Two-phase Flow Regimes Research Articles

Stability of mass transfer parameters in critical regimes of two-phase flows

Stability of fractional separation parameters has been revealed in separation practice. Such stability is surprising, because an immense number of particles of various sizes take part in a process with a large number of random factors. Analysis of this phenomenon from the standpoint of statistical approach has explained its existence. A key notion of a statistical system is entropy. It is shown that a mass system evolves into a state corresponding to the maximal entropy value. Whenever fluctuations disturb the equilibrium of a system, its entropy decreases, and non-equilibrium processes return the system into its extreme state. This fact explains the stability of separation parameters.

Experimental investigation on gas-liquid two-phase flow patterns and vibration characteristics of an inducer pump

Inducers typically enhance centrifugal pump performance in two-phase flow regimes by producing more uniform mixtures and increasing pressure before the impeller. Their impact is most pronounced under part-load conditions compared to overload situations. This study experimentally investigates air-water two-phase flow behavior within a pump inducer. Using high-speed photography and grayscale image processing, five distinct gas-liquid flow patterns were identified: bubble flow, strip bubble flow, agglomerated bubble flow, gas pocket flow, and segregated flow. The inducer's head and vibration characteristics were also measured. Results show that flow pattern transitions significantly affect performance degradation and vibration. Specifically, the head decreases as the liquid flow rate increases at a constant gas volume fraction (λ) and generally follows a downward trend as λ increases at a constant liquid flow rate. Bubble flow, representing minimal λ, has a negligible effect on performance. However, with higher λ, a sharp decline in head occurs within the agglomerated bubble flow range, followed by a gradual decrease during gas pocket flow under both optimal and overload conditions. In part-load conditions, the head decreases sharply during strip bubble and segregated flow. While bubble flow mitigates vibration fluctuations, increasing GVF leads to higher vibration amplitude, particularly in the range of 2–8 times the inducer's rotational frequency, due to flow pattern instability.

Gas liquid flow pattern prediction in horizontal and slightly inclined pipes: From mechanistic modelling to machine learning

This paper investigates the prediction of two-phase gas-liquid flow regimes in both horizontal and slightly inclined pipes. For this purpose, the mechanistic model of Taitel et al. (1976) and the machine learning approach have been adopted. First, the mechanistic model was implemented, tested and optimised by introducing factors in the transition equations to determine the configuration that gives the highest prediction accuracy for a specific two-phase system for which experimental data points are available. Second, several machine learning models are trained, tested and additionally validated. This is done by splitting the experimental data set corresponding to the pipe inclination range (−10∘ to 10∘) into training, test and validation sets. The best classifier achieved an accuracy of 95.5% after the test step and up to 98.9% after the validation step. Finally, the Taitel et al. model with the optimal configuration and the best machine learning classifier (XGB classifier) are used to generate the two-dimensional flow regime map.

Analysis of flow boiling incipience models in computational fluid dynamics

Recent advances in the computational fluid dynamics and heat transfer (CFD/HT) modeling of flow boiling have enabled a more detailed analysis and understanding of thermal management in relatively complex microsystems. Most advanced modeling approaches allow the transient analysis of two-phase flow and phase-change processes, providing the capability to post-process in detail the temperature, phase and pressure distributions in microscale applications with the use of moderate computational resources. Although these models have significantly evolved in recent years, there are still several idealizations and assumptions that have yet to be improved or better justified, the boiling incipience treatment being a commonly questioned topic. In the present investigation, a boiling incipience model is incorporated into an existing CFD/HT model for the analysis of flow boiling mechanisms, where the superheat temperature is derived from fundamental thermodynamic relations and compared to the original CFD/HT model that does not account for such effects. Results are compared using a non-trivial case of a silicon micro-cooling layer with variable density of pin fins that has been demonstrated to be an efficient thermal control device in high-power applications. The dielectric fluid HFE-7200 is used as the coolant in flow boiling conditions. The two-phase flow regimes and heat transfer results for both models are compared.

