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The ability to precisely estimate the void fraction of multiphase flow in a pipe is very important in the petroleum industry. In this paper, an approach based on our previous works is proposed for predicting the void fraction independent of flow regime and liquid phase density changes in gas–liquid two-phase flows. Implemented technique is a combination of dual modality densitometry and multi-beam gamma-ray attenuation techniques. The detection system is comprised of a single energy fan beam, two transmission detectors, and one scattering detector. In this work, artificial neural network (ANN) was also implemented to predict the void fraction percentage independent of the flow regime and liquid phase density changes. Registered counts in three detectors and void fraction percentage were utilized as the inputs and output of ANN, respectively. By applying the proposed methodology, the void fraction was estimated with a mean relative error of less than just 1.2480%.

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... Using the above mentioned methodology, the authors succeeded to recognize all the flow patterns and also to determine volume fractions with mean absolute error of less than 5.68%. Further researches in this field of study can be found in [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. ...

... Different volume fractions were achieved by altering density of the mixture as well as the mass fraction of each component. Although the modelled homogenous flow pattern in this investigation is an ideal case and is slightly different from the real homogenous pattern that occurs in multiphase flows, this system is easy and suitable for simulation because of its symmetry; other researchers adopted this model to simulate the homogenous regime [2,10,24]. Different volume fractions in the range of 10-80% with steps of 10% were replicated for all of the three flow patterns. Thirty-six modelled combinations of gas, oil, and water volume fractions for each flow pattern are shown in Fig. 3 which presents a graphical representation called ternary. ...

In this investigation, a fan-beam photon attenuation based system, including one X-ray tube and two sodium iodide crystal detectors, combined with group method of data handling (GMDH) neural network is proposed to recognize type of flow regime and predict gas-oil-water volume fractions of a three phase flow. One GMDH neural network was considered for recognizing flow patterns and two GMDH networks were implemented to predict the volume fractions. The recorded photon energy spectra from the two sodium iodide detectors were defined as the inputs of the three GMDH neural networks. The type of flow pattern and volume fractions were the output obtained from the first and the other two GMDH neural networks, respectively. Through the application of the proposed methodology, all of the flow patterns were recognized correctly except one single case. The volume fraction was also predicted with RMS error of less than 3.1.

... They used a 137 Cs source and one detector located next to the emitter to measure backscattered gamma rays. Roshani et al. implemented structures in order to determine the void fraction in twophase flows independent of the flow regime and liquid phase density changes [8]. Hanus et al. presented many time and frequency domain features to recognize the flow regimes in dynamic conditions [9][10]. ...

Determining the type of flow pattern and gas volumetric percentage with high precision is one of the vital topics for researchers in this field. For this, in this paper, three different types of liquid-gas two-phase flow regimes, namely annular, stratified, and homogenous were simulated in various gas volumetric percentages ranging from 5% to 90%. Simulations were performed by Monte Carlo N Particle (MCNP) code. Metering system includes one 137 Cs sources, one Pyrex glass, and two NaI detectors to register the transmitted photons. Because the signals which are received from the MCNP simulations contain high-frequency noises, the Savitzky-Golay filter has been applied to solve this problem. Then, thirteen characteristics in time domain were extracted from the recorded data of both detectors. Since none of features were capable of completely separating the flow regimes, two methods as "extracting two different features from the recorded data of both detectors" and "extracting three features from the recorded data of both detectors" were proposed. Using these methods, many different separator cases were found and the best separator cases were distinguished via S parameter. Finally, two artificial neural network (ANN) models of multilayer perceptron (MLP) were implemented for each method to identify the flow regimes and approximate the gas volumetric percentages. The proposed methodology and networks could diagnose all flow patterns properly, and also predict the volumetric percentage with a root mean square error (RMSE) of less than 0.60. Increasing the precision of two-phase flow meter by extracting time-domain features and signal processing techniques is the most important advantage of this study.

... Another advantage of this method is its low cost of implementation and operation. In combination with other measuring methods, described for example in the articles [7][8][9][10][11], the orifice-based measurement can be used to control multiphase flows. Orifice flowmeters also have disadvantages, the main ones being high pressure drop and high sensitivity to the orifice inlet profile [12]. ...

