International Journal of Multiphase Flow最新文献

Traditional Chinese medicine is a pivotal industry in China, and the technology for manufacturing high-quality pills is of great interest to numerous sectors. Among various methodologies, the liquid drip method has gained significant attention from the pharmaceutical industry due to its advantages such as high drug content uniformity, low cost, ease of operation, high production efficiency, and extensive adjustment range. However, the occurrence of undesirable satellite droplets during the drip production process remains a persistent issue. These satellite droplets are challenging to collect and their prolonged presence can disrupt the normal functioning of the system. Previous studies have identified two primary modes of droplet formation: dripping mode and jetting mode. Given that droplet formation in dripping mode is more stable and satellite droplets can be effectively suppressed, studying the transition between dripping and jetting modes and improving the uniformity of the generated droplets become crucial. Achieving this transition can be accomplished through both active and passive methods. In view of the limitations of the active methods in actual productions, we explored a passive method of adding a drainage device, which can effectively inhibit satellite droplets.

In this experimental and numerical simulation investigation, we successfully induced the dripping-to-jetting transition by incorporating a drainage device into the dropper. Additionally, we examined how modifications to the dropper's structure influenced droplet formation. Results indicated that increasing the length and diameter of the drainage device resulted in higher critical Weber numbers required for the dripping-to-jetting transition. Contrarily, the effect of the Kapitza number on the critical Weber numbers exhibited an opposite trend. The critical Kapitza number without complex droplets was also discussed.

A two-way coupled Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM) calculation was performed to numerically investigate the gas and powder flow characteristics within a packed bed. The analysis and discussion focused on the effects of gas phase velocity, particle shape, and size on the flow behavior of the powders. The results revealed that the average absolute velocity of the powder increased with an increase in the inlet gas flow rate. Conversely, the average dimensionless velocity exhibited the opposite trend. Particle shape significantly impacted the flowability of the powders. A critical value of sphericity equal to 0.96 was observed. For powders with sphericity less than 0.96, the average velocity fluctuated around a progressively decreasing value over time, and the powders tended to accumulate in the inlet region. In contrast, powders with a sphericity greater than 0.96 exhibited average velocities fluctuating around a constant value throughout the simulation. Additionally, a smaller number of these particles remained within the fluid domain, indicating a higher flow rate through the packed bed and ultimately exiting the fluid domain. Regarding the influence of particle size, the flowability reached a minimum value at a radius of 0.14 mm, with the average powder velocity mirroring this trend. Powders with a radius exceeding 0.14 mm demonstrated increasing flowability and average velocity. The observed impact on flowability was likely due to a combination of mechanisms. The findings presented in this paper offer valuable insights into the dynamics of powders within a packed bed. This knowledge can be instrumental in the design and analysis of packed bed reactors.

The vapor-liquid interface can be characterized as a circle or sphere affected by surface tension. However, linearization or planarization processing is commonly employed to simplify the reconstruction of the vapor-liquid interface within a grid cell, introducing errors in numerical simulations of two-phase flow. Therefore, this paper proposes a straightforward and accurate Horizontal Refined Piecewise Curve Interface Reconstruction (HOPCIR) algorithm to reconstruct the curve vapor-liquid interface for the two-dimensional structured grid. In this method, a standardized circle segment is employed to represent the vapor-liquid interface segment within each mixed grid cell, uniquely defined by its radius and center. The radius of this circle is determined by the curvature of the interface segment, and an iterative procedure is employed to calculate the circle's center using the known fluid volume fraction and initial interface obtained by linear reconstruction. Another integral aspect of the HOPCIR algorithm involves solving the reconstructed distance function to enhance the accuracy of curve reconstruction. Comparative analysis with the coupled volume-of-fluid and level set (VOSET) method demonstrates the superior performance of the HOPCIR algorithm in reconstructing vapor-liquid interfaces and solving the reconstructed distance function, interface curvature, surface tension, and fluid volume fraction.

