Abstract In this work, the ability of artificial neural networks (ANNs) to predict void fraction ... more Abstract In this work, the ability of artificial neural networks (ANNs) to predict void fraction of gas–liquid two–phase flow in horizontal and inclined pipes was investigated. For this purpose, an ANN model was designed and trained using a total of 301 experimental data points reported in the literature for inclination angles between –20° and +20°. Pipe inclination angle as well as superficial Reynolds number of gas (Re sg ) and liquid (Re sl ) were chosen as input parameters of different structures of multilayer perceptron (MLP) neural networks, while the corresponding void fraction was selected as their output parameter. A hyperbolic tangent sigmoid and a linear function were employed as transfer functions of hidden and output layers, respectively, and Levenberg–Marquardt back propagation algorithm was used to train the networks. By trial–and–error method, a three–layer network with 10 neurons in the hidden layer was achieved as optimal structure of the ANN which made it possible to predict the void fraction with a high accuracy. Mean absolute percent error (MAPE) of 1.81% and coefficient of determination (R 2 ) of 0.9976 for training data and MAPE of 1.52% and R 2 value of 0.9948 for testing data were obtained. Also for all data, MAPE of 1.95% and R 2 value of 0.9972 were calculated, and 96% data were within ±5% error band. In addition, the accuracy of the proposed ANN model was compared with the predictions from 17 void fraction correlations available in the literature for different flow patterns and horizontal and inclined flows. For all cases, the proposed ANN model gave better performance than all of the studied correlations. The results confirm the very good capability of the ANNs to predict the void fractions of gas–liquid flow in inclined pipes, regardless of flow pattern. Finally, by performing interpolation using the trained network, the void fraction values for some other conditions were predicted.
Abstract This paper presents the application of artificial neural network (ANN) in prediction of ... more Abstract This paper presents the application of artificial neural network (ANN) in prediction of water holdup of oil–water two-phase flow in a vertical and an inclined pipe (90°, 75°, 60°, and 45° from horizontal) without knowing the type of flow pattern. For this purpose, superficial velocity of water and oil and the inclination angles of the pipe were used as input parameters, while water holdup values of two-phase flow were used as output parameters in training and testing of the multi-layer, feed-forward, back-propagation neural networks. Experimental data (468 data points) were taken from literature and used for developing of the ANN model. The obtained results showed that the network predictions have very good agreement with the experimental water holdup data. The accuracy between the neural network predictions and experimental data was achieved with low average absolute percent error (AAPE) and high coefficient of determination ( R 2 ) for both training data (AAPE = 2.34% and R 2 = 0.999) and testing data (AAPE = 2.89% and R 2 = 0.997) sets. In addition, a comparison of the prediction results of the proposed ANN model with Mukherjee et al. (1981) correlation (AAPE = 9.83% and R 2 = 0.961) revealed that the correlation had more deviations.
Abstract Polyamide based composites were formed by melt blending of polyamide 6 (PA6) with a γ) -... more Abstract Polyamide based composites were formed by melt blending of polyamide 6 (PA6) with a γ) -alumina powder toughened with ethylene–octene copolymer grafted by maleic anhydride (EOC-g-MAH) and also without EOC in a corotating twin screw extruder. Mechanical properties, morphological structure and thermal stability of toughened PA6 (PA6-g-EOC) and PA6-g-EOC/alumina composites were investigated in this study.To study the effect of powder loading of γ-alumina on the mechanical properties of the composites such as tensile strength, modulus of elasticity, break point and impact strength, varied amounts of 5, 10 and 15 wt-% were deployed. The toughened PA6–γ-alumina composites, i.e. blended by EOC-g-MAH, revealed higher impact strength and more toughness compared to that of the PA6–γ-alumina composites without EOC-g-MAH. Morphology of the composites was investigated by scanning electron microscopy (SEM) from the as moulded specimens. Micrographs showed a fine dispersion of γ-alumina particles in polyamide matrix due to appropriate mixing. Furthermore, thermal stability and degradation characteristics of the toughened PA6–γ-alumina composites were measured by thermogravimetric analysis. The addition of γ-alumina into the polyamide matrix showed an increase in thermal resistance so that thermal stability was increased by a rise in the powder loading.