The article analyses the impact of the Reynolds number on the estimated uncertainty of the mass flow rate measurement using an orifice plate. The objects of the research were two types of orifices: centric (ISA) and eccentric, with the diameter ratio β = 0.5. Studies were performed by Monte Carlo simulation and experiments for Reynolds numbers in the range 10,000 ≤ Re ≤ 20,000. The obtained results have shown that for both orifice types, the results obtained from the experiments and from the Monte Carlo simulation are similar. The nature of changes in the expanded uncertainty of the flow measurement is very similar for each type. For the both types of orifices, the value of the expanded uncertainty of the flow measurement increases linearly with the increasing Reynolds number.
(The final version of the article is available free of charge at: https://www.sciencedirect.com/science/article/pii/S0263224120303894?via%3Dihub )

... A variety of typical methods, including capacitance sensors [6,7], wire-mesh sensors [8,9], radiation attenuation techniques [10,11], magnetic resonance [12,13] and ultrasonic techniques [14,15] have been applied to measure the gas void fraction of the gas-liquid two-phase flow. The sensing principle of the capacitance sensors makes use of different electrical permittivities of the phases. ...

The carbon capture and storage (CCS) system has the potential to reduce CO2 emissions from traditional energy industries. In order to monitor and control the CCS process, it is essential to achieve an accurate measurement of the gas void fraction in a two-phase CO2 flow in transportation pipelines. This paper presents a novel instrumentation system based on the laser attenuation technique for the gas void fraction measurement of the two-phase CO2 flow. The system includes an infrared laser source and a photodiode sensor array. Experiments were conducted on the horizontal and vertical test sections. Two Coriolis mass flowmeters are respectively installed on the single-phase pipelines to obtain the reference gas void fraction. The experimental results obtained show that the proposed method is effective. In the horizontal test section, the relative errors of the stratified flow are within ±8.3%, while those of the bubble flow are within ±10.6%. In the vertical test section, the proposed method performs slightly less well, with relative errors under ±12.2%. The obtained results show that the measurement system is capable of providing an accurate measurement of the gas void fraction of the two-phase CO2 flow and a useful reference for other industrial applications.

... The simulation configuration is shown in Fig. 1. It should be noted that the simulation geometry has been benchmarked with the laboratory experiments in our previous works [2,14,15]. Air with a density of 0.00125 g/cm 3 was used as the air phase in all simulations. Also, gasoil with a density of 0.826 g/cm 3 and the chemical formula C 12 H 23 was used as the liquid phase. ...

The void fraction is one of the most important parameters characterizing a multiphase flow. The prediction of the performance of any system operating with more than single phase relies on our knowledge and ability to measure the void fraction. In this work, a validated simulation study was performed in order to predict the void fraction independent of the flow pattern in gas-liquid two-phase flows using a gamma ray⁶⁰Co source and just one scintillation detector with the help of an artificial neural network (ANN) model of radial basis function (RBF). Three used inputs of ANN include a registered count under Compton continuum and counts under full energy peaks of 1173 and 1333 keV. The output is a void fraction percentage. Applying this methodology, the percentage of void fraction independent of the flow pattern of a gas-liquid two-phase flow was estimated with a mean relative error less than 1.17%. Although the error obtained in this study is almost close to those obtained in other similar works, only one detector was used, while in the previous studies at least two detectors were employed. Advantages of using fewer detectors are: cost reduction and system simplification.

... One of the most important issues in this regard is the best combination of gamma-ray sources; however, there is no study about the best combination until now. Roshani and coworkers used 241 Am & 137 Cs, 60 Co and 152 Eu & 137 Cs in their works [2][3][4][5][6][7][8][9][10][11][12]. Hanus and coworkers used 133 Ba and 241 Am in some works [13][14][15][16]. ...

The used metering technique in this paper is based on the multienergy (at least dual) gamma-ray attenuation. The aim of the current study is investigation of different combinations of sources in order to find the best combination for precise metering gas, oil and water percentages in annular three-phase flows. The required data were generated numerically using Monte Carlo N Particle extended (MCNPX) code. As a matter of fact, the current investigation devotes to predict the volume fractions in the annular three-phase flow, on the basis of a multienergy metering system including different radiation sources and one sodium iodide detector, using the hybrid model. Since the summation of volume fractions is constant, a constraint modeling problem exists, meaning that the hybrid model must predict only two volume fractions. Six hybrid models associated with the number of applied radiation sources are employed. The models are applied to predict the oil and gas volume fractions. For the next step, the hybrid models are trained based on numerically obtained data from the MCNPX code. The results show that the best prediction results are obtained for the oil and gas volume fractions of a system with the (241Am & 137Cs) radiation sources.