Influence of nanoparticle concentration on the flow regimes of crude oil – Nanosuspension in a microchannel

This paper presents the results of an investigation into the effect of silica nanoparticles on two-phase flow regimes in a Y-shaped microchannel. The following fluids were subjected to investigation: oil, water, and a water-based suspension containing SiO2 nanoparticles at a weight concentration of 1 ≤ φ ≤ 10 %. A systematic investigation of the flow regimes revealed the following: slug, parallel, and droplet regimes. The ranges of existence of the obtained flow regimes were determined and flow regime maps were constructed as a function of fluid flow rates and dimensionless numbers. The results of the study demonstrated that the slug length and frequency of slug formation are influenced by varying silica concentrations. Furthermore, the correlation between slug length and nanoparticle concentration remains consistent as the suspension flow rate increases. The dependence of the ratio of oil layer width to channel width for suspensions with varying nanoparticle concentrations was quantified. A mathematical simulation of two-phase flow of immiscible fluids was conducted. The results of the numerical simulation demonstrated the existence of the flow regimes that had been previously identified through experimentation. The aforementioned methodology may therefore be employed for the investigation of the two-phase immiscible flow of oil and nanosuspension.

Experimental and numerical study of flow boiling in a vertical upward narrow rectangular channel under low heat flux

This article experimentally and numerically investigates the flow boiling in a vertical upward narrow rectangular channel under low heat flux. VOF model for interface tracking and the phase-change Lee model are coupled to build a numerical model of a channel with a scale of 1200 mm × 250 mm × 2.75 mm. The temperature, velocity and phase distribution in the channel are obtained at different heights The bubble movement is analyzed and the two-phase flow regimes are classified. Furthermore, the local characteristic of flow boiling heat transfer along the channel and the effect of the bubble regimes on the heat transfer are studied. The results indicate that saturated nucleate boiling is the dominant boiling form under low heat flux.

Numerical Investigation on Two-Phase Flow-Electrochemical Reaction Coupling in PEM Water Electrolyzers Porous Transport Layer Under High Operating Pressures

Proton exchange membrane water electrolyzers (PEMWEs) operated at high pressures have advantages such as lower energy consumption, reduced noise, and absence of component slippage. They have a more robust potential for commercial applications than other electrolyzers. The transport characteristics of two-phase flow within the porous transport layer (PTL) and channels are not well understood, especially under high-pressure conditions. The gas bubbles generated in the catalyst layer (CL) pass through the PTL and reach the flow channel. However, oxygen evolution reaction in the anode CL may be limited if these bubbles block the pathway of liquid water, causing reactant starvation. The interaction between electrochemical reactions and gas bubbles thus directly impacts a PEMWE's efficiency. This study aims to investigate the coupling between the two-phase flow and electrochemical reactions under high-pressure conditions.A two-dimensional (2D) CFD model was developed to simulate the bubbly flow through a reconstructed PTL. The volume of fluid (VOF) method was employed to capture bubble growth, movement, and detachment, as illustrated in Figure 1. The coupling of bubble transport and electrochemical reactions was achieved through the application of a weighted averaging method. A cross-section of a stochastically reconstructed titanium felt model was generated as the computational domain; see Fig. 1(a). The computation domain consists of a liquid water channel, a PTL, and a simplified CL interface at the bottom. The CL's reaction rate is modeled as a function of local bubble coverage on the CL interface. Transient simulations of the two-phase flow of the PEMWE anode side were carried out for pressures ranging from 1 bar to 200 bar. The simulation results elucidate the flow characteristics of liquid water in the PTL under high-pressure conditions and quantitatively reveal the coupling between two-phase flow and electrochemical reactions. The behavior of the two-phase flow in PEMWE is found to be sensitive to operating pressure. As the operating pressure increases, the size of bubbles generated on the CL interface decreases, resulting in a higher permeability of liquid water than that under low pressures. When the flow rate of liquid water in the channel is sufficiently high, bubbles do not fully develop and are rapidly removed from the domain. This phenomenon significantly shifts the two-phase flow regime within the channels,. Furthermore, the activation overpotential is substantially reduced under high-pressure conditions. The present study demonstrates a numerical simulation tool for high-pressure PEMWE. A future study of a full 3D PTL model is currently underway. Figure 1

