Detecting Non-Uniform structures in Oil-in-Water Bubbly Flow Experiments

Acknowledgements This work was supported in part by the National Natural Science Foundation of China, under Grant 62373278, 52378254 and 41704131, and in part by the National Natural Science Foundation of Tianjin, China, under Grant 21JCJQJC00130, and in part by the Taishan Industrial Experts Program.Peer reviewe

Detecting gas–liquid two-phase flow pattern determinism from experimental signals with missing ordinal patterns

This work was supported by the National Natural Science Foundation of China (NNSFC) under Grant No. 41704131.Peer reviewedPostprintPublisher PD

Convolutional Neural Networks and Feature Fusion for Flow Pattern Identification of the Subsea Jumper

The gas–liquid two-phase flow patterns of subsea jumpers are identified in this work using a multi-sensor information fusion technique, simultaneously collecting vibration signals and electrical capacitance tomography of stratified flow, slug flow, annular flow, and bubbly flow. The samples are then processed to obtain the data set. Additionally, the samples are trained and learned using the convolutional neural network (CNN) and feature fusion model, which are built based on experimental data. Finally, the four kinds of flow pattern samples are identified. The overall identification accuracy of the model is 95.3% for four patterns of gas–liquid two-phase flow in the jumper. Through the research of flow profile identification, the disadvantages of single sensor testing angle and incomplete information are dramatically improved, which has a great significance on the subsea jumper’s operation safety.publishedVersio

Pipe fractional flow theory : principles and applications

The contribution of this research is a simple, analytical mathematical modeling framework that connects multiphase pipe flow phenomena and satisfactorily reproduces key multiphase pipe flow experimental findings and field observations, from older classic data to modern ones. The proposed unified formulation presents, for the first time, a reliably accurate analytical solution for averaged (1D) multiphase pipe flow over a wide range of applications. The two new fundamental insights provided by this research are that: (a) macroscopic single-phase pipe flow fluid mechanics concepts can be generalized to multiphase pipe flow, and (b): viewing and analyzing multiphase pipe flow in general terms of averaged relative flow (or fractional flow) can lead to a unified understanding of its resultant (global) behavior. The first insight stems from our finding that the universal relationship that exists between pressure and velocity in single-phase flow can also be found equivalently between pressure and relative velocity in multiphase flow. This eliminates the need for a-priori flow pattern determination in calculating multiphase flow pressure gradients. The second insight signifies that, in general, averaged multiphase flow problems can be sufficiently modeled by knowing only the averaged volume fractions. This proves that flow patterns are merely the visual, spatial manifestations of the in-situ velocity and volume fraction distributions (the quantities that govern the transport processes of the flow), which are neatly captured in the averaged sense as different fractional flow paths in our proposed fractional flow graphs. Due to their simplicity, these new insights provide for a deeper understanding of flow phenomena and a broader capability to produce quantitative answers in response to what-if questions. Since these insights do not draw from any precedent in the prior literature, a science-oriented, comprehensive validation of our core analytical principles was performed. Model validation was performed against a diverse range of vapor-liquid, liquid-liquid, fluid-solid and vapor-liquid-liquid applications (over 74,000 experimental measurements from over 110 different labs and over 6,000 field measurements). Additionally, our analytical theory was benchmarked against other modeling methods and current industry codes with identical (unbiased), named published data. The validation and benchmarking results affirm the central finding of this research – that simple, suitably-averaged analytical models can yield an improved understanding and significantly better accuracy than that obtained with extremely complex, tunable models. It is proven that the numerous, continuously interacting (local) flow microphysics effects in a multiphase flow can be (implicitly) accounted for by just a few properly validated (global) closure models that capture their net (resultant) behavior. In essence, it is the claim of this research that there is an underlying simplicity and connectedness in this subject if looking at the resultant macroscopic (averaged) behaviors of the flow. The observed coherencies of the macroscopic, self-organizing physical structures that define the subject are equivalently present in the macroscopic mathematical descriptions of these systems, i.e., the flow-pattern-implicit, averaged-equations mixture models that describe the collective behavior of the flowing mixture.Chemical Engineerin