... Nazemi et al. (2015) applied a method to eliminate the dependency of multiphase flow meter on liquid phase density in two stratified phase horizontal flows. Further investigations in this field and also using ANN in nuclear engineering problems can be found in these references (Hanus 2015;Hanus et al. 2014a, b;Nazemi et al. 2014Nazemi et al. , 2016bMosorov et al. 2016;Roshani et al. 2014Roshani et al. , 2017bRoshani and Nazemi 2017;Yadollahi et al. 2016a, b;Zych et al. 2014). ...

The use of adaptive neuro-fuzzy inference system (ANFIS) has been reported for predicting the volume fractions in a gas–oil–water multiphase system. In fact, the volume fractions in the annular three-phase flow are measured based on a dual energy metering system consisting of \(^{152}\hbox {Eu}\) and \(^{137}\hbox {Cs}\) and one NaI detector using ANFIS. Since the summation of volume fractions is constant, therefore ANFIS must predict only two volume fractions. In this study, three ANFIS networks are applied. The first is utilized to predict the gas and water volume fractions. The next one is applied to predict the gas and oil, and the last one is used to predict the water and oil volume fractions. In the next step, ANFIS networks must be trained based on numerically obtained data from MCNP-X code. Then, the average testing errors of these three networks are computed and compared. The network with the least error has been selected as the best predictor model.

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.

It is important for operators of poly-pipelines in petroleum industry to continuously monitor characteristics of transferred fluid such as its type and amount. To achieve this aim, in this study a dual energy gamma attenuation technique in combination with artificial neural network (ANN) is proposed to simultaneously determine type and amount of four different petroleum by-products. The detection system is composed of a dual energy gamma source, including americium-241 and barium-133 radioisotopes, and one 2.54 cm × 2.54 cm sodium iodide detector for recording the transmitted photons. Two signals recorded in transmission detector, namely the counts under photo peak of Americium-241 with energy of 59.5 keV and the counts under photo peak of Barium-133 with energy of 356 keV, were applied to the ANN as the two inputs and volume percentages of petroleum by-products were assigned as the outputs.

In this paper, X-ray tube is introduced as a potential alternative for radioisotope sources used in radiation based liquid-gas two-phase flowmeters. X-ray tubes have lots of advantages over the radioisotope sources such as having an adjustable emitting photon's energy, being safer from point of view of radiation health physics during the transportation of the source, having ability to generate a high flux photon beam, and etc. The proposed radiation based system in this study composes an X-ray tube with a tube voltage of 150 kV and a 2.5 mm aluminum filter as the radiation source and one sodium iodide crystal as the photon detector. A pipe was positioned between the X-ray tube and the detector. Two main flow regimes of annular and stratified with different void fractions were modelled inside the pipe. Artificial neural network model of multi-layer perceptron (MLP) was also used in this study for analyzing the obtained data. The output spectrum of sodium iodide detector with 150 samples was applied as the input of multi-layer perceptron network and void fraction was considered as its output. The root mean squared error of proposed measuring system was 4.13 which shows the X-ray tube can be implemented as a promising alternative for radioisotope in radiation based two phase flow meters.

Radiation based gauges have been widely utilized as a nondestructive and robust tool for measuring the thickness of metal sheets in industry. The typical radiation thickness meter can just work accurately when the composition of the material is fixed during the measurement process. In conditions that material composition may differ substantially from the nominal composition, such as manufacturing rolled metals factories, the thickness measurements would be along with errors. The purpose of the present research is resolving the problem of measuring the thickness of metal sheets with various alloys. The aluminum is investigated in this work as a case study but the procedure can be applied for other types of metals. As the first step, the performance of various arrangements of two main detection techniques, named dual energy and dual modality, were investigated using MCNPX code to obtain optimum technique and arrangement. The simulation results indicated that a binary combination of ²⁴¹ Am- ⁶⁰ Co isotopes as the source and one transmission detector in dual energy technique is the most appropriate choice. After then, an experimental setup based on the obtained optimal technique from simulation investigations was established. The aluminum sheets with 4 alloy types of 1050, 3105, 5052 and 6061 and thicknesses in the range of 0.2–4 cm with a step of 0.2 cm were tested and the obtained data were implemented for testing and training the artificial neural network (ANN). The proposed methodology could predict the thickness of aluminum sheet independent of its alloy type with an error of less than 0.04 cm in experiments.