Prediction of Oil–Water Two-Phase Flow Patterns Based on Bayesian Optimisation of the XGBoost Algorithm

With the continuous advancement of petroleum extraction technologies, the importance of horizontal and inclined wells in reservoir exploitation has been increasing. However, accurately predicting oil–water two-phase flow regimes is challenging due to the complexity of subsurface fluid flow patterns. This paper introduces a novel approach to address this challenge by employing extreme gradient boosting (XGBoost, version 2.1.0) optimised through Bayesian techniques (using the Bayesian-optimization library, version 1.4.3) to predict oil–water two-phase flow regimes. The integration of Bayesian optimisation aims to enhance the efficiency of parameter tuning and the precision of predictive models. The methodology commenced with experimental studies utilising a multiphase flow simulation apparatus to gather data across a spectrum of water cut rate, well inclination angles, and flow rates. Flow patterns were meticulously recorded via direct visual inspection, and these empirical datasets were subsequently used to train and validate both the conventional XGBoost model and its Bayesian-optimised counterpart. A total of 64 datasets were collected, with 48 sets used for training and 16 sets for testing, divided in a 3:1 ratio. The findings highlight a marked improvement in predictive accuracy for the Bayesian-optimised XGBoost model, achieving a testing accuracy of 93.8%, compared to 75% for the traditional XGBoost model. Precision, recall, and F1-score metrics also showed significant improvements: precision increased from 0.806 to 0.938, recall from 0.875 to 0.938, and F1-score from 0.873 to 0.938. The training accuracy further supported these results, with the Bayesian-optimised XGBoost (BO-XGBoost) model achieving an accuracy of 0.948 compared to 0.806 for the traditional XGBoost model. Comparative analyses demonstrate that Bayesian optimisation enhanced the predictive capabilities of the algorithm. Shapley additive explanations (SHAP) analysis revealed that well inclination angles, water cut rates, and daily flow rates were the most significant features contributing to the predictions. This study confirms the efficacy and superiority of the Bayesian-optimised XGBoost (BO-XGBoost) algorithm in predicting oil–water two-phase flow regimes, offering a robust and effective methodology for investigating complex subsurface fluid dynamics. The research outcomes are crucial in improving the accuracy of oil–water two-phase flow predictions and introducing innovative technical approaches within the domain of petroleum engineering. This work lays a foundational stone for the advancement and application of multiphase flow studies.

Visualization of the two-phase flow in the air gap of an optically accessible generic electric motor and its effect on torque

Electric motors with high-power densities are required for the implementation of electromobility. To achieve this, direct liquid cooling methods are increasingly being considered, in which oil is injected into the motor compartment. This results in a two-phase flow that can be used for efficient cooling. However, the oil, which can also penetrate the air gap between the rotor and stator, can also lead to additional losses due to increased friction. Since little is known about the two-phase flow in such systems, especially in the air gap, it is investigated by means of simple optical visualizations and high-speed laser-induced fluorescence imaging as well as torque measurements. The measurements are carried out in the air gap of an optically accessible generic model of a directly cooled electric motor. Speed variations were performed from 100 to 2000 rpm, and three different two-phase flow regimes were observed. At low speeds (Flow Regime 1), the air gap is filled locally with oil in radial direction, in the medium speed range (Flow Regime 2) with foam, while at high speeds (Flow Regime 3) separated films were observed on the rotor and stator. The torque difference between the two-phase and single-phase operation, which quantifies the mechanical losses due to the injected oil, increased continuously due to the oil in the air gap until it reached a maximum in Flow Regime 2 due to foam formation. In Flow Regime 3, the torque difference was negative. This was attributed to the fact that the grooves in the stator were filled with oil, thus reducing the turbulence generation of the air flow.