This paper presents the application of artificial neural network (ANN) in prediction of heat tran... more This paper presents the application of artificial neural network (ANN) in prediction of heat transfer coefficients (HTCs) of two-phase flow of air–water in a pipe in the horizontal and slightly upward inclined (2, 5, and 7 deg) positions. For this purpose, the superficial liquid and gas Reynolds numbers and the inclination of the pipe were used as input parameters, while the HTCs of two-phase flow were used as output parameters in training and testing of the multilayered, feedforward, backpropagation neural networks. In this present study, experimental data were taken from literature and then used for the ANN model. The superficial liquid and gas Reynolds numbers ranged from 740 to 26,100 and 560 to 47,600 for water and air, respectively. The mean deviations against experimental data were determined for the model. Results showed that the network predictions were in very good agreement with the experimental HTC data, whereas the correlation showed more deviations. Finally, results sh...
The cubic equation of state (CEoS) is a powerful method for calculation of the vapor-liquid equil... more The cubic equation of state (CEoS) is a powerful method for calculation of the vapor-liquid equilibrium (VLE) in polymer solutions. Using CEoS for both the vapor and liquid phases allows one to calculate the non-ideality of polymer solutions based on a single EoS approach. In this research, VLE calculations of polypropylene glycol (polypropylene oxide) [PPG(PPO)]/solvent solutions were carried out. In this approach, eight models containing Peng-Robinson-Stryjek-Vera (PRSV) and Soave-Redlich-Kwong (SRK) CEoS separately combined with four mixing rules, namely van der Waals one-fluid mixing rule with one adjustable parameter (vdW1), van der Waals one-fluid mixing rule with two adjustable parameters (vdW2), Wong-Sandler (WS), and Zhong-Masuoka (ZM) were applied to calculations of bubble point pressure. For a better correlation, the adjustable binary interaction parameters existing in any mixing rule were optimized. The results were very acceptable and satisfactory. The results of absolu...
Vapor-liquid equilibria (VLE) of polymer/solvent solutions is a topic of importance because of se... more Vapor-liquid equilibria (VLE) of polymer/solvent solutions is a topic of importance because of several areas of applications, including the designing of process equipment. Theoretical and thermodynamic models are reported in the literature for the estimation of VLE. However, up until now, the simultaneous representation of VLE and pressure-volume-temperature data is not satisfactory enough with respect to experimental accuracies. New models are therefore highly required. In the present study, a hybrid model including artificial neural networks (ANN) and genetic algorithm (GA) were applied to estimating the VLE data of seven binary polystyrene (PS)/solvents. The ranges of variables used were 283.15–343.15 K and 0.105–7.46 MPa. The VLE data of these systems were taken from the literature. The net was trained, validated and tested with randomly 65% (108 data points), 10% (17 data points) and the 25% (42 data points), respectively. The mean deviations from the experimental data were det...
This paper presents the application of artificial neural network (ANN) in prediction of heat tran... more This paper presents the application of artificial neural network (ANN) in prediction of heat transfer coefficients (HTCs) of two-phase flow of air-water in a pipe in the horizontal and slightly upward inclined (2, 5, and 7 deg) positions. For this purpose, the superficial liquid and gas Reynolds numbers and the inclination of the pipe were used as input parameters, while the HTCs of two-phase flow were used as output parameters in training and testing of the multilayered, feedforward, backpropagation neural networks. In this present study, experimental data were taken from literature and then used for the ANN model. The superficial liquid and gas Reynolds numbers ranged from 740 to 26,100 and 560 to 47,600 for water and air, respectively. The mean deviations against experimental data were determined for the model. Results showed that the network predictions were in very good agreement with the experimental HTC data, whereas the correlation showed more deviations. Finally, results sh...