In recent years, there has been an increasing interest in implementing artificial intelligence in radiation based multiphase flow meter systems. This study revolves around an approach in which the grey wolf optimization (GWO) algorithm was employed to train the artificial neural network (ANN), and a hybrid network called as the GWO-trained ANN was introduced to predict the volume fractions in a gas-oil-water multiphase flow system. After that, the obtained GWO-based neural network was employed to measure the volume fractions in the stratified three-phase flow, on the basis of a dual energy metering system including the ¹⁵²Eu and ¹³⁷Cs and one NaI detector. The first network was utilized to predict the oil and gas, the next one to predict the gas and water, and the last one to predict the water and oil volume fractions. In the next step, the GWO-based networks were trained based on numerically obtained data from MCNP-X code. The accuracy of the nets were evaluated and compared with each other. Based on the results, the best GWO-based net could predict the oil and gas volume fractions with the mean absolute percentage error of less than 0.8% and 0.4% for the testing and checking data, respectively.

In this paper, based on dual-energy broad beam gamma ray attenuation technique (using two transmission 1-inch NaI detectors and a dual-energy gamma ray source), an artificial neural network (ANN) model was used in order to predict the volume fraction of gas, oil and water in three-phase flows independent of the flow regime. A multilayer perceptron (MLP) neural network was used for developing the ANN model in MATLAB 8.1.0.604 software. The input parameters of the MLP model were registered counts under first and second full energy peaks of the both transmission NaI detectors, and the outputs were gas and oil percentage. The volume fractions were obtained precisely independent of flow regime using the presented model. Mean absolute error of the presented model was less than 2.24%.

Void fraction is an important parameter in the oil industry. This quantity is necessary for volume rate measurement in multiphase flows. In this study, the void fraction percentage was estimated precisely, independent of the flow regime in gas–liquid two-phase flows by using γ-ray attenuation and a multilayer perceptron neural network. In all previous studies that implemented a multibeam γ-ray attenuation technique to determine void fraction independent of the flow regime in two-phase flows, three or more detectors were used while in this study just two NaI detectors were used. Using fewer detectors is of advantage in industrial nuclear gauges because of reduced expense and improved simplicity. In this work, an artificial neural network is also implemented to predict the void fraction percentage independent of the flow regime. To do this, a multilayer perceptron neural network is used for developing the artificial neural network model in MATLAB. The required data for training and testing the network in three different regimes (annular, stratified, and bubbly) were obtained using an experimental setup. Using the technique developed in this work, void fraction percentages were predicted with mean relative error of <1.4%.

Artificial neural network (ANN) is an appropriate method used to handle the modeling, prediction and classification problems. In this study, based on nuclear technique in annular multiphase regime using only one detector and a dual energy gamma-ray source, a proposed ANN architecture is used to predict the oil, water and air percentage, precisely. A multi-layer perceptron (MLP) neural network is used to develop the ANN model in MATLAB 7.0.4 software. In this work, number of detectors and ANN input features were reduced to one and two, respectively. The input parameters of ANN are first and second full energy peaks of the detector output signal, and the outputs are oil and water percentage. The obtained results show that the proposed ANN model has achieved good agreement with the simulation data with a negligible error between the estimated and simulated values. Defined MAE% error was obtained less than 1%.