Time Irreversibility from Time Series for Analyzing Oil-in-Water Flow Transition

We first experimentally collect conductance fluctuation signals of oil-in-water two-phase flow in a vertical pipe. Then we detect the flow pattern asymmetry character from the collected signals with multidimensional time irreversibility and multiscale time irreversibility index. Moreover, we propose a novel criterion, that is, AMSI (average of multiscale time irreversibility), to quantitatively investigate the oil-in-water two-phase flow pattern dynamics. The results show that AMSI is sensitive to the flow pattern evolution that can be used to predict the flow pattern transition and bubble coalescence

Classification of flow regimes using a neural network and a non-invasive ultrasonic sensor in an S-shaped pipeline-riser system

A method for classifying flow regimes is proposed that employs a neural network with inputs of extracted features from Doppler ultrasonic signals of flows using either the Discrete Wavelet Transform (DWT) or the Power Spectral Density (PSD). The flow regimes are classified into four types: annular, churn, slug, and bubbly flow regimes. The neural network used in this work is a feedforward network with 20 hidden neurons. The network comprises four output neurons, each of which corresponds to the target vector's element number. 13 and 40 inputs are used for features extracted from PSD and DWT respectively. Experimental data were collected from an industrial-scale multiphase flow facility. Using the PSD features, the neural network classifier misclassified 3 out of 31 test datasets in the classification and gave 90.3% accuracy, while only one dataset was misclassified with the DWT features, yielding an accuracy of 95.8%, thus showing the superiority of the DWT in feature extraction of flow regime classification. The approach demonstrates the applicability of a neural network and DWT for flow regime classification in industrial applications using a clamp-on Doppler ultrasonic sensor. The scheme has significant advantages over other techniques as only a non-radioactive and non-intrusive sensor is used. To the best of our knowledge, this is the first known successful attempt for the classification of liquid-gas flow regimes in an S-shape riser system using an ultrasonic sensor, PSD-DWTs features, and a neural network

ANALYSIS OF THRESHOLDING TECHNIQUES FOR TWO-PHASE IMAGE

This report presents the result of a study the behaviour of two-phase gas-liquid flow and two-phase liquid-wax waxy crude oil by using several thresholding techniques. The behaviour of gas-liquid is based on liquid film thickness and liquid holdup for gas-liquid flow and behaviour of liquid-wax is based on wax volume fraction. Several thresholding techniques are applied on the images which are captured by Electrical Capacitance Tomography (ECT). The results at the end of this report show which thresholding techniques are capable of yielding good threshold’s images and which methods can be used for the two-phase images in ECT applications

Experimental investigations of two-phase flow measurement using ultrasonic sensors

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

Hydrodynamic of a co-current gas liquid upflow in a moving packed bed reactor with porous catalysts

In this study, the hydrodynamic, i.e. flow regime identification, line average liquid holdup, and the internal liquid holdup of a co-current moving packed bed reactor were studied. For the sake of hydrodynamics study, moving bed reactor is investigated as two-phase upflow packed bed reactor. Scaled down configuration was used to simulate of the moving bed reactors utilized in the industrial process. First, line average liquid holdup is measured with different geometrical configurations covering empty, dry, wet, and packed column under flowrates operation conditions. A new methodology has been developed to determine the line average liquid holdup for a porous catalyst. Second, flow regime is identified by variation of superficial gas velocity at constant liquid superficial velocity. The experiments were carried out in an 11 - inch inner diameter Plexiglas column using the air-water system, at superficial gas velocities in the range of 0.6 to 7.7 cm/s and at a constant liquid superficial velocity of 0.017 cm/s. Gamma ray densitometry (GRD) technique was used to obtain the line average liquid holdup and to identify the flow regime at different axial and radial positions along the column. The obtained results showed that the flow regimes are bubble flow and pulse flow regimes with a transition flow in-between under the operation conditions used. The result showed that the liquid holdup decreased as the superficial gas velocity increased. It was also found that the liquid holdup radial distribution was not uniform. These kinds of information are essential to improve the performance of the reactor --Abstract, page iv