In the present study, isothermal fluid flow in an annular helically-coiled tube has been modeled ... more In the present study, isothermal fluid flow in an annular helically-coiled tube has been modeled for different fluid flow regimes. The three-dimensional governing equations for mass and momentum have been solved, and k-e turbulence model was used to model the turbulent flow regime. The fluid is water at Dean numbers ranging from 125 to 5600. It was found that centrifugal forces create a high velocity region at the outer side of the annular helicoidal pipe walls. The acceleration forces acting on the fluid flow create high pressure region near the outer pipe wall. The friction factor values for both laminar and turbulent regimes were compared with the experimental data and correlations from literature. Friction factor decreases as the tendency for turbulence increases. The best consistency between correlation and CFD model was observed for turbulent regime.
Designing an artificial neural network for prediction of HTC of a refrigerant (R134a). 440 experi... more Designing an artificial neural network for prediction of HTC of a refrigerant (R134a). 440 experimental data points were applied for training the ANN model. The input parameters were inclination angle, mass flux, saturation temperature and mean vapor quality. The ANN was trained for the whole range of inclination angles regardless of the flow pattern. The results confirm the high ability of the ANN for predicting the HTC. g r a p h i c a l a b s t r a c t a b s t r a c t An artificial neural network model was developed to predict convective heat transfer coefficient (HTC) during condensation of R134a in an inclined smooth tube for the entire range of inclination angles at different saturation temperatures and regardless of flow pattern. The network was designed and trained using a total of 440 experimental data points collected from the literature. Inclination angle, mass flux, saturation temperature and mean vapor quality were used as input variables of multiple layer perceptron (MLP) neural network, while the corresponding HTC was selected as its output variable. By trial-and-error method, MLP network with 18 neurons in the hidden layer was achieved as optimal structure of the ANN which made it possible to predict the HTC with a high accuracy. Mean absolute percent error (MAPE) of 1.48% and correlation coefficient (R) of 0.997 for training data and MAPE of 1.94% and R value of 0.995 for testing data were obtained. Also, 95% and 99% all data were within ±5% and ±7% error band, respectively. MAPE of 1.61% and R value of 0.9963 were calculated for all data. These results confirm the high ability of the ANNs for predicting the HTC values for the entire range of inclination angles and independent of the flow pattern.
In this work, the ability of artificial neural networks (ANNs) to predict void fraction of gas–li... more In this work, the ability of artificial neural networks (ANNs) to predict void fraction of gas–liquid two– phase flow in horizontal and inclined pipes was investigated. For this purpose, an ANN model was designed and trained using a total of 301 experimental data points reported in the literature for inclination angles between –20 ° and + 20 °. Pipe inclination angle as well as superficial Reynolds number of gas (Re sg) and liquid (Re sl) were chosen as input parameters of different structures of multilayer perceptron (MLP) neural networks, while the corresponding void fraction was selected as their output parameter. A hyperbolic tangent sigmoid and a linear function were employed as transfer functions of hidden and output layers, respectively, and Levenberg–Marquardt back propagation algorithm was used to train the networks. By trial–and–error method, a three–layer network with 10 neurons in the hidden layer was achieved as optimal structure of the ANN which made it possible to predict the void fraction with a high accuracy. Mean absolute percent error (MAPE) of 1.81% and coefficient of determination (R 2) of 0.9976 for training data and MAPE of 1.52% and R 2 value of 0.9948 for testing data were obtained. Also for all data, MAPE of 1.95% and R 2 value of 0.9972 were calculated, and 96% data were within ±5% error band. In addition, the accuracy of the proposed ANN model was compared with the predictions from 17 void fraction correlations available in the literature for different flow patterns and horizontal and inclined flows. For all cases, the proposed ANN model gave better performance than all of the studied correlations. The results confirm the very good capability of the ANNs to predict the void fractions of gas–liquid flow in inclined pipes, regardless of flow pattern. Finally, by performing interpolation using the trained network, the void fraction values for some other conditions were predicted.