Strontium (Sr) and Cesium (Cs) are two important nuclear fission products which are present in the radioactive wastewater resulting from nuclear power plants. They should be treated by considering environmental and economic aspects. In this study, artificial neural network (ANN) was implemented to evaluate the optimal experimental conditions in continuous electrodeionization method in order to achieve the highest removal percentage of Sr and Ce from aqueous solutions. Three control factors at three levels were tested in experiments for Sr and Cs: Feed concentration (10, 50 and 100 mg/L), flow rate (2.5, 3.75 and 5 mL/min) and voltage (5, 7.5 and 10 V). The obtained data from the experiments were used to train two ANNs. The three control factors were utilized as the inputs of ANNs and two quality responses were used as the outputs, separately (each ANN for one quality response). After training the ANNs, 1024 different control factor levels with various quality responses were predicted and finally the optimum control factor levels were obtained. Results demonstrated that the optimum levels of the control factors for maximum removing of Sr (97.6%) had an applied voltage of 10 V, a flow rate of 2.5 mL/min and a feed concentration of 10 mg/L. As for Cs (67.8%) they were 10 V, 2.55 mL/min and 50 mg/L, respectively.

Artificial neural network (ANN) is a good technique used to handle problems of modeling, prediction, control, and classification. In this study, four accurate and precise MLP model were developed. The first one was used in order to determine the type of three phase flow regime. All of the regimes were recognized correctly. Then, according to determined regime, three independent MLP models were used in order to predict the volume fraction percentages of gas, oil and water. The networks were developed based on the validated simulated data from MCNPX code. The volume fractions were measured with Mean Relative Error (MRE) of less than 1.63%.

The problem of how to accurately measure the flow rate of oil–gas–water mixtures in a pipeline remains one of the key challenges in the petroleum industry. This paper proposes a new methodology for identifying flow regimes and predicting volume fractions in gas-oil-water multiphase systems using dual energy fan-beam gamma-ray attenuation technique and artificial neural networks. The novelty of this study in comparison with previous works, is using just 4 extracted features (photo peaks of ²⁴¹Am and ¹³⁷Cs in 2 detectors) from the gamma ray spectrums instead of using the whole gamma ray spectrum, which reduces the undesired noises and also improves the speed of recognition in real situations. Radial basis function was used for developing the neural network model in MATLAB software in order to classify the flow patterns (annular, stratified and homogenous) and predict the value of volume fractions. The ideal and static theoretical models for flow regimes have been developed using MCNP-X code. The proposed networks could correctly recognize all the three different flow regimes and also determine volume fractions with mean absolute error of less than 5.68% according to the recognized regime.

In this study, a simple detection system comprised of one 60Co source and just one NaI detector was investigated in order to identify flow regime and measure void fraction in gas-liquid two phase flows. For this purpose, 3 main flow regimes of two-phase flows including stratified, homogenous and annular with void fractions in the range of 5–95% were simulated by Monte-Carlo N Particle (MCNP) code. At first step, 3 features (count under full energy peaks of 1.173 and 1.333 MeV, and count under Compton continuum) were extracted from registered gamma spectrum. These 3 extracted features were used as inputs of artificial neural network (ANNs). A primary network was trained for identifying the flow regimes, but after testing many different structures, it was found that just two regimes of stratified and annular could be completely identified from each other. After identifying the mentioned two flow regimes by the first ANN, two specific ANNs were also implemented for predicting the void fraction. Using the proposed method in this work, void fraction percentages were predicted with a mean relative error (MRE) of less than only 0.42%. Using fewer detectors is of advantage in industrial nuclear gauges, because of reducing economical expenses and also simplicity of working with these systems.

Colemanite is the most convenient boron mineral which has been widely used in construction of radiation shielding concrete in order to improve the capture of thermal neutrons. But utilization of Colemanite in radiation shielding concrete has a deleterious effect on both physical and mechanical properties. In the present work, Taguchi method and artificial neural network (ANN) were employed to find an optimal mixture of Colemanite based concrete in order to improve the boron content of concrete and increase thermal neutron absorption without violating the standards for physical and mechanical properties. Using Taguchi method for experimental design, 27 concrete samples with different mixtures were fabricated and tested. Water/cement ratio, cement quantity, volume fraction of Colemanite aggregate and silica fume quantity were selected as control factors, and compressive strength, ultrasonic pulse velocity and thermal neutron transmission ratio were considered as the quality responses. Obtained data from 27 experiments were used to train 3 ANNs. Four control factors were utilized as the inputs of 3 ANNs and 3 quality responses were used as the outputs, separately (each ANN for one quality response). After training the ANNs, 1024 different mixtures with different quality responses were predicted. At the final, optimum mixture was obtained among the predicted different mixtures. Results demonstrated that the optimal mixture of thermal neutron shielding concrete has a water-cement ratio of 0.38, cement content of 400 kg/m3, a volume fraction Colemanite aggregate of 50% and silica fume-cement ratio of 0.15.

