Void fraction correlations analysis and their influence on heat transfer of helical double-pipe vertical evaporator

Parrales, A.; Colorado, D.; Huicochea, A.; Díaz, Juan Pablo; Hernández, J.A.

doi:10.1016/j.apenergy.2014.04.036

Cited by 12 publications

(4 citation statements)

References 42 publications

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“…The evaporator is one of the main components of the AHTER in addition to the absorber, the generator, and the condenser [20]. (Figure 1) shows the closed thermodynamic cycle of AHTER, in which the heat source with a low energy level was transferred to the generator and the evaporator, while in the absorber, a high-level heat source was generated.…”

Section: Methodsmentioning

confidence: 99%

Heat Transfer Coefficients Analysis in a Helical Double-Pipe Evaporator: Nusselt Number Correlations through Artificial Neural Networks

Parrales

Hernández-Pérez

Flores

et al. 2019

Entropy

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In this study, two empirical correlations of the Nusselt number, based on two artificial neural networks (ANN), were developed to determine the heat transfer coefficients for each section of a vertical helical double-pipe evaporator with water as the working fluid. Each ANN was obtained using an experimental database of 1109 values obtained from an evaporator coupled to an absorption heat transformer with energy recycling. The Nusselt number in the annular section was estimated based on the modified Wilson plot method solved by an ANN. This model included the Reynolds and Prandtl numbers as input variables and three neurons in their hidden layer. The Nusselt number in the inner section was estimated based on the Rohsenow equation, solved by an ANN. This ANN model included the numbers of the Prandtl and Jackob liquids as input variables and one neuron in their hidden layer. The coefficients of determination were R 2 > 0.99 for both models. Both ANN models satisfied the dimensionless condition of the Nusselt number. The Levenberg-Marquardt algorithm was chosen to determine the optimum values of the weights and biases. The transfer functions used for the learning process were the hyperbolic tangent sigmoid in the hidden layer and the linear function in the output layer. The Nusselt numbers, determined by the ANNs, proved adequate to predict the values of the heat transfer coefficients of a vertical helical double-pipe evaporator that considered biphasic flow with an accuracy of ±0.2 for the annular Nusselt and ±4 for the inner Nusselt.Generally, in the case of heat exchangers with helical pipelines, it is known that the flow behavior has a greater complexity compared to exchangers that include straight pipes [2]. Due to the generation of vortices, induced by the interaction between the viscous forces and the centrifugal force, a disturbance in the flow is generated, which increases the rate of the heat transfer and the friction losses-a behavior referred to as secondary flow [3].If a helical exchanger transports a biphasic flow, specifically gas-liquid, the complexity is even higher due to the sudden changes in the flow pattern. These phenomena are mainly caused by the sliding between the phases, the orientation of the pipe, and the void fraction, among other additional variables and the relationships between them [4]. Therefore, to know and understand these phenomena, it is necessary to estimate the values of the heat transfer coefficient in the helical exchangers that contain biphasic flows, and hence any method that leads to their calculation is considered useful.One of the methods commonly used to estimate heat transfer coefficients is the Wilson plot method, proposed by Wilson in 1915 and subsequently modified in 1957 [5]. This traditional method quantifies the heat flow for each section by the exchanger through the measurement of the global temperature between the two fluids involved and their rate of heat transfer [6]. One of the most significant advantages of this technique is that it avoids the complex dire...

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Section: Methodsmentioning

confidence: 99%

Heat Transfer Coefficients Analysis in a Helical Double-Pipe Evaporator: Nusselt Number Correlations through Artificial Neural Networks

Parrales

Hernández-Pérez

Flores

et al. 2019

Entropy

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“…Therefore, many experimental studies have been made in order to determine the value of the void fraction in a two-phase flow. For that reason, several correlations have been established to predict the behavior of the void fraction [4].…”

Section: Correlationsmentioning

confidence: 99%

“…In other studies, Parrales et al [4] studied 50 different void fraction correlations to find the most appropriate ones for determining the heat transfer coefficient on evaporators, and determined that the most reliable ones were based on a drift flux formulation. On a separate research, Godbole et al [5] analyzed more than 50 correlations for the prediction of the void fraction for vertical upward flow; the main results showed that the drift flux-based correlations had the best performance, and suggested that future correlations should be developed based on this formulation.…”

Section: Introductionmentioning

confidence: 99%

Comparison of 63 different void fraction correlations for different flow patterns, pipe inclinations, and liquid viscosities

et al. 2020

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Gas-liquid two-phase flow is commonly encountered in the oil industry, especially in transport pipelines. Thus, the correct prediction of operational parameters such as void fraction or pressure drop is necessary for the development of efficient processes and facility design. Nowadays, there are different studies focused on predicting the void fraction parameter with empirical correlations. This study was aimed at analyzing and comparing 63 different void fraction correlations in order to determine if there was a unique correlation capable of accurately predicting the void fraction for the different operational conditions of flow patterns, pipe inclinations, and liquid viscosity. For this reason, a database of 11,895 experimental points was used to compare the results against the different empirical correlations available in the literature and determine the best one using statistical analyses based on indicators, such as relative error, absolute average percent error, among others. It must be mentioned that the database was strictly filtered and divided depending on the flow pattern (i.e., slug, churn, bubbly, and annular), the different pipe inclinations (i.e., vertical, horizontal and inclined), and the liquid viscosities. The results showed that there was not a unique unified correlation to determine the void fraction accurately for the different operational conditions and fluid properties. However, the best correlations for each specific set of flow patterns, pipe inclinations, and intervals of liquid viscosities were determined according to the statistical indicators, the general assumptions of each correlation, and information reported in the literature. Moreover, it was possible to analyze and provide some recommendations for the patterns evaluated, taking into account that some specific cases require further study, as there was no correlation capable of providing reliable results for the prediction of void fraction. Keywords Empirical correlations • Flow pattern • Statistical indicators • Two-phase flow • Void fraction • Viscosity List of symbols D Pipe diameter (m) L Pipe length (m) Viscosity (Pa s) Density (kg/m 3) Void fraction (-) E 1 Average percent error (%) E 2 Absolute average percent error (%) E 3 Percent standard deviation (%) E 4 Average error (-) E 5 Absolute average error (-) E 6 Standard deviation (-) R 2 Determination coefficient (-) g Gravity acceleration (m/s 2) Inclination angle (°) u sG Gas superficial velocity (m/s)

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“…Energy is essential in economic development and social progress for all countries [1]. The large consumption of the fossil fuel is not only unsustainable but also pollutes the environment.…”

Section: Introductionmentioning

confidence: 99%

Optimum performance of a double absorption heat transformer

Wang

et al. 2016

Energy Conversion and Management

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Void fraction correlations analysis and their influence on heat transfer of helical double-pipe vertical evaporator

Cited by 12 publications

References 42 publications

Heat Transfer Coefficients Analysis in a Helical Double-Pipe Evaporator: Nusselt Number Correlations through Artificial Neural Networks

Heat Transfer Coefficients Analysis in a Helical Double-Pipe Evaporator: Nusselt Number Correlations through Artificial Neural Networks

Comparison of 63 different void fraction correlations for different flow patterns, pipe inclinations, and liquid viscosities

Optimum performance of a double absorption heat transformer

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