Although curved pipes are used in a wide range of applications, flow in curved pipes is relativel... more Although curved pipes are used in a wide range of applications, flow in curved pipes is relatively less well known than that in straight ducts. This paper presents a computational fluid dynamics study of isothermal laminar single-phase flow of water in a hollow helical pipe at various Reynolds numbers. The ranging of Reynolds numbers of fluid was from 703.2 to 1687.7. The three dimensional governing equations for mass and momentum have been solved. It was found that with increasing Reynolds number and creation of centrifugal forces, a high velocity and pressure region occurs between two tubes, at the outer side of the hollow helical pipe walls. Friction factor decreases as the tendency for turbulence increases.
In the present study, isothermal fluid flow in an annular helically-coiled tube has been modeled ... more In the present study, isothermal fluid flow in an annular helically-coiled tube has been modeled for different fluid flow regimes. The three-dimensional governing equations for mass and momentum have been solved, and k-ε turbulence model was used to model the turbulent flow regime. The fluid is water at Dean numbers ranging from 125 to 5600. It was found that centrifugal forces create a high velocity region at the outer side of the annular helicoidal pipe walls. The acceleration forces acting on the fluid flow create high pressure region near the outer pipe wall. The friction factor values for both laminar and turbulent regimes were compared with the experimental data and correlations from literature. Friction factor decreases as the tendency for turbulence increases. The best consistency between correlation and CFD model was observed for turbulent regime.
—In this research, vapor-liquid equilibrium behavior of Polypropylene oxide (PPO)/solvent and Pol... more —In this research, vapor-liquid equilibrium behavior of Polypropylene oxide (PPO)/solvent and Polypropylene glycol (PPG)/solvent were calculated using cubic equation of states. Eight models containing PRSV and SRK CEOS with four mixing rules namely vdW1, vdW2, Wong-Sandler (WS), and Zhong-Masuoka (ZM) were applied to calculations of bubble point pressure. For the better prediction, the adjustable binary interaction parameters existing in any mixing rule were optimized. The results of absolute average deviations (%AAD) between predicted and experimental bubble point pressure were calculated and presented. The PRSV+vdW2 model was the best predictive model with the highest accuracy (AAD=1.021%) between other models. Index Terms—Vapor-liquid equilibrium, polypropylene oxide solutions, cubic equations of state.
Abstract In this work, the ability of artificial neural networks (ANNs) to predict void fraction ... more Abstract In this work, the ability of artificial neural networks (ANNs) to predict void fraction of gas–liquid two–phase flow in horizontal and inclined pipes was investigated. For this purpose, an ANN model was designed and trained using a total of 301 experimental data points reported in the literature for inclination angles between –20° and +20°. Pipe inclination angle as well as superficial Reynolds number of gas (Re sg ) and liquid (Re sl ) were chosen as input parameters of different structures of multilayer perceptron (MLP) neural networks, while the corresponding void fraction was selected as their output parameter. A hyperbolic tangent sigmoid and a linear function were employed as transfer functions of hidden and output layers, respectively, and Levenberg–Marquardt back propagation algorithm was used to train the networks. By trial–and–error method, a three–layer network with 10 neurons in the hidden layer was achieved as optimal structure of the ANN which made it possible to predict the void fraction with a high accuracy. Mean absolute percent error (MAPE) of 1.81% and coefficient of determination (R 2 ) of 0.9976 for training data and MAPE of 1.52% and R 2 value of 0.9948 for testing data were obtained. Also for all data, MAPE of 1.95% and R 2 value of 0.9972 were calculated, and 96% data were within ±5% error band. In addition, the accuracy of the proposed ANN model was compared with the predictions from 17 void fraction correlations available in the literature for different flow patterns and horizontal and inclined flows. For all cases, the proposed ANN model gave better performance than all of the studied correlations. The results confirm the very good capability of the ANNs to predict the void fractions of gas–liquid flow in inclined pipes, regardless of flow pattern. Finally, by performing interpolation using the trained network, the void fraction values for some other conditions were predicted.
