International Journal of Multiphase Flow最新文献

The numerical simulation of space launchers combustion chambers is a topic of increasing interest, as it may help the development of safer and more efficient designs. Understanding fuel injection is a particularly severe challenge. The liquid oxygen is injected by a round orifice surrounded by an annular gaseous stream of fuel, leading in subcritical conditions to a two-phase assisted atomization process. The result of this process is a very dense and polydisperse two-phase flow, which strongly influences the behavior of the chamber. Experimental investigation of this flow is difficult due to the axisymmetric geometry and the dense characteristic of the spray. Neither RANS nor Large Eddy Simulation (LES) possess reliable models able to reproduce the smallest scales of atomization, one of the reasons being the lack of relevant experimental data. Therefore, this work aims to provide detailed information on the atomization process using Direct Numerical Simulation. This paper presents a Direct Numerical Simulation (DNS) of a coaxial liquid–gas assisted atomization in the typical fiber regime encountered in cryogenic injectors. This study aims to better understand the evolution of liquid topology and extract relevant information that may help develop larger-scale models. The flow was first analyzed in terms of topology statistical data, using a dedicated detection and classification algorithm that could characterize the individual liquid structures. These include the central liquid core, the ligament created during primary atomization, and the spherical droplet obtained at the end of the atomization process. Subsequently, a more global statistical topology indicator was investigated, namely the interface area density distribution. This quantity is used in larger-scale RANS or LES models to predict the smallest scales of atomization. Therefore, understanding its behavior in a realistic case is of utmost importance. The interface area density distribution was correlated to the global jet behavior and the liquid topology data obtained by the detection algorithm. The results showed, in particular, a strong correlation between the initial increase of liquid–gas interface area density with the generation of ligaments and between the continuous decrease of the interface area density during droplet formation and stabilization.

The nucleate pool boiling plays an important role in thermal and chemical engineering applications. Analyzing bubble dynamics at nucleation site is crucial to improve the understanding of boiling heat transfer mechanism. Quantitative extraction of bubble parameters from high-speed visualized images is a labor-intensitive and time-consuming task making it necessary for automatically detect single bubble growth and measure boiling characteristic parameters.

In the present work, we proposed a deep learning based self-adaptive statistical algorithm for extraction of bubble behavior parameters quickly and automatically from numerous high-speed visualization images looking from the side view of a boiling chamber. A dataset was constructed for training and performance evaluation based on experimental data of saline solution pool boiling. The StarDist and U-Net convolutional neural network were combined in the algorithm so that more exact segmentation of the bubbles can be identified. Based on the segmentation results, a post-processing program was developed to extract the sequential variation of bubbles during consecutive cycles at nucleation sites. The dynamic characteristic parameters that affect heat transfer, such as nucleation density, bubble departure diameter, departure frequency, and wait time under different heat flux were obtained quantitatively. The comparison of automatic extraction algorithm and manual processing proves the reliability and superiority of our method. This work indicates that the proposed method has great potential to be widely applied as an efficient and universal tool for processing different types of bubble shadowgraph images.

This study investigates homogeneous flow in a bubble column up to $50\%$ gas holdup. For low to medium gas holdup below $\sim 20\%$, the good performance of an established baseline model is confirmed. In this range, the mixture pressure gradient is decisive in determining the relative velocity, resulting in good predictions without considering swarm effects. However, beyond a gas holdup of $\sim 20\%$, a swarm corrector to the drag force becomes necessary, for which several proposals from the literature are evaluated. In addition, the lift force influences the shape of the gas fraction profile depending on the bubble size, which has a significant impact on the liquid flow inside the column. For wall-peaked profiles, the liquid flow remains moderate, while center-peaked profiles strongly boost the liquid velocity. Finally, several mechanisms proposed in the literature for inducing unstable flow based on the lift force, bubble-induced turbulence or flooding are investigated. Of these only the first gave qualitative agreement with the observed gas holdup.