Modeling of microfluidic solvent extraction: Evaluation of different flow regimes and cascade configurations

ABSTRACT Solvent extraction using a microfluidic platform is one of the process intensification tools. It may involve different two-phase flow regimes that have different physical and mass transfer characteristics. Therefore, this study focuses on the selection of a better flow regime, flow conditions and cascade configuration (cross or counter-current) using mathematical modeling for a typical high-throughput application (1000 L/day). The mathematical model is based on a plug flow model. The liquid–liquid system considered is a water–acetone–toluene system. The different flow regimes such as slug flow (SF), slug-droplet flow (SDF) and core-annular flow (CAF) in different cascade configurations are compared on the basis of parameters like specific extraction rate (SER), solvent extraction process effectiveness (SEPE) and modified merit index (MMI). Among the different flow regimes and cases studied, CAF is found to offer lesser parallel paths, total extraction length, total pressure drop and residence time. The best case of CAF has higher SER (3.6 times), SEPE (3.6 times) and MMI (21.8 times) than the best case of SF in both cascades. SDF shows the results between SF and CAF. For a specific flow regime, higher values of parameters are obtained at higher flow rates. Counter-current cascade is found to be better than cross-current cascade.

Determination Of Two-Phase Flow Regimes Of A Condensing Water-Air Mixture In An Array Of Micro Channels

Proton exchange membrane (PEM) full cells (FCs) are a promising alternative to combustion engines for mobile applications. Their signature low operating temperature leads to the possible existence of product water in its liquid form. Liquid water formation and transport are important phenomena to understand and model in order to facilitate the development of new, more efficient PEMFCs. This contribution investigates the two-phase flow phenomena separately by introducing a gaseous air-vapor mixture into an optically accessible fuel cell flow plate with 14 parallel micro channels (confinement number Co = 7.3) and a carbon paper inlay representing the FC's gas diffusion layer and bringing it to the point of onset of condensation. Special care is taken to provide a controlled and reproducible air-vapor mixture with low pressure pulsation. One key feature of the experiment is that the flow plate used is designed to be as similar to a regular research fuel cell flow plate as possible, retaining its channel surface properties and electrical parameters. It can also be heated to a given temperature to mimic the fuel cell operating conditions. Therefore, the same plate can also be used in in-situ experiments in an operated fuel cell. An air mass flow rate of 500 - 1000 mLn / min with relative humidities of 75% - 95% at 65°C and 75°C was investigated. The emerging flow patterns were captured using a high-speed camera at 250 Hz and categorized using canonical two-phase flow regime nomenclature. It is shown that the present setup can produce the relevant two-phase flow patterns that can be found in operated fuel cells as reported by previous studies and is furthermore equipped to investigate flow states not easily reached with an operated fuel cell (e.g. flooded channels).

Study on the Flow Characteristics of Heavy Oil-Water Two-Phase Flow in the Horizontal Pipeline.

Aiming at the problem of transportation for heavy oil during the middle-later development stages of the Lvda oilfield, based on the self-developed design of a visual circulating flow experimental apparatus for heavy oil-water two-phase flow-the flow regime characteristics and corresponding drag properties of the two-phase flow of Lvda viscous oil, which is simulated by 500# industrial white oil and water in a horizontal pipeline are investigated experimentally. According to the Kelvin-Helmholtz instability theory, the flow pattern transition criteria from stratified flow to annular flow (AF) are proposed. The effects of 0.11-0.90 m/s oil superficial velocities, 0.06-1.49 m/s water superficial velocities, and 0.09-0.93 input water cuts on the drag reduction effect of different flow regimes are analyzed. The experimental results indicated that with the increase of mixing velocity and water volume fraction, stratified flow, AF, oil plug flow, and dispersed oil lump flow are successively observed in the horizontal heavy oil-water two-phase flow, in which AF is the main flow pattern. As the Froude number increases to 4.0, the input water volume fraction does not change any more and remains at about 10% of the total flow rate in the process of converting from stratified flow to AF. The four delivery approaches can archive the reduction of transportation resistance for heavy oil at different degrees, in which the transportation of heavy oil surrounded by a water ring has the best effect of drag reduction. At the optimal working conditions of 0.61 m/s oil superficial velocity, 0.07 m/s water superficial velocity, and 0.10 input water cut, the pressure drop of water annulus conveying for heavy oil is only 1/62.54 of that of separate transport for pure heavy oil under the same oil flow rate.

