Comparison of Void Fraction Correlations for Different Flow Patterns in Upward Vertical Two-Phase Flow

Godbole, P.; Tang, Clement C.; Ghajar, Afshin J.

doi:10.1080/01457632.2011.548285

Cited by 88 publications

(30 citation statements)

References 44 publications

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“…The larger velocity difference results in a small variation of gas-liquid phase interface and hence weakens the influence of bubbles on the liquid flowing. allowing validations with aforementioned works and current studies [18] [24], [29], [30].…”

Section: Void Fraction Estimation Based On One Dp Signalsupporting

confidence: 76%

Void Fraction Measurement of Gas-Liquid Two-Phase Flow Based on Empirical Mode Decomposition and Artificial Neural Networks

Wang

Sefiane

Duursma

et al. 2018

Heat Transfer Engineering

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Phone Number: 0 (+44) 131 650 4868, Fax Number: 0 (+44) 131 650 6551 2 ABSTRACT A new void fraction estimation method for gas-liquid two-phase flow combining two differential pressure (DP) signals acquired from a single Venturi tube and based on Empirical Mode Decomposition (EMD) and Artificial Neural Networks (ANN) was experimentally investigated. In order to study gas-liquid distribution in horizontal pipes, two DP signals from the top and bottom sections of the Venturi tube are acquired and EMD is adopted to extract stable and fluctuating components of the DP signals. Experimental data revealed that fluctuating index increases nearly linearly with increasing void fraction when void fraction is less than 0.4. When void fraction is larger than 0.4, this near-linearity ceases. A combination of ANN method and the fluctuating index of DP signals is developed to estimate void fraction. Experimental results show that void fraction based on DP signal from top section of Venturi tube is overestimated because of clustering of bubbles and the scarcity of liquid information when gas-liquid mixture velocity is low. Void fraction is underestimated when the mixture velocity is high. A high gas-liquid slip ratio results in void fraction underestimation. Void fraction prediction performance is satisfactory when void fraction is less than 0.4 and fluctuating index of DP signal less than 1.2. ll f ll kk p            (1)

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Section: Void Fraction Estimation Based On One Dp Signalsupporting

confidence: 76%

Void Fraction Measurement of Gas-Liquid Two-Phase Flow Based on Empirical Mode Decomposition and Artificial Neural Networks

Wang

Sefiane

Duursma

et al. 2018

Heat Transfer Engineering

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“…The selection of the inputs for the ANN was made by analyzing the most influential forces on the behavior of the multiphase mixture flow, as proposed by Shaikh and Al-Dahhan [4].Based on the extensive literature review, the following forces have been found to affect the multiphase flow [5,11,12,13,14,15]:…”

Section: Selecting the Inputsmentioning

confidence: 99%

Development of a Neural Network for the Identification of Multiphase Flow Patterns

Inoue¹,

Carvalho²,

Estevam³

et al. 2013

Power and Energy / 807: Intelligent Systems and Control / 808: Technology for Education and Learning

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This work is concerned with the development of an artificial neural network (ANN), capable of classifying two-phase flow patterns, such as discrete bubbles, stratified, slug-flow, intermittent and annular regimes. Experimental operating data from the literature and the physical properties of the fluids were used to define and calculate dimensionless numbers. These numbers constitute the inputs of the neural model. They successfully describe the flow because they account for the competing forces occurring within the multiphase fluid. The training procedure was performed using a Levenberg-Marquardt algorithm. The methodology used to find the best network architecture is described in detail. All the flow regimes were accurately classified presenting only a small deviation. The final goal is to develop an automatic classification tool for multiphase flow patterns aimed at laboratories and field applications.

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“…To cater to the needs of these diverse applications, a large amount of work has been devoted to the study of fluid flow and heat transfer, pressure drop mechanisms in micro and minichannels. Specifically, researchers have concentrated on the prediction of flow patterns, heat transfer characteristics and instability [1][2][3][4][5] in two phase flows in microchannels. The literature survey reveals that most of the work in the area of microchannels is concentrated in the range of 100 µm -4mm using refrigerants and a relatively small number of studies have been performed with water as the working fluid.…”

Section: Introductionmentioning

confidence: 99%

Two Phase Flow Boiling Pressure Drop in Small Channels

2015

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Two-phase pressure drop, flow instability and boiling regime identifications are an essential step in the design of a variety of plant in the power and process industries. Despite the wide spread applications, significant experimental data on flow boiling in small channel is not available. In the present work, experimental set-up was designed for studying boiling flows in 19 mm ID tube and identifies flow regimes at various mass and heat flux conditions by varying inlet fluid temperature. The steady-state pressure data was collected and flow regimes identified using pressure drop and pressure fluctuations inside the channel.

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Comparison of Void Fraction Correlations for Different Flow Patterns in Upward Vertical Two-Phase Flow

Cited by 88 publications

References 44 publications

Void Fraction Measurement of Gas-Liquid Two-Phase Flow Based on Empirical Mode Decomposition and Artificial Neural Networks

Void Fraction Measurement of Gas-Liquid Two-Phase Flow Based on Empirical Mode Decomposition and Artificial Neural Networks

Development of a Neural Network for the Identification of Multiphase Flow Patterns

Two Phase Flow Boiling Pressure Drop in Small Channels

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