Fausto Arinos Barbuto

An Experimental Characterization of Horizontal Gas-Liquid Slug Flow

The present work is meant to broaden the knowledge on the fluid mechanics of the two-phase slug flows in horizontal pipes by means of an experimental approach. To accomplish this goal, an experimental facility at the LACIT-UTFPR labs consisting of a 25.8 mm ID, 9-m long transparent pipeline was used. A pair of 12×12-node wire-mesh sensors based on electrical capacitance was used to identify the void fraction in each node of the mesh. Bubble velocities, unit cell frequencies, void fractions and the characteristic lengths of this kind of flow were then obtained after a proper signal processing of the experimental data. To verify the measurements, a methodology to evaluate every measurement done in this work was proposed. Due to the intermittent nature of those flows, their characteristic parameters were identified as probability distributions, and approximated by probability density functions (PDF) such as the normal or log-normal ones. Correlations depending upon the inlet superficial velocities of both liquid and gas phases were fitted for the average values and standard deviation of each parameter. Finally, those correlations were compared to the experimental data, with the aim of accurately predicting the aforementioned parameters as functions of the inlet flow variables, so that those parameters can be used in the development of theoretical models for horizontal gas-liquid slug flow.Copyright

Two-Phase Slug Flow Characterization Using Artificial Neural Networks

Gas-liquid two-phase flows are present in nature and in different industrial activities alike, such as the chemical, petroleum, and nuclear industries. In this type of flow, the liquid and gas phases assume different spatial configurations inside the pipe, called flow patterns. The mathematical modeling of slug flow comprises from simple steady-state models to more complex models for transient regimes. Those models require closure relationships, e.g., empirical correlations and statistical distributions of characteristic flow parameters. In this paper, a model based on artificial neural networks (ANNs) for predicting the two-phase slug flow behavior is proposed. With this ANN model, the parameters that characterize the flow are extracted from the time series of void fractions obtained experimentally. The variables of interest are superficial velocities of the fluids, liquid slug and gas bubble lengths, and the bubble translational velocity and their standard deviations. The knowledge and understanding of those parameters will improve the characterization of the intermittent slug flows and will also provide information on the development of physical models that describe this phenomenon, such as the unit cell models, the drift flux model and the slug tracking model. In general, the estimation models based on ANNs showed good results compared with reference values obtained experimentally. The results show that the estimation models present a mean square error below 2%. The methodology presented here, combining experimentally obtained void fraction time series and ANN, is an appropriated method to infer flow parameters and thus to support slug flow characterization.

A Two-Fluid Model for Slug Flow Initiation Based on a Lagrangian Scheme

Two-phase slug flow is present in many industrial processes, such as the exploitation and transportation of hydrocarbon mixtures from oil wells. This kind of flow is characterized by two distinct structures which repeat intermittently: a liquid slug with a large amount of momentum followed by a compressible gas bubble. In recent decades, a few models for simulating such complex flows were developed, as the eulerian two-fluid model and drift flux, and the lagrangian slug tracking. The aim of this work is to present a detailed study on the numerical implementation of the hybrid model proposed by Fabien Renault and Nydal which is able to track down waves that arise in the gas-liquid interface and possible slugs generated by them. This model was developed from the two-fluid model equations in which the motion generated by the dynamic pressure of the gas on the slugs is decoupled from the slow movement of the liquid below the gas. The movement of the bubbles in the liquid is then modeled similarly to shallow-water equations. The solution of the equation set is achieved in two steps. The first step provides the pressure field and the gas flow through the numerical solution of the equations for the gas, using the finite difference method. The second step solves the adapted shallow-water equations analytically. The model was coded in object-oriented Intel Visual Fortran95. Simulations to analyze the ability of the code to generate slugs for some pairs of water-air superficial velocities at atmospheric pressure were carried out. The results, as the distribution of the slug length, frequency and average values were compared to experimental results reported in the literature.© 2014 ASME

Analysis of Numerical Simulation of Gas-Liquid Slug Flow Using Slug Tracking Model

The distribution of the interfaces in gas-liquid two-phase flows in pipes can assume several shapes. Amongst those shapes, the slug flow pattern stands out as the most common one and occurs quite often in oil and gas production due to the flow rates and geometries used. This pattern is characterized by the succession of the so-called unit cells, that is, a flow structure composed of an aerated liquid slug and an elongated bubble surrounded by a liquid film. Due to its complexity, the study and understanding of this pattern’s behaviour becomes very important. The main methodologies used to describe slug flows are the steady-state one-dimensional models, based on the slug unit concept, and the transient approach, which takes the flow intermittence into account. The slug tracking model is one such transient approach, which considers slugs and elongated bubbles as separated bodies and analyzes the evolution along the flow and the interaction between those bodies. Whenever this model is numerically implemented, its initial conditions are important parameters that affect the results. The goal of this article is to study the influence of the initial conditions on slug flow simulation using the slug tracking model. A computer program written in Fortran95 using a slug tracking model to provide the characteristic parameters of slug flows such as the bubble and slug lengths and void fraction in the bubble region was built and used. The results were compared to experimental data and showed the important role the initial conditions play on the computational simulation of slug flow.Copyright

Analysis of Slug Frequency Correlations for Two-Phase Gas-Liquid Horizontal Slug Flow

Multiphase flows in pipelines show several flow patterns depending on the industrial applications where they appear. In oil and gas production, typical flow rates, geometries and the physical properties of the phases make slug flow to be the most common of all patterns. This kind of flow is characterized by an intermittent succession of an aerated liquid slug region and a long, turbulent gas bubble surrounded by a liquid film. Due to its complexity, slug flow modelling has been a challenge to many researchers over the last four decades. Presently, steady-state one-dimensional models based on the unit cell concept and more accurate physical representations based on either two-fluid or slug tracking models embedding transient flow capabilities are available. These models require closure relationships for predicting flow parameters. In the present work, a literature review on frequency correlations is presented. An analysis of the performance of those correlations with experimental data for horizontal slug flows was carried out and its results are presented.Copyright