Due to variation of neutron energy spectrum in the target sample during the activation process and to peak overlapping caused by the Compton effect with gamma radiations emitted from activated elements, which results in background changes and consequently complex gamma spectrum during the measurement process, quantitative analysis will ultimately be problematic. Since there is no simple analytical correlation between peaks’ counts with elements’ concentrations, an artificial neural network for analyzing spectra can be a helpful tool. This work describes a study on the application of a neural network to determine the percentages of cement elements (mainly Ca, Si, Al, and Fe) using the neutron capture delayed gamma-ray spectra of the substance emitted by the activated nuclei as patterns which were simulated via the Monte Carlo N-particle transport code, version 2.7. The Radial Basis Function (RBF) network is developed with four specific peaks related to Ca, Si, Al and Fe, which were extracted as inputs. The proposed RBF model is developed and trained with MATLAB 7.8 software. To obtain the optimal RBF model, several structures have been constructed and tested. The comparison between simulated and predicted values using the proposed RBF model shows that there is a good agreement between them. © 2016, Società Italiana di Fisica and Springer-Verlag Berlin Heidelberg.

A gamma-ray transmission technique is present to measure the void fraction and identify the flow regime of a two-phase flow using two detectors which were optimized in terms of detector orientation. Using Monte-Carlo simulation, experimental results were utilized for training an artificial neural network. Radial Basis Function was used to classify flow regimes (annular, stratified and bubbly) and predict the value of void fraction. All of the training and testing data sets were determined correctly and the mean relative error percentage of predicted void fraction was less than 1.5%. Although the method was applied to a certain pipe size in a static flow configuration, it provides a framework for application to other configurations.

Neutron Activation Analysis (NAA) is an important technique for quantitative and qualitative multi-element analysis. The Inertial Electrostatic Confinement Fusion (IECF) device is known as a fast and monoenergetic neutron generator. In this study, NAA for cement elements using an IECF facility as a high energy neutron source was investigated. The Iranian IECF device was simulated using the MCNPX code version 2.7 and the ‘ACT card’ was used to consider the induced delayed gamma-ray spectra during delayed gamma NAA (DGNAA). The peaks related to Al, Ca, Fe and Si were distinguished precisely, which shows the applicability of IECF as an appropriate neutron source for DGNAA analysis for cement elements.

In the production of radiation shielding concrete (RSC), it is necessary to find an optimal mixture to fulfill all the desired quality characteristics simultaneously. In this study, Taguchi method and artificial neural network (ANN) were implemented to find the optimal mixture of RSC containing lead-slag aggregate. Using Taguchi method for experimental design, 27 concrete samples with different mixtures were fabricated and tested. Water–cement ratio, cement quantity, volume ratio of lead-slag aggregate and silica fume quantity were selected as control factors and slump, compressive strength and gamma linear attenuation coefficient were considered as the quality responses. Obtained data from 27 experiments were used to train 3 ANNs. Four control factors were utilized as the inputs of all the 3 ANNs and 3 quality responses were used as the outputs, separately (each ANN for one quality response). After training the ANNs, 1024 different mixtures with different quality responses were predicted. At the final, optimum mixture was obtained among the predicted different mixtures. Results demonstrated that the optimal mixture of RSC has a water–cement ratio of 0.45, cement quantity of 390 kg, a volume fraction of lead slag aggregate of 60% and silica fume–cement ratio of 0.15.

The main purpose of this study is experimental and numerical void fraction measurement in two-phase flow inside a vertical pipe by using gamma-ray. Three types of flow regimes including homogenous, stratified and annular were modeled in a vertical pipe by using polyethylene phantoms. These three flow regimes are basis regimes in two-phase flow and the other flow regimes are incorporated of these patterns. For all three modeled flow regimes all transmitted and scattered gamma rays in all directions were measured by setting a gamma ray source and detector around the pipe. Numerical modeling was done by using MCNP code to improve the accuracy and validation of experimental results. Finally, innovative correlations to predict the void fraction in two-phase flow in a vertical pipe was presented.