Modeling and simulation of air-water upward annular flow characteristics in a vertical tube using CFD

Annular flow refers to a special type of two-phase flow pattern in which liquid flows as a thin film at the periphery of a pipe, tube, or conduit, and gas with relatively high velocity flows at the center of the flow section. This gas also includes dispersed liquid droplets. The liquid film flow rate continuously changes inside the tube due to two processes-entrainment and deposition. To determine the liquid holdup, pressure drop, the onset of dryout, and heat transfer characteristics in annular flow, it is important to have proper knowledge of flow characteristics. Especially a better understanding of entrainment fraction is important for the heat transfer and safe operation of two-phase flow systems operating in an annular two-phase flow regime. Therefore, the objective of this work is to develop a computational model for the simulation of the annular two-phase flow regime and assess the various existing models for the entrainment rate. In this work, Computational Fluid Dynamics (CFD) in ANSYS FLUENT has been applied to determine annular flow characteristics such as liquid film thickness, film velocity, entrainment rate, deposition rate, and entrainment fraction for various gas-liquid flow conditions in a vertical upward tube. The gas core with droplets was simulated using the Discrete Phase Model (DPM) which is based on the Eulerian-Lagrangian approach. The Eulerian Wall Film (EWF) model was utilized to simulate liquid film on the tube wall. Three different models of Entrainment rate were implemented and assessed through user-defined functions (UDF) in ANSYS. Finally, entrainment for fully developed flow was determined and compared with the experimental data available in the literature. From the simulations, it was obtained that the Bertodano correlation performed best in predicting entrainment fraction and the results were within the ±30 % limit when compared to experimental data.

Identifying steady and pulsatile two-phase flow regimes with pressure drop signals and acoustic emissions

Identifying two-phase flow regimes is vital for understanding phase-change heat and mass transfer processes. Traditionally, flow regime identification has relied on subjective flow visualization studies, which are then converted to flow regime maps applicable to specific operating conditions. However, these conventional, largely subjective, maps fall short in predicting abrupt changes in flow patterns that often occur during regular operation of vapor compression and absorption heat pumps and other thermal systems. Conventional flow regime maps are also inadequate for addressing the transient, intermittent, or periodic flow regimes that occur in boiling and condensation enhanced using acoustic and other emerging techniques. Therefore, there is a pressing need for alternative flow regime identification techniques that can adapt and reliably track rapid and local changes in two-phase hydrodynamics. Promising candidates for dynamic flow pattern classification include high-resolution pressure drop signals and acoustic emission spectra, which can provide insights into the local hydrodynamics within a flow channel. To assess the feasibility of these measurement techniques, differential pressure and condenser microphone measurements are recorded alongside high-speed videos of a two-phase flow of saturated R134a. The total mass flux and vapor quality are varied to understand the effects of phase velocity and liquid inventory on wave propagation in the channel. Additionally, forced oscillations are introduced to a steady two-phase flow to analyze their impact on flow patterns and their corresponding pressure drop and acoustic signals. Statistical analyses, including Gaussian mixture modeling, are employed to reveal characteristic pressure drop probability associated with each flow pattern, which form the basis for developing a model capable of predicting flow regimes in both steady and oscillating flows. The resulting framework introduces a new flow regime identification technique that can adapt to dynamic operating conditions, benefiting a wide range of thermal systems and phase-change processes.

