Flow Regime Identification Research Articles

Time–Frequency Analysis Based Flow Regime Identification Methods for Airlift Reactors

The flow regime transitions in an airlift reactor were investigated based on pressure fluctuation signals. Two time–frequency analysis methods, i.e., Wigner–Ville distribution and wavelet transform, were used to extract flow regime characteristics from pressure signals. The main frequency derived from the smoothed pseudo-Wigner–Ville distribution of the pressure signal was used to quantify flow regime transitions in the reactor. Two flow regime transition points were successfully detected from the evolution of main frequencies of pressure signals. In addition, the local dynamic characteristics of the pressure signal at different frequency bands were analyzed by use of the wavelet transform. A new flow regime identification method based on the wavelet entropy of the pressure signal was proposed. This method was confirmed to be reliable and efficient to detect flow regime transitions in the reactor.

Fast reconstruction of electrical resistance tomography (ERT) images based on the projected CG method

Electrical resistance tomography (ERT) is considered to be one of the most promising process tomography techniques for multi-phase flow measurement due to the advantages such as high speed, low cost and non-intrusive sensing. The image reconstruction for ERT is an inverse problem to find the conductivity distribution of an object by measuring the voltage between sets of electrodes placed around its periphery. The conjugate gradient (CG) method is one of the most popular methods applied for image reconstruction, although its convergence rate is low. In this paper, an advanced version of the CG method, namely the projected CG method, is proposed for image reconstruction of an ERT system. The solution space is projected into the Krylov subspace and the inverse problem is solved by the CG method in a low-dimensional specific subspace. Both simulation and experimental work were carried out for typical gas–water two-phase flow regimes. The flow regimes are identified according to the reconstructed images of the projected CG method and conventional methods. Results obtained indicate that the projected CG method improves the quality of the reconstructed images and dramatically reduces the computational time compared with the traditional sensitivity, Landweber, and CG methods. Therefore, the projected CG method is suitable for identification of online two-phase flow regimes.

Estimation of volume fraction and flow regime identification in inclined pipes based on gamma measurements and multivariate calibration

A combination of gamma measurements and multivariate calibration was applied to estimate multiphase flow mixture density and to identify flow regime. The experiments were conducted using recombined hydrocarbon fluids sampled from an onshore receiving terminal including hydrate thermodynamic inhibitors (monoethylene glycol and methanol (MeOH)). These hydrate inhibitors were added to deionised water at 60% concentration by volume. The experiments were conducted at a temperature of 0 °C and a 75‐bar pressure, comparable with deep water production on the Norwegian continental shelf. Two angles of inclination (1° and 5°) and two water cuts (15% and 85%) were investigated. A single‐energy gamma densitometer was installed on the test facility for measuring the mixture density, whereas the dual‐energy gamma densitometer was traversed linearly from the bottom to the top of the pipe for multivariate calibration and prediction. Seventy partial least square prediction models were calibrated based on single‐phase experimental data. These models were used in estimating the mixture density and identifying the flow regime in all the experiments. The estimated mixture densities were accurate as compared with those from the single‐energy gamma densitometer with the root mean square error of prediction of 13.6 and 9.7 kg/m3 for 1° angle of inclination and 17 and 26.6 kg/m3 for 5° pipe inclination. The models were also able to identify the flow regimes investigated for both 1° and 5° angles of inclination. Copyright © 2012 John Wiley & Sons, Ltd.

Analysis of production data in shale gas reservoirs: Rigorous corrections for fluid and flow properties

Analysis of long-term linear flow periods associated with shale gas production has received much attention in recent literature as a means of obtaining information about stimulation efficiency. However, the most popular methods for analysis (ex. square-root-of-time plot) can lead to incorrect characterization. Nobakht and Clarkson (2011a) demonstrated that the square-root-of-time plot may not be a straight line for constant gas rate production linear flow and the non-linear shape may lead to incorrect flow regime identification. The square-root-of-time plot is however a straight line for constant flowing pressure (Nobakht and Clarkson, 2011b). Ibrahim and Wattenbarger (2005, 2006) and Nobakht and Clarkson (2011b) showed that using the slope of square-root-of-time plot, for constant flowing pressure constraint, leads to an overestimation of fracture half-length. Additional important considerations for shale gas analysis are non-Darcy flow and non-static reservoir properties. Clarkson et al. (2011) demonstrated that ignoring gas-slippage effects, thought to be important in ultra-low permeability reservoirs, can cause errors in reservoir characterization. They incorporated slippage into pseudo-variables for production data analysis, as has been done with non-static permeability (Thompson et al., 2010). Finally, Nobakht et al. (2011) extended the methodology proposed by Nobakht and Clarkson (2011b) to properly analyze linear flow in the presence of slippage and desorption.The purpose of the current work is to evaluate the current methods for analyzing linear flow in shale gas reservoirs, and establish which method is the most accurate for reservoir characterization. First, recent studies addressing linear flow under constant flowing pressure and constant gas rate production are briefly reviewed. Then, a comparison among the above-mentioned methods for calculating fracture half-length or contacted matrix surface area is made. It is shown that Nobakht et al. (2011) method yields the fracture half-lengths that best match the expected values for constant flowing pressure. Finally, we present a method for analyzing linear flow for real production data, where neither flowing pressure nor gas rate is constant. The method is validated using three numerically-simulated cases. It is found that this method works well for the three cases provided.