Oscillating droplets rebounding completely from non-superhydrophobic surfaces (polydimethylsiloxane, PDMS) were experimentally studied and theoretically interpreted. The new experimental finding is that, with increasing the droplet impact Weber numbers ($We$), the recovery coefficient of droplet velocity, which is defined as the ratio of the rebounding velocity over the impact velocity, has an overall trend of decrease but in a fluctuating manner. Physically, a sufficiently large droplet freely falling under gravity has an inevitable oscillation, which makes the impacting droplet shape slightly deviate from being spherical and in turn affects the interaction between the droplet and the surface. The fluctuating recovery coefficient is the result of the periodically varying phase of droplet oscillation with increasing $We$, and increasing the droplet viscosity can suppress the droplet oscillation and then the fluctuation amplitude of the recovery coefficient. A theoretical model of oscillating droplet rebound is proposed and well fits the present experiments over a wide range of $We$.

The occurrence of a steam generator tube rupture (SGTR) accident in a lead-bismuth cooled fast reactor results in the formation of steam bubbles in the liquid lead-bismuth eutectic (LBE). This may degrade heat transfer and power transients in the reactor core due to the migration and accumulation of steam bubbles. To investigate the dynamics of steam bubbles flowing in liquid LBE, it is essential to develop an accurate model for the bubble drag coefficient. In this paper, a three-dimensional numerical model is first established to simulate the injection of high-pressure steam bubbles into a high-temperature LBE molten pool. The model is based on the CLSVOF method. By analyzing the trajectory, velocity, and diameter of bubbles, and combining them with the force equilibrium equation for bubbles, the values of the drag coefficient for bubbles are determined. On this basis, the suitability of current empirical drag models for bubble migration in LBE is evaluated. Finally, the optimal drag coefficient model is selected and further improved. Results reveal that the prediction error of the optimized model for the bubble drag coefficient in liquid LBE is within ±15 %.

The accurate reconstruction of immiscible fluid–fluid interfaces from the volume fraction field is a critical component of geometric Volume of Fluid methods. A common strategy is the Piecewise Linear Interface Calculation (PLIC), which fits a plane in each mixed-phase computational cell. However, recent work goes beyond PLIC by using two planes or even a paraboloid. To select such planes or paraboloids, complex optimization algorithms as well as carefully crafted heuristics are necessary. Yet, the potential exists for a well-trained machine learning model to efficiently provide broadly applicable solutions to the interface reconstruction problem at lower costs. In this work, the viability of a machine learning approach is demonstrated in the context of a single plane reconstruction. A feed-forward deep neural network is used to predict the normal vector of a PLIC plane given volume fraction and phasic barycenter data in a $3\times 3\times 3$ stencil. The PLIC plane is then translated in its cell to ensure exact volume conservation. Our proposed neural network PLIC reconstruction (PLIC-Net) is equivariant to reflections about the Cartesian planes. Training data is analytically generated with $O\left(10^6\right)$ randomized paraboloid surfaces, which allows for the sampling a broad range of interface shapes. PLIC-Net is tested in multiphase flow simulations where it is compared to standard LVIRA and ELVIRA reconstruction algorithms, and the impact of training data statistics on PLIC-Net’s performance is also explored. It is found that PLIC-Net greatly limits the formation of spurious planes and generates cleaner numerical break-up of the interface. Additionally, the computational cost of PLIC-Net is lower than that of LVIRA and ELVIRA. These results establish that machine learning is a viable approach to Volume of Fluid interface reconstruction and is superior to current reconstruction algorithms for some cases.

Solid–fluid force models are essential to efficiently model multiple industrial apparatuses such as fluidized beds, spouted beds, and slurry transport. They are generally built using strong hypotheses (e.g. fully developed flow and no relative motion between particles) that affect their accuracy. We study the effect of these hypotheses on particle dynamics using the sedimentation of a pair of particles. We develop new induced drag, lift and torque models for pairs of particles based on an artificial neural network (ANN) regression. The fluid force model covers a range of Reynolds numbers of 0.1 to 100 and particle centroid distance of up to 9 particle diameters. The ANN model uses 3475 computational fluid dynamics (CFD) simulation results as the training data set. Using this fluid force model, we develop a reduced-order model (ROM), which includes the virtual mass force, the Meshchersky force, the history force, the lubrication force, and the Magnus force. Using the results of a resolved computational fluid dynamics coupled with a discrete element method (CFD-DEM) model as a reference, we analyze the discrepancies between the ROM and CFD-DEM results for a series of sedimentation cases that cover particle Archimedes number from 20 to 2930 and particle to fluid density ratio of 1.5 to 1000. The errors primarily stem from particle history interactions that are not accounted for by the fully developed flow hypothesis. The importance of this effect on the dynamic of two particles is isolated and it is shown that it is more pronounced in cases with a lower particle-to-fluid density ratio (such as solid–liquid cases). This work underscores the need for more research on these effects to increase the precision of solid–fluid force models for small particle-to-fluid density ratios (1.5).