Abstract This paper presents the application of artificial neural network (ANN) in prediction of ... more Abstract This paper presents the application of artificial neural network (ANN) in prediction of water holdup of oil–water two-phase flow in a vertical and an inclined pipe (90°, 75°, 60°, and 45° from horizontal) without knowing the type of flow pattern. For this purpose, superficial velocity of water and oil and the inclination angles of the pipe were used as input parameters, while water holdup values of two-phase flow were used as output parameters in training and testing of the multi-layer, feed-forward, back-propagation neural networks. Experimental data (468 data points) were taken from literature and used for developing of the ANN model. The obtained results showed that the network predictions have very good agreement with the experimental water holdup data. The accuracy between the neural network predictions and experimental data was achieved with low average absolute percent error (AAPE) and high coefficient of determination ( R 2 ) for both training data (AAPE = 2.34% and R 2 = 0.999) and testing data (AAPE = 2.89% and R 2 = 0.997) sets. In addition, a comparison of the prediction results of the proposed ANN model with Mukherjee et al. (1981) correlation (AAPE = 9.83% and R 2 = 0.961) revealed that the correlation had more deviations.
Abstract Polyamide based composites were formed by melt blending of polyamide 6 (PA6) with a γ) -... more Abstract Polyamide based composites were formed by melt blending of polyamide 6 (PA6) with a γ) -alumina powder toughened with ethylene–octene copolymer grafted by maleic anhydride (EOC-g-MAH) and also without EOC in a corotating twin screw extruder. Mechanical properties, morphological structure and thermal stability of toughened PA6 (PA6-g-EOC) and PA6-g-EOC/alumina composites were investigated in this study.To study the effect of powder loading of γ-alumina on the mechanical properties of the composites such as tensile strength, modulus of elasticity, break point and impact strength, varied amounts of 5, 10 and 15 wt-% were deployed. The toughened PA6–γ-alumina composites, i.e. blended by EOC-g-MAH, revealed higher impact strength and more toughness compared to that of the PA6–γ-alumina composites without EOC-g-MAH. Morphology of the composites was investigated by scanning electron microscopy (SEM) from the as moulded specimens. Micrographs showed a fine dispersion of γ-alumina particles in polyamide matrix due to appropriate mixing. Furthermore, thermal stability and degradation characteristics of the toughened PA6–γ-alumina composites were measured by thermogravimetric analysis. The addition of γ-alumina into the polyamide matrix showed an increase in thermal resistance so that thermal stability was increased by a rise in the powder loading.
This paper presents the application of artificial neural network (ANN) in prediction of heat tran... more This paper presents the application of artificial neural network (ANN) in prediction of heat transfer coefficients (HTCs) of two-phase flow of air–water in a pipe in the horizontal and slightly upward inclined (2, 5, and 7 deg) positions. For this purpose, the superficial liquid and gas Reynolds numbers and the inclination of the pipe were used as input parameters, while the HTCs of two-phase flow were used as output parameters in training and testing of the multilayered, feedforward, backpropagation neural networks. In this present study, experimental data were taken from literature and then used for the ANN model. The superficial liquid and gas Reynolds numbers ranged from 740 to 26,100 and 560 to 47,600 for water and air, respectively. The mean deviations against experimental data were determined for the model. Results showed that the network predictions were in very good agreement with the experimental HTC data, whereas the correlation showed more deviations. Finally, results sh...
The cubic equation of state (CEoS) is a powerful method for calculation of the vapor-liquid equil... more The cubic equation of state (CEoS) is a powerful method for calculation of the vapor-liquid equilibrium (VLE) in polymer solutions. Using CEoS for both the vapor and liquid phases allows one to calculate the non-ideality of polymer solutions based on a single EoS approach. In this research, VLE calculations of polypropylene glycol (polypropylene oxide) [PPG(PPO)]/solvent solutions were carried out. In this approach, eight models containing Peng-Robinson-Stryjek-Vera (PRSV) and Soave-Redlich-Kwong (SRK) CEoS separately combined with four mixing rules, namely van der Waals one-fluid mixing rule with one adjustable parameter (vdW1), van der Waals one-fluid mixing rule with two adjustable parameters (vdW2), Wong-Sandler (WS), and Zhong-Masuoka (ZM) were applied to calculations of bubble point pressure. For a better correlation, the adjustable binary interaction parameters existing in any mixing rule were optimized. The results were very acceptable and satisfactory. The results of absolu...