Solid particle-encapsulated droplets have significant applications in biochemistry, advanced materials, and inertial confinement fusion (ICF) experiments. However, there is a problem of encapsulating two solid cores in a single droplet during the preparation of single-core droplets, which reduces the utilization efficiency. In this study, an effective microfluidic approach for continuous splitting of solid-in-water-in-oil droplets encapsulating double solid cores is developed. Visualization experiments are conducted to analyze the movements of solid cores and evolution of liquid–liquid interface during the splitting. The results show that the squeezing stage during the splitting process is shortened due to the presence of solid cores. The splitting mechanisms were also revealed by analyzing the interaction forces between the solid cores and aqueous phase. The force analysis of the aqueous phase showed that sum of squeezing and shear force could overcome the interfacial tension, ensuring the successful splitting of the double-core droplets. The force analysis of the solid cores revealed that the motion of the core could be divided into three typical stages: deceleration, hitting and separation. The combined effect of the aqueous phase, channel wall, and interfacial forces ensured the stable separation of the two solid cores. The length distribution of the daughter droplets exhibited excellent monodispersity. The microfluidic method proposed in this work would effectively improve the controlled preparation efficiency of solid-in-water-in-oil droplets.

In this paper, the phase-field multiphase lattice Boltzmann method is employed to simulate droplet breakup in a T-junction under different outlet pressures. Three behaviors of droplet breakup: non-breakup (flow pattern I), breakup with tunnels (flow pattern II), and breakup with permanent obstruction (flow pattern Ⅲ) are identified. The evolution of morphological characteristics of droplet breakup is quantitatively characterized, based on which the asymmetric splitting mechanisms and the influencing factors are clarified. Additionally, the factors influencing the droplet splitting volume ratio (V_II/V_I) are elucidated. The results indicate that there is a non-linear relationship between the V_II/V_I and the flow rate ratio. Moreover, the curve depicting the final V_II/V_I versus the initial droplet length exhibits a V-shape and has a minimum value. A conclusion is drawn that the Capillary number mainly influences flow pattern II, with the final V_II/V_I decreasing as the Capillary number increases. Additionally, for flow pattern III, the final V_II/V_I increases linearly with rising droplet size at low viscosity ratios, whereas it decreases linearly at high viscosity ratios. The growing outlet pressure difference enlarges the flow difference between the two branches, leading to an increase in the final V_II/V_I.

This study aims to understand the comprehensive behavior of the gas–liquid flow and dissolution–evolution mass transfer. A quasi-static closed-tank experiment was designed to measure the static mass-transfer coefficients of the dissolution and evolution processes using the diffusion equation. After a detailed uncertainty analysis, a dynamic ventilated-pipe experiment with different-sized orifice plates was designed to illustrate the relationship between the hydrodynamic parameters, physical structure, and gas–liquid mass-transfer characteristics. The results showed that, as the static pressure and liquid-level height increase, both the dissolution and evolution coefficients exhibit increasing trends. However, when the physical condition reaches the initial state after pressurization and depressurization, the gas absorbed by the solution cannot completely evolve from the solution; that is, the dissolution rate is always greater than or equal to the evolution rate. For the equal-diameter pipe, as the gas flow rate increases, the concentration increment decreases slightly after reaching the peak, owing to the reduction in mass-transfer time caused by the increase in liquid flow rate. In particular, the maximal dissolved concentration, an increment of 210.9 %, occurred in the double large-orifice plate with the ventilated condition, far exceeding the maximum value in the quasi-static process. Moreover, the concentration under the layout of two small-orifice plates decreases slightly, and the larger gas content enables the solution to have more gas nuclei, making it easier to induce the gas evolution. The current study provides guidance for the gas–liquid-mixture transportation and improvement of the dissolved efficiency.

A numerical method is developed to consider the effects of fluid force and structural deformation on the flow field. The accuracy of the method is verified by experiments. The cavity evolution process are studied, and the characteristics of the velocity and vortex in the calculated flow field region are analyzed. The motion process of the dynamic wall of the cavity around the vehicle is simulated, and the phenomena of vortex distribution in the wake of the vehicle, instability in the wake of the cavity, and multistage closure of the cavity wall are obtained. It is revealed that the closing of the cavity will cause the velocity of the flow field inside the cavity to be reduced, and the return jet generated by the closing of the cavity will hit the vehicle and cause its attitude to be deflected. The peak velocity of the cavity wall appears at the position of cavity closure and the position of head cavity generation. The vortex scale around the vehicle gradually increases from the head of the body towards the top of the water surface. The research in this paper provides reference for the design of water entry vehicle.

