The use of accelerometer signals for early recognition of severe slugging is investigated in a pipeline-riser system conveying an air–water two-phase flow, where six accelerometers are installed from the bottom to the top of the riser. Twelve different environmental conditions are produced by changing water and gas superficial velocities, of which three conditions are stable states and the other conditions are related to severe slugging. For online recognition, simple parameters using statistics and linear prediction coefficients are employed to extract useful features. Binary classification to recognize stable flow and severe slugging is performed using a support vector machine and a neural network. In multiclass classification, the neural network is adopted to identify four flow patterns of stable state, two types of severe slugging, and an irregular transition state between severe slugging and dual-frequency severe slugging. The performance is compared and analyzed according to the signal length for three cases of sensor location: six accelerometers, one accelerometer at the riser base, and one accelerometer at the top of the riser.

Formation of a dispersed oil—water flow pattern is a common occurrence in flow lines and pipelines. The capability of predicting the size of droplets, as well as the distribution of dispersed phase volume fraction is of utmost importance for proper design of such systems. The present study aims at modelling dispersed water in oil flows in a horizontal pipe by employing a multi-fluid Eulerian approach along with the population balance model. To this end, momentum and continuity equations are solved for oil and water phases, and the coupling between the phases is achieved by considering the drag, lift, turbulent dispersion, and virtual mass forces. Turbulent effects are modelled by employing the standard k-ε model. Furthermore, a population balance model, based on the method of class, along with the breakup and coalescence kernels is adopted for modelling the droplet size distribution. The obtained numerical results are compared to the experimental data in literature for either the in situ Sauter mean diameter or water volume fraction. A comparison among the obtained numerical results and the published experimental data shows a reasonable agreement.