Liquid droplet entrainment in an annular flow boiling regime—A Bayesian regularization algorithm based study

Accurate prediction of the entrained liquid droplet fraction in an annular two-phase flow regime plays a crucial role for estimating the dryout type critical heat flux to identify the optimized flow characteristics in the thermal systems across different industries. Existing studies have provided different correlations based on the limited experimental data. However, these correlations are applicable to certain operating conditions. Therefore, the present study aims at applying a deep learning method, specifically an artificial neural network (ANN), to enhance the prediction of the entrained liquid droplet fraction. Experimental data from various works on annular flow, covering a wide spectrum of pressure and flow conditions, are utilized for training the ANN model. Eight input variables, viz, superficial gas velocity (JSG), superficial liquid velocity (JSL), gas viscosity (μG), liquid viscosity (μL), gas density (ρG), liquid density (ρL), pipe diameter (d) and liquid surface tension (σLV) are considered as input features. The entrained liquid droplet fraction is the single output feature. The present model employs the Bayesian regularization backpropagation algorithm for training. The present ANN model is compared against the performance of linear regression, decision tree and support vector machine algorithms, and found that the performance of the present Bayesian regularization neural network (BRNN) model is superior within ∼7.5% deviation. Further, the BRNN model is coupled with the film mass flow rate model to obtain the axial variation of the liquid film mass flow rate and good agreement is noticed when compared against the experimental data.

Experimental analysis of subcooled flow boiling in a microchannel evaporator of a pumped two-phase loop

High-power electronics exceed the heat dissipation capability of conventional air or liquid-based forced convection cooling systems. A pumped two-phase loop (P2PL) utilizing phase change heat transfer can effectively transport and dissipate high heat fluxes generated from the electronics at very small thermal resistance while consuming negligible pumping power. Existing research primarily focuses on standalone evaporators with fixed boundary conditions, neglecting the interactions between the evaporator and other components within the P2PL. This knowledge gap hinders the comprehensive understanding of system performance. To address this need, an experimental study was conducted to examine subcooled flow boiling in a microchannel evaporator of P2PL using R-134a. The parameters used for the investigation are evaporator heat input, mass flux, chiller loop inlet temperature, and fluid charge ratio. Additionally, flow visualization through a sight glass tube at the evaporator outlet was used to identify the two-phase flow regimes, namely – the onset of nucleate boiling, bubbly, slug, stratified, annular, and mist flow. The dominant flow boiling mechanisms and its transitions in the microchannel were identified based on the varying slopes of the boiling curves. The dynamic responses were additionally utilized to differentiate between single-phase and two-phase flows as well as to identify the propagation of dryout in the microchannel evaporator. The study further revealed that for a constant wall superheat, within the convective boiling dominant regime, the heat transfer rate in the evaporator exhibits an increase with the mass flow rate. In contrast, in the nucleate boiling dominant regime, the heat transfer rate remains unaffected by the mass flow rate. Furthermore, it was found that as the fluid charge ratio increases, the heat transfer coefficient and pressure drop in the evaporator decreases.

Spatio-temporal void fraction visualization in air-water two-phase flow regime transitions by combination of convolutional neural network and long short-term memory implemented into multiple current-voltage (MCV-CNN_LSTM)

Spatio-temporal void fraction in air-water two-phase flow regime transitions has been visualized by combination of convolutional neural network and long short-term memory implemented into multiple -current-voltage (MCV-CNN_LSTM). The MCV-ML is composed of two components, which are MCV-CNN_LSTM training and evaluation. In the first component, four steps are carried out as 1) true void fraction α measurement was conducted by wire-mesh sensor (WMS) as objective variables, 2) simulated MCV voltage U calculation by simulation of MCV measurement for explanatory variable, 3) CNN_LSTM training instance generation, and 4) CNN_LSTM model training and testing. In the second component, two additional steps are experimentally conducted which are 5) actual MCV voltage V measurement by MCV for input of trained ML, and 6) spatio-temporal void fraction α prediction. For the dataset generation, the experiment was conducted on air-water two phase flow regime transitions in vertical pipe with an inner diameter of 25 mm and a length of 350 mm from the elbow. The superficial velocity of liquid and gas phase are varied to present the flow regime transition of bubbly to slug flow. As a result, the estimated spatio-temporal void fraction αˆ by MCV-CNN_LSTM enables the visualization of detailed gas distribution in vertical upward two-phase flow. The spatial averaged instantaneous void fraction measured by WMS and estimated by MCV-CNN_LSTM show qualitative agreement in normalized cross correlation (INCC) of 0.364 on its temporal variation.

