The use of an ultrasonic technique and neural networks for identification of the flow pattern and measurement of the gas volume fraction in multiphase flows

Figueiredo, María Marta; Gonçalves, J. L.; Nakashima, Atsushi; Fileti, Ana Maria Frattini; Carvalho, R.D.M.

doi:10.1016/j.expthermflusci.2015.08.010

Cited by 89 publications

(39 citation statements)

References 33 publications

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“…Other ultrasonic methods reported in the literature employed pulse echo ultrasound plus neural network or subjective methods of flow regimes indemnification (Jha et al 2013;Figueiredo et al 2016). The key strengths of the neural network based methods are fast classification and flexible procedure for finding good nonlinear solutions.…”

Section: Flow Regime Identificationmentioning

confidence: 99%

“…More importantly, neural network would offer a non-linear mapping between the ultrasound input signals and the predicted flow regimes. So the use of the ANN avoids the need for calibration of the multiphase flowmeter (Figueiredo et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…They found that the trained ANN models were able to estimate the values of the volumetric fraction and the interfacial area. Similarly, Figueiredo et al (2016) employed an ultrasonic methodology based on pulse wave ultrasound transducers which is operates on the principle of signal attenuation detection. The ultrasound signal attenuation was analysed and incorporated with artificial neural network for flow pattern detection and void fraction measurement.…”

Section: Introductionmentioning

confidence: 99%

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Non-invasive classification of gas–liquid two-phase horizontal flow regimes using an ultrasonic Doppler sensor and a neural network

Abbagoni¹,

Yeung²

2016

Meas. Sci. Technol.

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The identification of flow pattern is key issue in multiphase flow which encountered in the petrochemical industry. Gas-liquid two-phase flow is difficult to identify the gas-liquid flow regimes objectively. This paper presents a feasibility of a clamp-on instrument for objective flow regime classification of two-phase flow using an ultrasonic Doppler sensor and artificial neural network. It is on recording and processing of the ultrasonic signals reflected from the two-phase flow. Experimental data obtained on a horizontal test rig with total pipe length of 21 m long and 5.08 cm internal diameter carrying air-water two-phase flow under slug, elongated bubble, stratified-wavy and, stratified flow regimes. Multilayer Perceptron Neural Networks (MLPNNs) used for developing the classification model. The classifier requires features as input which is representative of the signals. Ultrasound signal features extracted by applying both power spectral density (PSD) and discrete wavelet transforms (DWT) methods to the flow signals. A classification scheme of "1-of-C coding method for classification" was adopted to classify features extracted into one of four flow regime categories. To improve the performance of the flow regime classifier network, a second level neural network was incorporated by using output of a first level networks features as input features. Addition of the two network models provided a combined neural network models which has achieved higher accuracy than single neural network models. Classification accuracies evaluated in the form of both the PSD and DWT features. The success rates of the two models are: (1) using PSD features, the classifier missed three datasets out of 24 test datasets of the classification and scored 87.5% accuracy. (2) With the DWT features, the network misclassified only one data point and it was able to classify the flow patterns up to 95.8% accuracy. This approach has demonstrated success of a clamp-on ultrasound sensor for flow regime classification and it would be possible in industry practice. It is considerably more promising than other techniques as it uses of non-invasive and nonradioactive sensor.

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Section: Flow Regime Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Non-invasive classification of gas–liquid two-phase horizontal flow regimes using an ultrasonic Doppler sensor and a neural network

Abbagoni¹,

Yeung²

2016

Meas. Sci. Technol.

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“…Description, explanation and measurement of two-phase flow are necessary and required in a variety of industrial applications such as chemical reaction, pipeline transportation and medical treatment [1][2][3]. Simultaneous flow of gas and liquid, which is termed as gas-liquid two-phase flow, is commonly encountered.…”

Section: Introductionmentioning

confidence: 99%

A Contactless Electromagnetic Coupling Resonance-Based Volume Fraction Detection Technique for Gas-Liquid Flow

Shi¹,

Sun²,

Wang³

et al. 2019

PIER M

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To obtain the volume fraction of a gas-liquid two-phase flow, a contactless electromagnetic coupling resonance based volume fraction detection (CECR-VFD) technique is proposed. By mathematical calculation and numerical simulation, it is found that the CECR-VFD method is a better alternative than the conventional electromagnetic induction based method. The distance between the excitation coil and receiving coil is firstly determined. Then the effect of the pipe length is investigated. Additionally, the relationship between the output voltage across the receiving coil and the volume fraction is studied for stratified flow and annular flow. Experiments have been carried out for validation, and the results indicate that the output voltage can be used to predict the volume fraction of a two-phase flow.

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“…Distinct from ordinary indirect flow-metering devices, no calibration is required preliminarily because of the capability of velocity distributions [2]. This feature enables the target of flow metering to be expanded widely such as for curved flows [3], non-Newtonian flows [4]- [6], and for multiphase flows [7]- [19]. In particular, the application to multiphase flow is challenging.…”

Section: Introductionmentioning

confidence: 99%

Ultrasound Flow-Monitoring and Flow-Metering of Air–Oil–Water Three-Layer Pipe Flows

et al. 2017

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The combined use of ultrasound pulse-echo intensity and Doppler shift frequency is examined as a means to measure strong unsteady three-phase pipe flows of a gas and two liquids. With air, oil, and water as components of the fluid media, particular attention is given to analyze ultrasound responses at the air-oil and oil-water interfaces. Reciprocating slugging is generated inside a 55-mm-diameter circular pipe, of which edges oscillate vertically at a controlled frequency. We use an ultrasound velocity profiler to obtain the 1-D cross-sectional distributions of the instantaneous flow velocity at the sampling rate of 60 Hz. All the measurements are realized by a single ultrasound transducer located outside the pipe. Measurement accuracy is validated using a high-speed camera coupled with particle image velocimetry that is synchronized with the profiler. The results demonstrate that the proposed technique works properly in sensing both interfaces as well as in-phase flow velocity distributions. In addition, multiphase volume flow rates for the constituents are obtained by velocity profile integration assuming vertical phase stratification in an approximation.INDEX TERMS Doppler method, flow metering, multiphase flow, particle image velocimetry, pipe flow, pipe line, ultrasound

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The use of an ultrasonic technique and neural networks for identification of the flow pattern and measurement of the gas volume fraction in multiphase flows

Cited by 89 publications

References 33 publications

Non-invasive classification of gas–liquid two-phase horizontal flow regimes using an ultrasonic Doppler sensor and a neural network

Non-invasive classification of gas–liquid two-phase horizontal flow regimes using an ultrasonic Doppler sensor and a neural network

A Contactless Electromagnetic Coupling Resonance-Based Volume Fraction Detection Technique for Gas-Liquid Flow

Ultrasound Flow-Monitoring and Flow-Metering of Air–Oil–Water Three-Layer Pipe Flows

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