International Journal of Multiphase Flow最新文献

This study experimentally investigates the spatiotemporal evolution and associated local instabilities of a drop subjected to a weak shock wave. The front and side views of the drop are captured to understand its three-dimensional evolution and breakup. The interaction of shock causes the windward side of the drop to compress and generate a surface wave over it. Its temporal amplification is found to be governed by Kelvin–Helmholtz instability. The core of the deformed drop expands in a stream-wise direction, forming a Rayleigh–Taylor instability-driven bag structure. Consistent pressure gradients across the bag cause its continuous elongation until the pressure gradient overcomes the surface tension. This continuous elongation leads the sheet to undergo kinematic thinning, which causes the sheet to destabilize and nucleate the hole. This hole recedes and gathers liquid from upstream to thicken its interface, called the bag rim. The accelerating receding motion of the bag rim triggers Rayleigh–Taylor instability, and the corrugation that forms over it grows into ligaments and destabilizes to shed droplets through end pinching and ligament merging. Additionally, the accelerating rim undergoes radial expansion, with its further destabilization governed by the coupled effect of Rayleigh–Taylor and Rayleigh–Plateau instabilities, as well as the collision of the receding bag rim. This leads to the formation of corrugations, which grow into ligaments and further destabilize to shed drops via end pinching. Nonlinear effects dominate ligament dynamics and increase with the Weber number. The asymmetric ejection of the daughter drop from the rim causes it to evolve into a bag, undergoing tertiary breakup.

In this study, we investigate a novel type of static thermal instability that occurs in two-phase flow systems. The instability is theoretically and experimentally confirmed to occur for flow boiling micro-channel scenarios and flow condensing scenarios. The theory presented is independent of the evaporator geometry as well as flow boiling/condensing situations. A stability criteria is derived using a Reynolds Transport approach applied to the evaporator of a two-phase pumped loop (TPPL) system. The theory is then validated experimentally using a TPPL containing a parallel micro-channel evaporator. The instability is confirmed with pressure, temperature, and void fraction measurements acquired at the exit of the evaporator. The findings reveal that static thermal instabilities can arise when simultaneous heat addition and reduction in system saturation temperature occurs (or vice versa). The implications of the thermal instability result in dramatic changes in evaporator heat flux, as well as a flow transition from stratified laminar flow to vigorous turbulent flow with high void fractions. By identifying the additional instability mechanisms, this work contributes to enhancing system reliability and predictability of TPPL systems.

Heavy and light particles are commonly found in many natural phenomena and industrial processes, such as suspensions of bubbles, dust, and droplets in incompressible turbulent flows. Based on a recent machine learning approach using a diffusion model that successfully generated single tracer trajectories in three-dimensional turbulence and passed most statistical benchmarks across time scales, we extend this model to include heavy and light particles. Given the particle type – tracer, light, or heavy – the model can generate synthetic, realistic trajectories with correct fat-tail distributions for acceleration, anomalous power laws, and scale dependent local slope properties. This work paves the way for future exploration of the use of diffusion models to produce high-quality synthetic datasets for different flow configurations, potentially allowing interpolation between different setups and adaptation to new conditions.

The analysis of bubbly two-phase flows is challenging due to their turbulent nature and the need for intrusive phase-detection probes. However, accurately characterizing these flows is crucial for safely designing critical infrastructure such as dams and their appurtenant structures. The combination of dual-tip intrusive phase-detection probes with advanced signal processing algorithms enables the assessment of pseudo-instantaneous 1-D velocity time series; for which the limitations are not fully fathomed. In this investigation, we theoretically define four major sources of error, which we quantify using synthetically generated turbulent time series, coupled with the simulated response of a phase-detection probe. Based on the analysis of 10¹⁰ simulated bubble trajectories, our findings show that typical high-velocity flows in hydraulic structures hold up to 15% error in the mean velocity estimations and up to 35% error in the turbulence intensity estimations for the most critical conditions, typically occurring in the proximity of the wall. Based on thousands of simulations, our study provides a novel data-driven tool for the estimation of these baseline errors (bias and uncertainties) in real-word phase-detection probe measurements of bubbly flows (air concentrations $c<40\text{\%}$).

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.