Vapor-liquid equilibria (VLE) of polymer/solvent solutions is a topic of importance because of se... more Vapor-liquid equilibria (VLE) of polymer/solvent solutions is a topic of importance because of several areas of applications, including the designing of process equipment. Theoretical and thermodynamic models are reported in the literature for the estimation of VLE. However, up until now, the simultaneous representation of VLE and pressure-volume-temperature data is not satisfactory enough with respect to experimental accuracies. New models are therefore highly required. In the present study, a hybrid model including artificial neural networks (ANN) and genetic algorithm (GA) were applied to estimating the VLE data of seven binary polystyrene (PS)/solvents. The ranges of variables used were 283.15–343.15 K and 0.105–7.46 MPa. The VLE data of these systems were taken from the literature. The net was trained, validated and tested with randomly 65% (108 data points), 10% (17 data points) and the 25% (42 data points), respectively. The mean deviations from the experimental data were det...
This paper presents the application of artificial neural network (ANN) in prediction of heat tran... more This paper presents the application of artificial neural network (ANN) in prediction of heat transfer coefficients (HTCs) of two-phase flow of air-water in a pipe in the horizontal and slightly upward inclined (2, 5, and 7 deg) positions. For this purpose, the superficial liquid and gas Reynolds numbers and the inclination of the pipe were used as input parameters, while the HTCs of two-phase flow were used as output parameters in training and testing of the multilayered, feedforward, backpropagation neural networks. In this present study, experimental data were taken from literature and then used for the ANN model. The superficial liquid and gas Reynolds numbers ranged from 740 to 26,100 and 560 to 47,600 for water and air, respectively. The mean deviations against experimental data were determined for the model. Results showed that the network predictions were in very good agreement with the experimental HTC data, whereas the correlation showed more deviations. Finally, results sh...
In the present study, isothermal fluid flow in an annular helically-coiled tube has been modeled ... more In the present study, isothermal fluid flow in an annular helically-coiled tube has been modeled for different fluid flow regimes. The three-dimensional governing equations for mass and momentum have been solved, and k-e turbulence model was used to model the turbulent flow regime. The fluid is water at Dean numbers ranging from 125 to 5600. It was found that centrifugal forces create a high velocity region at the outer side of the annular helicoidal pipe walls. The acceleration forces acting on the fluid flow create high pressure region near the outer pipe wall. The friction factor values for both laminar and turbulent regimes were compared with the experimental data and correlations from literature. Friction factor decreases as the tendency for turbulence increases. The best consistency between correlation and CFD model was observed for turbulent regime.
Designing an artificial neural network for prediction of HTC of a refrigerant (R134a). 440 experi... more Designing an artificial neural network for prediction of HTC of a refrigerant (R134a). 440 experimental data points were applied for training the ANN model. The input parameters were inclination angle, mass flux, saturation temperature and mean vapor quality. The ANN was trained for the whole range of inclination angles regardless of the flow pattern. The results confirm the high ability of the ANN for predicting the HTC. g r a p h i c a l a b s t r a c t a b s t r a c t An artificial neural network model was developed to predict convective heat transfer coefficient (HTC) during condensation of R134a in an inclined smooth tube for the entire range of inclination angles at different saturation temperatures and regardless of flow pattern. The network was designed and trained using a total of 440 experimental data points collected from the literature. Inclination angle, mass flux, saturation temperature and mean vapor quality were used as input variables of multiple layer perceptron (MLP) neural network, while the corresponding HTC was selected as its output variable. By trial-and-error method, MLP network with 18 neurons in the hidden layer was achieved as optimal structure of the ANN which made it possible to predict the HTC with a high accuracy. Mean absolute percent error (MAPE) of 1.48% and correlation coefficient (R) of 0.997 for training data and MAPE of 1.94% and R value of 0.995 for testing data were obtained. Also, 95% and 99% all data were within ±5% and ±7% error band, respectively. MAPE of 1.61% and R value of 0.9963 were calculated for all data. These results confirm the high ability of the ANNs for predicting the HTC values for the entire range of inclination angles and independent of the flow pattern.