Experimental heat transfer results and flow visualization of horizontal Near-Saturated liquid nitrogen flow boiling in uniformly heated circular tube under Earth gravity

This study investigates 1-ge horizontal flow boiling of liquid nitrogen with a near-saturated inlet along a circular tube of dimensions 8.5-mm inner diameter and 680-mm heated length. Experiments are conducted using a payload developed for eventual parabolic flight experiments. The operating parameters varied are mass velocity of 406.76–1572.77 kg/m2s, inlet quality of −0.05 to −0.01, and inlet pressure of 336.29–493.07 kPa. High speed video recordings are presented to explain two-phase flow structure and regime transitions which are visualized through a transparent tube in a visualization section situated downstream of the heated tube. Recognized flow patterns are bubbly, plug, slug, stratified annular, and annular. Heat transfer results are presented and discussed in terms of flow boiling curve trends, streamwise wall temperature profiles, streamwise heat transfer coefficient (HTC) profiles, and average HTCs. Previous HTC correlations are evaluated against the measured HTC data, of which two are identified for superior accuracy in predicting cryogen data. Finally, this study confirms the reliability and readiness of the payload for subsequent parabolic flight experiments intended for acquisition of microgravity data.

Thermal Characterization of Subcooled Flow Boiling in a Pin-Fin Coldplate With Non-Uniform Heating

Abstract Coldplates are a crucial component in various cooling applications, such as cooling data center servers and power electronics. The unprecedented growth in electronics power density, along with the resulting ultrahigh heat fluxes, demands a transition from single-phase forced convection to two-phase flow boiling heat transfer. The majority of studies in the literature have focused on flow boiling in fin-enhanced silicon microgaps and microchannels, with only a few addressing flow boiling in millimeter-scale heat sinks. In the present study, flow boiling of HFE-7200 dielectric fluid in a millimeter-scale pin-fin coldplate is experimentally investigated under nonuniform heating conditions. Four background heaters represent the low-dissipating-power devices. On the other hand, five hotspot heaters mimic the high-heat-flux devices and generate heat fluxes ranging from 50 W/cm2 to 1000 W/cm2, corresponding to hotspot heat inputs ranging from 62.5 W to 1.25 kW, respectively. The coldplate's thermohydraulic performance is investigated for various flow rates and inlet temperature ranging from 0.5 L/min to 1.5 L/min and from 25 °C to 60 °C, respectively. A high-speed camera is utilized for a narrow field of view (FOV) flow visualization at a frame rate of 2229 fps while a digital camera is used for a wider FOV at 60 fps. Flow visualization demonstrated the transition between bubbly, slug/churn, and stratified two-phase flow regimes.

A DETAILED NUMERICAL INVESTIGATION OF TWO-PHASE FLOWS INSIDE A PLANAR FLOW-BLURRING ATOMIZER

Flow-blurring atomization is an innovative twin-fluid atomization approach that has demonstrated superior effectiveness in producing fine sprays compared to traditional airblast atomization methods. In flow-blurring atomizers, the high-speed gas flow is directed perpendicular to the liquid jet. Under specific geometric and physical conditions, the gas penetrates back into the liquid nozzle, resulting in a highly unsteady bubbly two-phase mixing zone. Despite the remarkable atomization performance of flow-blurring atomizers, the underlying dynamics of the two-phase flows and breakup mechanisms within the liquid nozzle remain poorly understood, primarily due to the challenges in experimental measurements of flow details. In this study, detailed interface-resolved numerical simulations are conducted to investigate the two-phase flows generated by a planar flow-blurring atomizer. By varying key dimensionless parameters, including the dynamic-pressure ratio, density ratio, and Weber number, over wide ranges, we aim to comprehensively characterize their effects on the two-phase flow regimes and breakup dynamics.