions for two flow regimes, annular and stratified. Group method of data handling (GMDH), a powerful regression tool, was also implemented to analyze the obtained data. The obtained results in this work indicate that a simple system based on an X-ray tube and just one NaI detector could be a potential alternative to radioisotope-based systems for separate measurements of gas, oil, and water volume fractions in annular and stratified flow regimes of a three-phase flow.

Characterization of R134a two-phase flow regimes in horizontal and vertical smooth tubes with capacitive sensors

This paper presents the identification of slug/stratified-wavy, stratified-wavy and annular regimes for horizontal flow (in the range of mass fluxes 40 – 150 kg m−2 s−1), and slug, churn, and annular flow regimes for vertical upward flow (in the range 65 – 115 kg m−2 s−1) for R134a flow through 7 mm ID tube. Flow regimes are characterized based on time plot of normalized capacitive signals, kernel density estimation (KDE), power spectral density (PSD), and visualization results from a high-speed camera. Sensors with different axial lengths (D, 2D/3, and D/2) are also tested to study the adequacy of shorter sensors for the characterization of flow regimes. Results show that all three sensors have a similar capability of characterizing flow regimes, justifying the use of the shorter sensors in many applications with limited space.

МОДЕЛІ РОЗРАХУНКУ ТЕПЛООБМІНУ ПІД ЧАС ПЛІВКОВОЇ КОНДЕНСАЦІЇ У СЕРЕДИНІ ГОРИЗОНТАЛЬНИХ ТРУБ

The study of heat transfer during condensation in the middle of the pipes is relevant due to the constant need to improve the design of various heat exchangers (condensers of refrigeration and air conditioning systems, reactor safety systems, and power plant heaters). It is important to know the exact values of heat transfer coefficients in the case of condensation when their value is close to the heat transfer from the cooling side. Well-known theoretical, semi-empirical and experimental models and correlations for heat transfer calculation during condensation of working fluids inside smooth horizontal pipes have been described in the article. Selected models are widely used in various scien-tific publications for heat transfer prediction and construction of various heat exchangers with in-tube condensation. All correlations allow determining the average by tube perimeter, but local by the length of the tube, heat transfer coefficients in the case of annular and intermediate regimes of two-phase flow. All used dependences take into account current modes of phase flow. For choosing the best correlations, a comparison between selected models and experimental values of heat transfer coefficients obtained by different authors for condensation of ten working fluids inside smooth hori-zontal tubes with internal diameters more than 3 mm have been performed. Experimental values were selected from well-known works, in which heat transfer was studied during condensation of such fluids as fluorocarbons, immersion refrigerants, hydrocarbons and steam in the case of intermediate and annular phase flows with a detailed description of research conditions (geometrical parameters of investigated tubes and regimes parameters of condensation process, such as heat flux, mass velocity, vapour quality, and condensation temperature). Comparison of various models with experimental data of different authors allowed identifying the models that show the best convergence with experi-ments among all correlations. Recommendations (range of application) for use of models in engineer-ing practice are given.

Evaluation of flow pattern recognition and void fraction measurement in two phase flow independent of oil pipeline’s scale layer thickness

The main objective of the present research is to combine the effect of scale thickness on the flow pattern and characteristics of two-phase flow that is used in oil industry. In this regard, an intelligent nondestructive technique based on combination of gamma radiation attenuation and artificial intelligence is proposed to determine the type of flow pattern and gas volume percentage in two phase flow independent of petroleum pipeline’s scale layer thickness. The proposed system includes a dual energy gamma source, composed of Barium-133 and Cesium-137 radioisotopes, and two sodium iodide detectors for recording the transmitted and scattered photons. Support Vector Machine was implemented for regime identification and Multi-Layer Perceptron with Levenberg Marquardt algorithm was utilized for void fraction prediction. Total count in the scattering detector and counts under photo peaks of Barium-133 and Cesium-137 were assigned as the inputs of networks. The results show the ability of presented system to identify the annular regime and measure the void fraction independent of petroleum pipeline’s scale layer thickness.

Multibody Simulation for Gravity-Driven Falling Film Using Computational Fluid Dynamics

The annular flow two-phase regime appears in nuclear power plants cooling by water. On accidents with lost of cooling this regime determine the heat extraction conditions from the core. The experimental facility Gerador de Película Ondulatoria (GEPELON) was constructed at Universitat Politècnica de València to study this phenomenon. In this work, we use computational fluid dynamics techniques to simulate an air-water regime, specifically a gravity-driven falling liquid film in the GEPELON experimental facility. We consider a multibody geometry where the porous sintered of the input device is simulated in a realistic way. The Volume of Fluid (VOF) model was used in the resolution of the constitutive equations. We propose three geometric approximations to estimate film thickness, and simulation results were compared with experimental values. The simulation results show a good agreement with experimental measurements. Besides, no significant differences were observed between geometrical approximations for film thickness calculations.

Entropy–stable discontinuous Galerkin approximation with summation–by–parts property for the incompressible Navier–Stokes/Cahn–Hilliard system

We develop an entropy–stable two–phase incompressible Navier–Stokes/Cahn–Hilliard discontinuous Galerkin (DG) flow solver method. The model poses the Cahn–Hilliard equation as the phase field method, a skew–symmetric form of the momentum equation, and an artificial compressibility method to compute the pressure. We design the model so that it satisfies an entropy law, including free– and no–slip wall boundary conditions with non–zero wall contact angle. We then construct a high–order DG approximation of the model that satisfies the summation–by–parts simultaneous–approximation–term property. With the help of a discrete stability analysis, the scheme has two modes: an entropy–conserving approximation with central advective fluxes and the Bassi–Rebay 1 (BR1) method for diffusion, and an entropy–stable approximation with an exact Riemann solver for advection and interface stabilization added to the BR1 method. The scheme is applicable to, and the stability proofs hold for, three–dimensional unstructured meshes with curvilinear hexahedral elements. We test the convergence of the schemes on a manufactured solution, and their robustness by solving a flow initialized from random numbers. In the latter, we find that a similar scheme that does not satisfy an entropy inequality had 30% probability to fail, while the entropy–stable scheme never does. We also solve the static and rising bubble test problems, and to challenge the solver capabilities we compute a three–dimensional pipe flow in the annular regime.