Electrical impedance-based void fraction measurement and flow regime identification in microchannel flows under adiabatic conditions

Electrical impedance of a two-phase mixture is a function of void fraction and phase distribution. The difference in the specific electrical conductance and permittivity of the two phases is exploited to measure electrical impedance for obtaining void fraction and flow regime characteristics. An electrical impedance meter is constructed for the measurement of void fraction in microchannel two-phase flow. The experiments are conducted in air–water two-phase flow under adiabatic conditions. A transparent acrylic test section of hydraulic diameter 780μm is used in the experimental investigation. The impedance void meter is calibrated against the void fraction calculated using analysis of images obtained with a high-speed camera. Based on these measurements, a methodology utilizing the statistical characteristics of the void fraction signals is employed for identification of microchannel flow regimes. A self-organizing neural network is used for classification of the flow regimes.

A New Analytical Method for Analyzing Linear Flow in Tight/Shale Gas Reservoirs: Constant-Rate Boundary Condition

Summary Hydraulically fractured vertical and horizontal wells completed in shale gas and some tight gas plays are known to exhibit long periods of linear flow. Recently, techniques for analyzing this flow period using (normalized) production data have been put forth, but there are known errors associated with the analysis. In this paper, linear flow from fractured wells completed in tight/shale gas reservoirs subject to a constant-production-rate constraint is studied. We show analytically that the square-root-of-time plot (a plot of rate-normalized pressure vs. square root of time that is commonly used to interpret linear flow) depends on the production rate. We also show that depending on production rate, the square-root-of-time plot may not be a straight line during linear flow; the higher the production rate, the earlier in time the plot deviates from the expected straight line. This deviation creates error in the analysis, especially for flow-regime identification. To address this issue, a new analytical method is developed for analyzing linear-flow data for the constant-gas-rate production constraint. The method is then validated using a number of numerically simulated cases. As expected, on the basis of the analytical derivation, the square-root-of-time plots for these cases depend on gas-production rate and, for some cases, the plot does not appear as a straight line during linear flow. Finally, we found that there is excellent agreement between the fracture half-lengths obtained using this method and the input fracture half-lengths entered in to numerical simulation.

Flow Regime Identification Using Neural Network–Based Electrodynamic Tomography System

Proses tomografi adalah suatu teknik membina imej yang murah, cekap dan sesuai untuk proses di industri yang kini semakin diguna pakai untuk tujuan pemantauan proses dan pengukuran. Mekanisme pengesanan dalam proses tomografi bergantung kepada bahan aliran dalam paip industri sama ada pepejal, gas atau cecair. Dalam kertas kerja ini, proses yang terlibat adalah pengaliran pepejal kering dalam paip mengikut arah graviti dan mekanisme pengesanan yang digunakan ialah penderia elektrodinamik. Pengenalpastian rejim aliran daripada pengukuran penderia adalah dengan menggunakan rangkaian neural yang akan mengenal pasti aliran pepejal sama ada dalam aliran penuh, suku separuh dan tiga suku. Kata kunci: Proses tomografi, rangkaian neural, penderia elektrodinamik, pengenalpastian Process tomography is a low cost, efficient and non-invasive industrial process imaging technique. It is used in many industries for process imaging and measuring. Provided that appropriate sensing mechanism is used, process tomography can be used in processes involving solids, liquids, gases, and any of their mixtures. In this paper, the process to be imaged and measured involves solid particles flow in gravity drop system. Electrical charge tomography or electrodynamic tomography is a tomographic technique using electrodynamic sensors. This paper presents the flow regime identification using neural network. Keywork: Process tomography; neural network; electrodynamic sensor; identification

Application Of Neural Network Technique And Electrodynamic Sensors In The Identification Of Solid Flow Regimes

