Influence of the inlet cross-sectional shape on the performance of a multi-inlet gas cyclone

Research on Flow Patterns of Two-Phase Fracturing Fluid in Hydraulic Fracture Considering the Fluid Leak-off

In in situ-generated proppant fracturing technology without proppant injection, the distribution of the flow pattern of two-phase fracturing fluid in the fracture determines the concentration of proppant particles formed by phase change in different positions. Therefore, the study of a two-phase fracturing fluid flow pattern is of great significance to reveal the formation mechanisms of different flow patterns and guide the on-site implementation of the technology. This paper establishes a mathematical model for the two-phase fracturing fluids in fractures based on their physical properties and presents numerical experiments on the flow pattern of two-phase fracturing fluids under different conditions of injection displacement, interfacial tension, and phase change liquid (PCL) ratio. The results show that at lower injection displacements, such as 3 or 4 m3/min, it is easier to form striped shape distributions, and at higher injection displacements, such as 5 or 6 m3/min, it is easier to form droplet shape distributions. When the interfacial tension is low (15 mN/m), PCL shrinks less and is distributed in strips; when the interfacial tension is high (25, 35, and 45 mN/m), PCL shrinks more and mainly forms droplet-shaped distributions. PCL tends to form discrete droplet shape distributions at PCL volume fractions of 10, and 20%. At 30% volume fraction, PCL is distributed in strips, and at 40% volume fraction, PCL forms strips of a larger size. These findings reveal the changing pattern of two-phase fracturing fluid flow and enrich the theoretical system of in situ-generated proppant fracturing technology, which can provide theoretical support for the on-site implementation of this technology.

Effect of a new pattern of surface roughness on flow field and erosion rate of a cyclone

This paper evaluates the impact of a special design of non-uniform surface roughness on the operating parameters of a gas–solid cyclone numerically. The cyclone surface is divided into 32 separate rings with different roughness heights when the roughness height varies from 0.2 to 3 mm. The results show that the tangential velocity is reduced and the axial velocity is enhanced by increasing the height of non-uniform roughness. It is observed that the cyclone with smooth walls can collect 7 μm solid particles while 3.7 and 2.77 μm particles can be separated by one-inlet and two-inlet cyclones, respectively. For the cut-off size diameter of d 50, the one-inlet cyclone with smooth walls can collect the particles with a diameter of 1.24 µm, while the cyclone with R Nu = 0.2, 0.5, 1, 2, and 3 mm can collect 1.3, 1.6, 1.8, 0.83, and 1.88 µm particles, respectively. The results demonstrate that the erosion rate decreases with the roughness and this reduction is larger for the one-inlet cyclone.

Modeling of Two-Phase Flow Parameters of a Multi-Channel Cylindrical Cyclone

The variation in the two-phase flow parameters in a cylindrical body of new geometry and principle of operation are considered for a device for separating solids from air flow, solving the problem of numerical flow modeling. The aim of this research was to analyze the changes in the parameters of a multi-channel cylindrical cyclone in a mathematical model and to compare it with the results of the examined physical model. Studies on the numerical modeling of cyclones are reviewed, and models and equations for complex vortex flow description are applied. Differential equations were numerically solved by the finite volume method using the standard turbulence models of k–ε and RNG k–ε. Numerical modeling of the velocities, pressures, and volumes of both phases of the two-phase flow was performed. The simulation of the volume distribution of the second phase (glass particles) in the cyclone structure at flow rates of 10.9 m/s, 13.9 m/s, and 21.9 m/s was performed. The values obtained were compared with the physical model of the cyclone in question. The mean relative error was ±6.9%.