Shock-driven variable density mixing has been frequently explored through the single-phase Richtmyer–Meshkov instability. Here, such mixing is considered when driven by a multiphase component, the Shock-Driven Multiphase Instability (SDMI). The simple case of a solid particle seeded gas in a cylindrical region surrounded by clean gas is studied. It has been previously shown that the particle-phase can lag behind the gas, diminishing vorticity deposition. In this letter we present theoretical analysis of the vorticity deposition, and a new model predicting the circulation deposition for an SDMI as a function of particle relaxation distance and hydrodynamic mixing strength. The theory is founded on a simplified vorticity equation, advection and multiphase source terms, using simple drag models to predict the particle dynamics, and scaling the results using existing circulation models for the Richtmyer–Meshkov instability in the small particle limit. The model is compared to new high-fidelity experimental data, and previous experiments and simulations, finding good agreement. This model provides the first theoretical prediction of mixing suppression in the SDMI.

In a continuous fluid, the presence of a velocity gradient perpendicular to the flow creates shear stress and shear rate between adjacent layers. The fluid's viscosity can be constant, depending only on temperature (Newtonian fluid), or vary with shear rate (non-Newtonian fluid). However, the viscosity characteristics of shear flows in discrete media, such as vibrated granular materials, remain insufficiently understood. This study experimentally investigated shear flows in vibrated granular media, exploring the relationship between shear stress, shear rate, and the impact of vibration conditions and particle number on granular viscosity. The findings indicate that the viscosity of sheared granular material transitions between dilatant and pseudoplastic non-Newtonian states with increasing vibration strength, shifts from pseudoplastic non-Newtonian fluid to Newtonian fluid with increasing vibration frequency, and remains consistently pseudoplastic non-Newtonian with increasing particle number. Two continuous non-Newtonian fluid models were utilized for comparison with our experimental results. Additionally, ascending curves of granular viscosity against granular temperature reveal gas-like flow characteristics in the sheared granular material, albeit with an abnormal descending viscosity–temperature relationship. These are attributed to volume expansion and oblique collisions in the vibrated granular medium. This study uncovers distinct viscosity properties in a discrete medium under shear flows, markedly different from those in continuous fluids, and highlights potential new applications for granular materials.

This study aims to provide valuable insights into the impact of porous structures on particle dynamics in non-Brownian, non-colloidal suspension flows at very low Reynolds numbers. Two experimental approaches, Particle Image Velocimetry (PIV) with refractive index matching and Optical Flow Tracking Velocimetry (OFTV) were employed to analyze very dilute suspensions over various porous media models. The study considered three different porous structures with permeabilities ranging from 0.7 to 0.9 and three different thicknesses ranging from 0.2 cm to 0.5 cm, while the suspension bulk volume fraction was maintained at 3 %. In the PIV analysis, we observed that decreasing the porous permeability resulted in the maximum velocity location within the free flow region moving closer towards the interface between the flow and the porous media. We further quantified the effect of the porous structure on the suspension by characterizing interface properties, such as dimensionless slip velocity, shear rate, and slip length. These interface properties were found to be influenced by both the thickness and permeability of the porous media. Next, we analyzed particle migration due to the presence of porous structures using OFTV for very dilute suspensions of 1 %, 2 %, and 3 %, considering a porous medium with known physical properties and thickness. The study revealed two local concentration maxima: one within the free flow region on top of the rod arrays used to create the porous structure and a second along the rods' centerline inside the porous media model.