Pulsed discharge in the vicinity of a multi-phase interface, where a discontinuity of physical properties exists, can be a joint problem of both electro- and thermo-physics. This study shows a comprehensive analysis of electric breakdown across an air-water interface and its successive multi-physical effects. The scenario is constructed via a pair of pin electrodes positioned on both sides of the interface, and the transient discharge is analyzed using high-speed backlight photography synchronized with electrical and optical diagnostics. It is observed that the corona/streamer develops from either side of the pin electrode. Electrostatic instability causes the interface to fluctuate and a water column to form above the interface. By increasing the applied voltage, discharge evolves from “dielectric barrier” mode (pin to interface) to “through breakdown” mode (pin to pin). Once the conductive channel bridges two electrodes, electric power of ∼40 kW peak and deposited energy of 100 mJ will be injected into the channel and promote the “streamer-spark” transition, resulting in a crown-like splash (100 m s^-1) near the interface and cavity formation. As the quenching of diffused plasmas, the over-expanded splash (5 mm in diameter) would be re-compressed by the ambient air. Particularly, the shrinkage of the thin water film of the splash can reach a 20 mm jet near the axis and develop Rayleigh-Taylor instability, along with the formation of micro-jets eruption during the convergent collision. More sophisticated interactions will appear at higher repetitive frequency (>100 Hz), where the perturbation caused by one pulse will influence the next, namely the “memory” effect. Furthermore, periodic loading on the interface effectively changes the cavity characteristics, showing an attractive prospect in fluid control applications.

The intricate process of bubble growth dynamics involves a broad spectrum of physical phenomena from microscale mechanics of bubble formation to macroscale interplay between bubbles and surrounding thermo-hydrodynamics. Traditional bubble dynamics models including atomistic approaches and continuum-based methods segment the bubble dynamics into distinct scale-specific models. To bridge the gap between microscale stochastic fluid models and continuum-based fluid models for bubble dynamics, we develop a composite neural operator model to unify the analysis of nonlinear bubble dynamics across microscale and macroscale regimes by integrating a many-body dissipative particle dynamics (mDPD) model with a continuum-based Rayleigh–Plesset (RP) model through a novel neural network architecture, which consists of a deep operator network for learning the mean behavior of bubble growth subject to pressure variations and a long short-term memory network for learning the statistical features of correlated fluctuations in microscale bubble dynamics. Training and testing data are generated by conducting mDPD and RP simulations for nonlinear bubble dynamics with initial bubble radii ranging from 0.1 to 1.5 micrometers. The results show that the trained composite neural operator model can accurately predict bubble dynamics across scales, with a 99% predictive accuracy for the time evolution of the bubble radius under varying external pressure while containing correct size-dependent stochastic fluctuations in microscale bubble growth dynamics. The composite neural operator is the first deep learning surrogate for multiscale bubble growth dynamics that can capture correct stochastic fluctuations in microscopic fluid phenomena, which sets a new direction for future research in multiscale fluid dynamics modeling.

A coupled volume-of-fluid (VOF) and discrete element model (DEM) is developed and used to study the dispersion of particles in an evaporating pinned sessile droplet on a heated substrate. Fully resolved simulations of evaporating droplets are performed to study the effects of substrate temperature and the Marangoni stresses to study the fluid flow and temperature distribution within the droplet. The fluid flow inside the evaporating droplets is used to predict the behavior of particles, studying the effect of relative particle density and the aforementioned effects on the particle dispersion within the droplet. This study shows that the presence of Marangoni stresses significantly affects the flow and temperature distribution inside the droplet, which, in turn, influences the dispersion of particles in the droplet. The fluid velocity induced by the Marangoni stresses is nearly two orders of magnitude larger than the velocity generated by capillary flow as a result of evaporation, promoting a strong convective mixing within the droplet, while working to equilibrate the temperature distribution at the interface. In the absence of Marangoni stresses, the dispersion of particles is governed by the competing effects of adsorption by the downward-moving interface as a result of evaporation, and particle sedimentation under the influence of gravity. However, both these effects become less dominant in the presence of a flow induced by the Marangoni stresses, causing the particles to initially move toward the apex of the droplet along the interface and, subsequently, toward a stagnation point on the interface.