Imaging of industrial processes have been accomplished with better efficiency and better control since the introduction of process tomography in several industries. This technique enables a deeper look into the internal conditions of a process without invading the process. In tomographic techniques, process information such as the distribution and velocity of the particles conveying at a particular plane can be obtained by placing sensors around the periphery of the plane. This paper is a continuation of a previous paper entitled Flow Regime Identification Using Neural Network–based Electrodynamic Tomography System in Jurnal Teknologi 40(D). This paper presents the results of sensors output in comparison to that of prediction models, concentration profiles and flow regimes identification obtained from the system described in the previous paper. Key words: Electrodynamic tomography, neural network, concentration profile, flow regimes

Flow Regime Identification in Three Multiphase Reactors Based on Kolmogorov Entropies Derived from Gauge Pressure Fluctuations

Flow Regime Identification in Three Multiphase Reactors Based on Kolmogorov Entropies Derived from Gauge Pressure Fluctuations

A New Method for Flow Regime Identification in a Fluidized Bed Based on Gamma-Ray Densitometry and Information Entropy

The accurate identification of the boundaries of the main flow regimes in fluidized beds is very important since the degrees of mixing and mass and heat transfer depend on the prevailing flow regime. Both the minimum fluidization velocity Umf (0.103 m·s−1) and the transition velocity Utrans (0.12 m·s−1) to bubbling fluidization regime have been successfully identified based on a newly developed maximum information entropy (IEmax) algorithm applied to gamma photon time series. The latter are recorded by means of gamma-ray densitometry scans performed in an air–polyethylene fluidized bed (0.438 m in ID) operated at ambient conditions. A comparison with the Kolmogorov entropy (KE) algorithm has found that the new approach yielded more accurate transition velocities. The value of Umf is also validated by means of the profiles of both bed pressure drop and bed height, respectively. It has been found, however, that these parameters are not capable of identifying the second transition velocity Utrans. It is demonstrated that the Umf value identified on the basis of IEmax is theoretically predictable. In the bubbling fluidization regime, a simple correlation between both the KE and IEmax values is developed.

ITERATIVE FILTERING RECONSTRUCTION ALGORITHM FOR GAMMA COMPUTERIZED TOMOGRAPHY: APPLICATION TO TWO-PHASE FLOW VISUALIZATION

Considering the influence of the limited measurement data on the effectiveness of the image reconstruction algorithms in industrial multiphase flow tomography, an iterative filtering reconstruction algorithm is proposed by redefining inverse problem in this article. In order to improve the image quality in iterative reconstruction algorithm, Wiener filter and soft-threshold algorithm are employed as constraints for the reconstructed image. The developed algorithm is applied to gamma-CT based two-phase flow measurements, both simulated and experimental. The results demonstrate that the proposed method cannot only realize the flow regime identification on horizontal pipe, but also improve the iterative reconstruction process, i.e., the reconstructed image quality is enhanced and the iteration number is reduced.

Two-phase flow structure in large diameter pipes

Flow in large pipes is important in a wide variety of applications. In the nuclear industry in particular, understanding of flow in large diameter pipes is essential in predicting the behavior of reactor systems. This is especially true of natural circulation Boiling Water Reactor (BWR) designs, where a large-diameter chimney above the core provides the gravity head to drive circulation of the coolant through the reactor. The behavior of such reactors during transients and during normal operation will be predicted using advanced thermal–hydraulics analysis codes utilizing the two-fluid model. Essential to accurate two-fluid model calculations is reliable and accurate computation of the interfacial transfer terms. These interfacial transfer terms can be expressed as the product of one term describing the potential driving the transfer and a second term describing the available surface area for transfer, or interfacial area concentration. Currently, the interfacial area is predicted using flow regime dependent empirical correlations; however the interfacial area concentration is best computed through the use of the one-dimensional interfacial area transport equation (IATE). To facilitate the development of IATE source and sink term models in large-diameter pipes a fundamental understanding of the structure of the two-phase flow is essential. This understanding is improved through measurement of the local void fraction, interfacial area concentration and gas velocity profiles in pipes with diameters of 0.102m and 0.152m under a wide variety of flow conditions. Additionally, flow regime identification has been performed to evaluate the existing flow regime transition criteria for large pipes. This has provided a more extensive database for the development and evaluation of IATE source and sink models. The data shows the expected trends with some distortion in the transition region between cap-bubbly and churn-turbulent flow. The flow regime map for the 0.102m and 0.152m diameter test sections agree with the existing flow regime transition criteria. It may be necessary to perform further experiments in larger pipes and at higher gas flow rates to expand the range of conditions for which models can be developed and tested.

