Void signal analysis and gas-liquid two-phase flow regime determination by a statistical pattern recognition method.

Sekoguchi, Kotohiko; Inoue, Katsuhiro; Imasaki, Takuo

doi:10.1299/jsme1987.30.1266

Cited by 21 publications

(2 citation statements)

References 4 publications

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“…Lin and Hanraty (1987) used pressure measurements to detect the intermittent flow The model was developed and tested on experimental data and a wide range of variables. Sekoguchi et al (1987) applied a statistical method and the mean void fraction to identify flow patterns. Regarding non-classical techniques of signal analysis, Giona et al (1994) and Seleghim & Hervieu (1998) used, respectively, fractal techniques and joint time-frequency analysis in the characterization of transitions between horizontal two-phase flow regimes.…”

Section: Introductionmentioning

confidence: 99%

On the application of self-organizing neural networks in gas-liquid and gas-solid flow regime identification

Barbosa¹,

Crivelaro²,

Seleghim³

2010

J. Braz. Soc. Mech. Sci. & Eng.

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Section: Introductionmentioning

confidence: 99%

On the application of self-organizing neural networks in gas-liquid and gas-solid flow regime identification

Barbosa¹,

Crivelaro²,

Seleghim³

2010

J. Braz. Soc. Mech. Sci. & Eng.

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“…As for experimental methods, a number of methods have been proposed (see e.g. [1,2]). Most of the methods are contact methods, namely requiring contact with the flow of interest.…”

Section: Introductionmentioning

confidence: 99%

Flow regime classification in air–magnetic fluid two-phase flow

Kuwahara

Vuyst

Yamaguchi

2008

J. Phys.: Condens. Matter

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A new experimental/numerical technique of classification of flow regimes (flow patterns) in air-magnetic fluid two-phase flow is proposed in the present paper. The proposed technique utilizes the electromagnetic induction to obtain time-series signals of the electromotive force, allowing us to make a non-contact measurement. Firstly, an experiment is carried out to obtain the time-series signals in a vertical upward air-magnetic fluid two-phase flow. The signals obtained are first treated using two kinds of wavelet transforms. The data sets treated are then used as input vectors for an artificial neural network (ANN) with supervised training. In the present study, flow regimes are classified into bubbly, slug, churn and annular flows, which are generally the main flow regimes. To validate the flow regimes, a visualization experiment is also performed with a glycerin solution that has roughly the same physical properties, i.e., kinetic viscosity and surface tension, as a magnetic fluid used in the present study. The flow regimes from the visualization are used as targets in an ANN and also used in the estimation of the accuracy of the present method. As a result, ANNs using radial basis functions are shown to be the most appropriate for the present classification of flow regimes, leading to small classification errors.

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Neural network based objective flow regime identification in air‐water two phase flow

et al. 1994

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Void signal analysis and gas-liquid two-phase flow regime determination by a statistical pattern recognition method.

Cited by 21 publications

References 4 publications

On the application of self-organizing neural networks in gas-liquid and gas-solid flow regime identification

On the application of self-organizing neural networks in gas-liquid and gas-solid flow regime identification

Flow regime classification in air–magnetic fluid two-phase flow

Neural network based objective flow regime identification in air‐water two phase flow

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