In this work, the ability of artificial neural networks (ANNs) to predict void fraction of gas–li... more In this work, the ability of artificial neural networks (ANNs) to predict void fraction of gas–liquid two– phase flow in horizontal and inclined pipes was investigated. For this purpose, an ANN model was designed and trained using a total of 301 experimental data points reported in the literature for inclination angles between –20 ° and + 20 °. Pipe inclination angle as well as superficial Reynolds number of gas (Re sg) and liquid (Re sl) were chosen as input parameters of different structures of multilayer perceptron (MLP) neural networks, while the corresponding void fraction was selected as their output parameter. A hyperbolic tangent sigmoid and a linear function were employed as transfer functions of hidden and output layers, respectively, and Levenberg–Marquardt back propagation algorithm was used to train the networks. By trial–and–error method, a three–layer network with 10 neurons in the hidden layer was achieved as optimal structure of the ANN which made it possible to predict the void fraction with a high accuracy. Mean absolute percent error (MAPE) of 1.81% and coefficient of determination (R 2) of 0.9976 for training data and MAPE of 1.52% and R 2 value of 0.9948 for testing data were obtained. Also for all data, MAPE of 1.95% and R 2 value of 0.9972 were calculated, and 96% data were within ±5% error band. In addition, the accuracy of the proposed ANN model was compared with the predictions from 17 void fraction correlations available in the literature for different flow patterns and horizontal and inclined flows. For all cases, the proposed ANN model gave better performance than all of the studied correlations. The results confirm the very good capability of the ANNs to predict the void fractions of gas–liquid flow in inclined pipes, regardless of flow pattern. Finally, by performing interpolation using the trained network, the void fraction values for some other conditions were predicted.
Although curved pipes are used in a wide range of applications, flow in curved pipes is relativel... more Although curved pipes are used in a wide range of applications, flow in curved pipes is relatively less well known than that in straight ducts. This paper presents a computational fluid dynamics study of isothermal laminar single-phase flow of water in a hollow helical pipe at various Reynolds numbers. The ranging of Reynolds numbers of fluid was from 703.2 to 1687.7. The three dimensional governing equations for mass and momentum have been solved. It was found that with increasing Reynolds number and creation of centrifugal forces, a high velocity and pressure region occurs between two tubes, at the outer side of the hollow helical pipe walls. Friction factor decreases as the tendency for turbulence increases.
In the present study, isothermal fluid flow in an annular helically-coiled tube has been modeled ... more In the present study, isothermal fluid flow in an annular helically-coiled tube has been modeled for different fluid flow regimes. The three-dimensional governing equations for mass and momentum have been solved, and k-ε turbulence model was used to model the turbulent flow regime. The fluid is water at Dean numbers ranging from 125 to 5600. It was found that centrifugal forces create a high velocity region at the outer side of the annular helicoidal pipe walls. The acceleration forces acting on the fluid flow create high pressure region near the outer pipe wall. The friction factor values for both laminar and turbulent regimes were compared with the experimental data and correlations from literature. Friction factor decreases as the tendency for turbulence increases. The best consistency between correlation and CFD model was observed for turbulent regime.
—In this research, vapor-liquid equilibrium behavior of Polypropylene oxide (PPO)/solvent and Pol... more —In this research, vapor-liquid equilibrium behavior of Polypropylene oxide (PPO)/solvent and Polypropylene glycol (PPG)/solvent were calculated using cubic equation of states. Eight models containing PRSV and SRK CEOS with four mixing rules namely vdW1, vdW2, Wong-Sandler (WS), and Zhong-Masuoka (ZM) were applied to calculations of bubble point pressure. For the better prediction, the adjustable binary interaction parameters existing in any mixing rule were optimized. The results of absolute average deviations (%AAD) between predicted and experimental bubble point pressure were calculated and presented. The PRSV+vdW2 model was the best predictive model with the highest accuracy (AAD=1.021%) between other models. Index Terms—Vapor-liquid equilibrium, polypropylene oxide solutions, cubic equations of state.
Uploads
Papers by Ebrahim Ahmadloo