School of Water, Energy and Environment (SWEE)

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Browsing School of Water, Energy and Environment (SWEE) by Author "Abbagoni, Baba Musa"

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    Experimental investigations of two-phase flow measurement using ultrasonic sensors
    (Cranfield University, 2016-05) Abbagoni, Baba Musa; Yeung, Hoi
    This thesis presents the investigations conducted in the use of ultrasonic technology to measure two-phase flow in both horizontal and vertical pipe flows which is important for the petroleum industry. However, there are still key challenges to measure parameters of the multiphase flow accurately. Four methods of ultrasonic technologies were explored. The Hilbert-Huang transform (HHT) was first applied to the ultrasound signals of air-water flow on horizontal flow for measurement of the parameters of the two- phase slug flow. The use of the HHT technique is sensitive enough to detect the hydrodynamics of the slug flow. The results of the experiments are compared with correlations in the literature and are in good agreement. Next, experimental data of air-water two-phase flow under slug, elongated bubble, stratified-wavy and stratified flow regimes were used to develop an objective flow regime classification of two-phase flow using the ultrasonic Doppler sensor and artificial neural network (ANN). The classifications using the power spectral density (PSD) and discrete wavelet transform (DWT) features have accuracies of 87% and 95.6% respectively. This is considerably more promising as it uses non-invasive and non-radioactive sensors. Moreover, ultrasonic pulse wave transducers with centre frequencies of 1MHz and 7.5MHz were used to measure two-phase flow both in horizontal and vertical flow pipes. The liquid level measurement was compared with the conductivity probes technique and agreed qualitatively. However, in the vertical with a gas volume fraction (GVF) higher than 20%, the ultrasound signals were attenuated. Furthermore, gas-liquid and oil-water two-phase flow rates in a vertical upward flow were measured using a combination of an ultrasound Doppler sensor and gamma densitometer. The results showed that the flow gas and liquid flow rates measured are within ±10% for low void fraction tests, water-cut measurements are within ±10%, densities within ±5%, and void fractions within ±10%. These findings are good results for a relatively fast flowing multiphase flow.
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    Non-invasive classification of gas–liquid two-phase horizontal flow regimes using an ultrasonic Doppler sensor and a neural network
    (IOP Publishing, 2016-06-23) Abbagoni, Baba Musa; Yeung, Hoi
    The identification of flow pattern is a key issue in multiphase flow which is encountered in the petrochemical industry. It is difficult to identify the gas–liquid flow regimes objectively with the gas–liquid two-phase flow. This paper presents the feasibility of a clamp-on instrument for an objective flow regime classification of two-phase flow using an ultrasonic Doppler sensor and an artificial neural network, which records and processes the ultrasonic signals reflected from the two-phase flow. Experimental data is obtained on a horizontal test rig with a total pipe length of 21 m 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) are used to develop the classification model. The classifier requires features as an input which is representative of the signals. Ultrasound signal features are extracted by applying both power spectral density (PSD) and discrete wavelet transform (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 the output of a first level networks feature as an input feature. The addition of the two network models provided a combined neural network model which has achieved a higher accuracy than single neural network models. Classification accuracies are 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 3 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 the success of a clamp-on ultrasound sensor for flow regime classification that would be possible in industry practice. It is considerably more promising than other techniques as it uses a non-invasive and non-radioactive sensor.
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    Non-invasive measurement of oil-water two-phase flow in vertical pipe using ultrasonic Doppler sensor and gamma ray densitometer
    (Elsevier, 2021-10-28) Abbagoni, Baba Musa; Yeung, Hoi; Lao, Liyun
    Oil–water two-phase flow experiments were conducted in a vertical pipe to study the liquid–liquid flow measurement using a non-invasive ultrasound Doppler flow sensor and a gamma-ray densitometer. Tap water and a dyed mineral oil are used as test the fluids. A novel Doppler effect strategy is used to estimate the mixture flow velocity in a vertical pipe based on flow velocity measured by the ultrasound sensor and the shear flow velocity profile model. Drift-flux flow model was used in conjunction phase fractions to predict the superficial velocities of oil and water. The results indicate that the proposed method estimated oil-continuous flow and water-continuous flow have average relative errors of 5.2% and 4.5%; and superficial phase flow velocities of oil and of water have average relative errors of 4.5% and 5.9% respectively. These results demonstrate the potential for using ultrasonic Doppler sensor combined with gamma densitometer for oil–water measurement.