Classification of two phase flows using linear discriminant analysis and expectation maximization clustering of video footage

Two-phase adiabatic flow of refrigerant R134a was studied and an automated flow regime detection algorithm was developed. For a saturation temperature of 15 °C, 5 s video data was captured for mass fluxes ranging from 200 kg/m 2 s to 500 kg/m 2 s in smooth horizontal tubes. The vapor quality varied between 0 and 1, the I.D. was 8 mm. Slug flow, intermittent flow and annular flow were discerned. A flow regime is assigned to each video stream as a whole, instead of on a frame by frame basis. In this way, each data point contains both high resolution temporal and spatial information. The dimensionality of the data is reduced by a combination of manual parameter selection and linear discriminant analysis. The classification of the reduced data is then done by unsupervised clustering with the expectation maximization algorithm. Based on our visual classification, all of the slug and annular flows are correctly recognized, intermittent flows are identified with 95% accuracy. The method can be used for automatic online flow regime identification.

Hilbert–Huang transform, Hurst and chaotic analysis based flow regime identification methods for an airlift reactor

The flow regimes and their transitions in an internal loop airlift reactor were investigated. The Hilbert–Huang transform (HHT) was applied to analyze the energy–frequency–time distribution of the pressure signal. It was found that the Hilbert spectrum of the pressure signal was closely related to the superficial gas velocity. The stochastic behaviors of intrinsic mode functions (IMFs) extracted from the pressure signal were studied using the Hurst analysis. Two different Hurst exponents were obtained for each pressure signal: one was smaller than 0.5, while the other was larger than 0.5, representing the anti-persistent and persistent hydrodynamic behaviors, respectively. The evolution of the larger Hurst exponent clearly indicated flow regime transitions in the downcomer. The wavelet transform combined with the autocorrelation analysis were applied to extract chaotic components of the pressure signal. Two flow regime transition points were successfully detected from the evolution of chaotic parameters, i.e. the largest Lyapunov exponent, correlation dimension and Kolmogorov entropy.

Estimation of volume fractions and flow regime identification in multiphase flow based on gamma measurements and multivariate calibration

Gamma measurements combined with multivariate calibration were applied to estimate volume fractions and identify flow regimes in multiphase flow. Multiphase flow experiments were carried out with formation water, crude oil and gas from different North Sea gas fields in an industrial scale multiphase flow test facility in Porsgrunn, Norway. The experiments were carried out with a temperature of 80∘C and 100 bar pressure which is comparable to field conditions. Different multiphase flow regimes (stratified-wavy, slug, dispersed and annular) and different volume fractions of oil, water and gas were investigated. A traversable dual energy gamma densitometer instrument consisting of a 30 mCi Ba133 source and a CnZnTd detector with a sampling frequency of 7 Hz was used.111 partial least square prediction models were calibrated based on single-phase experimental data. These models were used to predict all the volume fractions and also to identify the different flow regimes involved. The results from the flow regime identification were promising but the first results for the predictions of volume fractions were not acceptable. Principal component analysis was then applied to the calibration data and some of the calibration and test data in combination. The results from the PCA showed that there were differences between the calibration and test data.An average linear scaling technique was developed to improve the models volume fraction prediction performance. This technique was developed from half of the three-phase data sets and tested on the other half. The root mean square error of prediction (RMSEP) for the test data for gas, oil and water was 37.4%, 39.2% and 6.3% respectively before this technique was applied and 6.5%, 8.9% and 4.4% respectively after this technique was applied. Average linear scaling also improved the flow regime identification plots. Average scaling was then applied to predict the volume fractions and to identify the flow regimes of both the Gas/Oil and Gas/Water two-phase data sets. The RMSEP for gas, oil and water for Gas/Oil test data was 4.8%, 6.0% and 6.8% respectively. In the case of Gas/Water, the RMSEP for gas, oil and water were 6.2%, 9.2% and 5.8% respectively. Likewise their respective flow regimes were also easier to identify after this technique was applied.

Identification of Flow Regimes in a Concurrent Gas Liquid Upflow through Packed Beds

AbstractThe flow regimes bubble flow, pulse flow, and spray flow were identified by visual observation in a packed column. Three gas‐liquid systems (air/water, air/56 % glycerol, and air/monoethanolamine) and four column packings (Raschig rings, Intalox saddles, and two sizes of spheres) were investigated to cover wide ranges of physical properties of gas liquid systems and characteristics of column packings affecting the flow regime transition. Criteria for the flow regime transition were developed in terms of system and operating variables.