The fluid properties strongly affect the performance of radiation-based multiphase flow meter. By changing the fluid properties (especially density), recalibration is necessary. In this study, a method was presented to eliminate the dependency of multiphase flow meter on liquid phase density in stratified two phase horizontal flows. At the first step the position of the scattering detector was optimized in order to achieve highest sensitivity. Several experiments in optimized position were done. Counts under the full energy peak of transmission detector and total counts of scattering detector were applied to the Radial Basis Function neural network and the void fraction percentage was considered as the neural network output. Using this method, the void fraction was predicted independent of the liquid phase density change in stratified regime of gas-liquid two-phase flows with mean relative error percentage less than 1.2%.

This paper presents the application of adaptive neuro-fuzzy inference system (ANFIS) for prediction of fluid density in a previously designed and constructed gamma ray densitometer for pipes of various diameters and different fluids densities. The input parameters of the proposed ANFIS model are the pipe diameter and the number of the counted photons and the output is the density of the considered material. The required data for training and testing the ANFIS model has been obtained based on simulations using MCNP4C Monte Carlo code. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the proposed ANFIS model. Simulations for 4-in. polyethylene pipe had been validated with the experimental data previously. The proposed ANFIS model has achieved good agreement with the experimental results and has a small error between the estimated and experimental values. The obtained results show that the mean relative error percentage (MRE%) for training and testing data are less than 2.14% and 2.64%, respectively.

Flow regime information can be used to improve measurement accuracy on gas volume fractions and as complementary information for other types of flow instrumentation in order to enhance their accuracy. In this study a method based on dual modality densitometry using artificial neural network (ANN) was presented to first identify the flow regime and then predict the void fraction in two-phase flows. The full energy peak (transmission count), photon counts of Compton edge in transmission detector and total count in the scattering detector, were chosen as the three inputs of the ANN. The stratified, homogenous and annular regimes with various void fractions were simulated by the Monte Carlo N-Particle (MCNP) code, version X, in order to obtain adequate data set used for training and testing the ANN. To validate the simulated results, several experiments were carried out in the annular regime of two-phase flow. Experimental results were in good agreement with the simulated data. The maximum difference between experimental and simulated results for the transmission, Compton edge and scattered counts, is 3.4%, 3.8% and 3.6%, respectively. By applying this method, all the three regimes were correctly distinguished and void fraction was predicted in the range of 5-95% with error of less than 1.1%.

Artificial Neural Networks (ANNs) have been applied to deal with flow and heat transfer problems over the past two decades. In the present paper, recent work on the applications of ANNs for predicting the flow regime, pressure drop, void fraction, critical heat flux, onset of nucleate boiling, heat transfer coefficient and boiling curve has been reviewed, respectively. As can be noted in this review work, various types of ANNs can be employed as predictors with acceptable precisions. At the end of this review, methods to improve performance of ANNs and further applications of ANNs in flow and heat transfer problems were introduced.

This work investigates the response of attenuation gamma-rays in volume fraction prediction system for water–gas–oil multiphase flows considering variations in salinity of water. The approach is based on pulse height distributions pattern recognition by artificial neural network. The detection system uses fan beam geometry, comprised of a dual-energy gamma-ray source and two NaI(Tl) detectors in order calculate transmitted and scattered beams. Theoretical models for annular and stratified flow regimes have been developed using MCNP-X code to provide data for the network.

Void fraction simulating stratified air–water flow in cylindrical
tubes of different radii was measured using transmission and scattering
of gamma rays. A simple experimental set-up using 137Cs
γ-ray point source of 10 μCi and NaI(Tl) detector was used. The
void fractions determined from Compton–Compton scattering and
transmission peaks were found in good agreement with the real void
fractions. However, deviations were noticed between the results obtained
from traditional Compton scattered gamma rays and real void fraction. It
was shown that sensitivity of gamma ray scattering is better than the
transmission measurements. The set-up used in the present work is
simpler than those existing in literature and radiation safety and
shielding requirements are minimized.

