Proper selection and application of interfacial friction factor correlations has a significant impact on prediction of key flow characteristics in gas–liquid two-phase flows. In this study, experimental investigation of gas–liquid flow in a vertical pipeline with internal diameter of 0.060 m is presented. Air and oil (with viscosities ranging from 100–200 mPa s) were used as gas and liquid phases, respectively. Superficial velocities of air ranging from 22.37 to 59.06 m/s and oil ranging from 0.05 to 0.16 m/s were used as a test matrix during the experimental campaign. The influence of estimates obtained from nine interfacial friction factor models on the accuracy of predicting pressure gradient, film thickness and gas void fraction was investigated by utilising a two-fluid model. Results obtained indicate that at liquid viscosity of 100 mPa s, the interfacial friction factor correlation proposed by Belt et al. (2009) performed best for pressure gradient prediction while the Moeck (1970) correlation provided the best prediction of pressure gradient at the liquid viscosity of 200 mPa s. In general, these results indicate that the two-fluid model can accurately predict the flow characteristics for liquid viscosities used in this study when appropriate interfacial friction factor correlations are implemented.

The accurate prediction of the pressure distribution of highly viscous fluids in wellbores and pipelines is of great significance for heavy oil production and transportation. The flow behavior of high-viscosity fluids is quite different with respect to that of low-viscosity fluids. Currently, the performances of existing pressure-drop models seem to be relatively limited when they are applied to high-viscosity fluids. In this study, a gas-liquid two-phase flow experiment has been carried out using a 60 mm ID horizontal pipe with air and white oil. The experimental results indicate that viscosity exerts a significant influence on the liquid holdup and pressure drop. At the same gas and liquid volume, both the liquid holdup and pressure drop increase with an increase in the viscosity. Combining two existing models, a modified pressure drop method is developed, which is applicable to horizontal pipes for different viscosities and does not depend on the flow pattern. This new method displays a high accuracy in predicting the new experimental data presented here and other published data in literature.

Aiming at the two characteristics of premature convergence of particle swarm optimization that the particle velocity approaches 0 and particle swarm congregate, this paper learns from the annealing function of the simulated annealing algorithm and adaptively and dynamically adjusts inertia weights according to the velocity information of particles to avoid approaching 0 untimely. This paper uses the good uniformity of Anderson chaotic mapping and performs chaos perturbation to part of particles based on the information of variance of the population’s fitness to avoid the untimely aggregation of particle swarm. The numerical simulations of five test functions are performed and the results are compared with several swarm intelligence heuristic algorithms. The results shows that the modified algorithm can keep the population diversity well in the middle stage of the iterative process and it can improve the mean best of the algorithm and the success rate of search.