Suitability of an MRMCE (multi-resolution minimum cross entropy) algorithm for online monitoring of a two-phase flow

The flow regimes are important characteristics to describe two-phase flows, and measurement of two-phase flow parameters is becoming increasingly important in many industrial processes. Computerized tomography (CT) has been applied to two-phase/multi-phase flow measurement in recent years. Image reconstruction of CT often involves repeatedly solving large-dimensional matrix equations, which are computationally expensive, especially for the case of online flow regime identification. In this paper, minimum cross entropy reconstruction based on multi-resolution processing (MRMCE) is presented for oil–gas two-phase flow regime identification. A regularized MCE solution is obtained using the simultaneous multiplicative algebraic reconstruction technique (SMART) at a coarse resolution level, where important information on the reconstructed image is contained. Then, the solution in the finest resolution is obtained by inverse fast wavelet transformation. Both computer simulation and static/dynamic experiments were carried out for typical flow regimes. Results obtained indicate that the proposed method can dramatically reduce the computational time and improve the quality of the reconstructed image with suitable decomposition levels compared with the single-resolution maximum likelihood expectation maximization (MLEM), alternating minimization (AM), Landweber, iterative least square technique (ILST) and minimum cross entropy (MCE) methods. Therefore, the MRMCE method is suitable for identification of dynamic two-phase flow regimes.

Boolean logic analysis for flow regime recognition of gas–liquid horizontal flow

In order to develop a flowmeter for the accurate measurement of multiphase flows, it is of the utmost importance to correctly identify the flow regime present to enable the selection of the optimal method for metering. In this study, the horizontal flow of air and water in a pipeline was studied under a multitude of conditions using electrical resistance tomography but the flow regimes that are presented in this paper have been limited to plug and bubble air–water flows. This study proposes a novel method for recognition of the prevalent flow regime using only a fraction of the data, thus rendering the analysis more efficient. By considering the average conductivity of five zones along the central axis of the tomogram, key features can be identified, thus enabling the recognition of the prevalent flow regime. Boolean logic and frequency spectrum analysis has been applied for flow regime recognition. Visualization of the flow using the reconstructed images provides a qualitative comparison between different flow regimes. Application of the Boolean logic scheme enables a quantitative comparison of the flow patterns, thus reducing the subjectivity in the identification of the prevalent flow regime.

Flow Regime Identification Under Adiabatic Upward Two-Phase Flow in a Vertical Rod Bundle Geometry

Flow regime maps were obtained for adiabatic air-water two-phase flow through a flow channel with 8 × 8 rod bundle, which simulated a typical rod bundle in a boiling water reactor. Impedance void meters were used to measure the area averaged void fraction at various axial locations in the flow channel. The Cumulative Probability Distribution Functions of the signals from the impedance meters were utilized along with self organizing neural network methodology to identify the flow regimes. The flow regimes were identified at five axial locations in the channel in order to understand the development of the flow regimes in axial direction. The experimental flow regime transition boundaries for bubbly to cap-bubbly and part of the cap-turbulent to churn-turbulent agreed with the theoretical boundaries of bubbly to slug and slug to churn-turbulent in round pipes. In addition, the two impedance void meters located across a spacer grid, revealed the nature of change in the flow regime across the spacer grid.

Flow Regime Identification in Boiling Two-Phase Flow in a Vertical Annulus

This work describes the application of an artificial neural network to process the signals measured by local conductivity probes and classify them into their corresponding global flow regimes. Experiments were performed in boiling upward two-phase flow in a vertical annulus. The inner and outer diameters of the annulus were 19.1 mm and 38.1 mm, respectively. The hydraulic diameter of the flow channel, DH, was 19.0 mm and the total length is 4.477 m. The test section was composed of an injection port and five instrumentation ports, the first three were in the heated section (z/DH = 52, 108 and 149 where z represents the axial position) and the upper ones in the unheated sections (z/DH = 189 and 230). Conductivity measurements were performed in nine radial positions for each of the five ports in order to measure the bubble chord length distribution for each flow condition. The measured experiment matrix comprised test cases at different inlet pressure, ranging from 200 kPa up to 950 kPa. A total number of 42 different flow conditions with superficial liquid velocities from 0.23 m/s to 2.5 m/s and superficial gas velocities from 0.002 m/s to 1.7 m/s and heat flux from 55 kW/m2 to 247 kW/m2 were measured in the five axial ports. The flow regime indicator has been chosen to be statistical parameters from the cumulative probability distribution function of the bubble chord length signals from the conductivity probes. Self-organized neural networks (SONN) have been used as the mapping system. The flow regime has been classified into three categories: bubbly, cap-slug and churn. A SONN has been first developed to map the local flow regime (LFR) of each radial position. The obtained LFR information, conveniently weighted with their corresponding significant area, was used to provide the global flow regime (GFR) classification. These final GFR classifications were then compared with different flow regime transition models.