This work presents a new methodology for flow regimes identification and volume fraction predictions in water–gas–oil multiphase systems. The approach is based on gamma-ray pulse height distributions (PHDs) pattern recognition by means the artificial neural networks (ANNs). The detection system uses appropriate fan beam geometry, comprised of a dual-energy gamma-ray source and two NaI(Tl) detectors adequately positioned in order measure transmitted and scattered beams, which makes it less dependent on the regime flow. The PHDs are directly used by the ANNs without any parameterization of the measured signal. The system comprises four ANNs. The first identifies the flow regime and the other three ANNs are specialized in volume fraction predictions for each specific regime. The ideal and static theoretical models for annular, stratified and homogeneous regimes have been developed using MCNP-X mathematical code, which was used to provide training, test and validation data for the ANNs. The energy resolution of NaI(Tl) detectors is also considered on the mathematical model. The proposed ANNs could correctly identify all three different regimes with satisfactory prediction of the volume fraction in water–gas–oil multiphase system, demonstrating to be a promising approach for this purpose.

Gamma-ray densitometry is a frequently used non-intrusive method for determining void fraction in two- and multi-phase gas liquid pipe flows. The traditional gamma-ray densitometer using a 137Cs source and a scintillation PMT detector has proved itself reliable and robust. This paper presents a method using a low energy source (241Am), which offers the advantages of reduced size due to reduced shielding requirements, compact detectors, and lesser dependence on flow regime, due to its multibeam measurement configuration. These are important aspects with regard to future subsea and down-hole fluid flow measurement applications. The performance of single-beam and the compact multi-beam low-energy gamma-ray measurement principles was compared. Consideration of the measurement volume, defined by the detector area and the radiation beam, demonstrated the flow regime dependency of single-beam gamma-ray measurement principles. With the multi-beam low-energy gamma-ray measurement principle, the dependence on flow regime is negligible when several detector responses are combined. Use of phantoms and one movable detector verified the multi-beam gamma-ray measurement principle. The detector responses at several positions around the pipe were obtained for different flow regimes and void fractions.

The gamma ray scattering energy spectrum detected by one detector was presented to distinguish the gas liquid two-phase flow regime of vertical pipe. The simulation geometries of the gamma ray scattering measurement were built using Monte Carlo software Geant4. Computer simulations were carried out with homogeneous flow, annular flow and slug flow. The results show that the scattering energy characters of homogeneous flow and annular flow have significantly different. The scattering spectrum of slug flow is similar to annular flow for long gas slugs and similar to homogeneous flow for short gas slugs. The RBF neural networks were used to predict the flow regime. The results show that the homogeneous flow and annular flow can be completely distinguished and most of the slug flows were recognized by the neural network. It was demonstrated that the method of one detector scattering energy spectrum has the ability to identify the typical gas liquid flow regime of vertical pipe and fit the applications in engineering.

The models of dual modality densitometry were developed. It can be used for measuring the gas volume fraction and water volume
fraction in oil water gas pipe flow. The models are complex. In order to solve models, it often uses simplified models. This
reduces measurement precision. The method of measuring gas and water volume fraction using neural networks was presented.
The simulation data was gotten using Geant4. The radial basis function networks were trained and tested on computer simulation
data. The results show that networks predicted gas volume fraction fit true gas fraction well and water volume fraction has
some deviations.

This work presents methodology based on nuclear technique and artificial neural network for volume fraction predictions in annular, stratified and homogeneous oil-water-gas regimes. Using principles of gamma-ray absorption and scattering together with an appropriate geometry, comprised of three detectors and a dual-energy gamma-ray source, it was possible to obtain data, which could be adequately correlated to the volume fractions of each phase by means of neural network. The MCNP-X code was used in order to provide the training data for the network.

Dual mode densitometry is presented as a novel method of measuring the gas volume fraction in gas/oil/water pipe flows independent of the salinity of the water component. The different response in photoelectric attenuation and Compton scattering to changes in salinity is utilized. The total attenuation coefficient is found through traditional transmission measurements with a detector positioned outside the pipe wall diametrically opposite the source. The scatter response is measured with a second detector positioned somewhere between the source and the transmission detector. The feasibility of the method is demonstrated for homogeneously mixed flows.

Development of the multiphase meter using gamma densitometer concept

- M M Ibrahim
- Babelli