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Sensors are crucial in the understanding of machines working under high temperatures and high-pressure conditions. Current devices utilize polymeric materials as electrical insulators which pose a challenge in the device’s lifespan. Ceramics, on the other hand, is robust and able to withstand high temperature and pressure. For such applications, a co-fired ceramic device which can provide both electrical conductivity and insulation is beneficial and acts as a superior candidate for sensor devices. In this paper, we propose a novel fabrication technique of complex multi-ceramics structures via 3D printing. This fabrication methodology increases both the geometrical complexity and the device’s shape precision. Structural ceramics (alumina) was employed as the electrical insulator whilst providing mechanical rigidity while a functional ceramic (alumina-doped zinc oxide) was employed as the electrically conductive material. The addition of sintering additives, tailoring the printing pastes’ solid loadings and heat treatment profile resolves multi-materials printing challenges such as shrinkage disparity and densification matching. Through high-temperature co-firing of ceramics (HTCC) technology, dense high quality functional multi-ceramics structures are achieved. The proposed fabrication methodology paves the way for multi-ceramics sensors to be utilized in high temperature and pressure systems in the near future.

In this study, an inverse-problem method was applied to estimate the solid concentration in a solid–liquid two-phase flow. An algebraic slip mixture model was introduced to solve the forward problem of solid–liquid convective heat transfer. The time-average conservation equations of mass, momentum, energy, as well as the volume fraction equation were computed in a computational fluid dynamics (CFD) simulation. The solid concentration in the CFD model was controlled using an external program that included the inversion iteration, and an optimal estimation was performed via experimental measurements. Experiments using a fly-ash–water mixture and sand–water mixture with different solid concentrations in a horizontal pipeline were conducted to verify the accuracy of the inverse-problem method. The estimated results were rectified using a method based on the relationship between the estimated results and estimation error; consequently, the accuracy of the corrected inversion results improved significantly. After a verification through experiments, the inverse-problem method was concluded to be feasible for predicting the solid concentration, as the estimation error of the corrected results was within 7% for all experimental samples for a solid concentration of less than 50%. The inverse-problem method is expected to provide accurate predictions of the solid concentration in solid–liquid two-phase flow systems.

Liquid two-phase flow has been studied widely by many authors. Two phase flow exist in petroleum industries, nuclear power generation and chemical plants. In the present study a review on two phase flow such as oil/water was discussed, also the patterns of the flow and types of measurement are presented. Also a review on the new trends using the computational fluid dynamic (CFD) to predict the liquid-liquid two phase are viewed.

In the two-phase flow particle image velocimetry (PIV) experiment of an object entering water, the mask of non-computed area and the calculation of velocity field in particle images are two key stages. Due to the complexity of the edge of the object in the particle image, the mask calibration is usually performed manually and then the PIV estimation is carried out. We propose a cascaded convolutional neural network (CNN) in this paper to implement end-to-end two-phase flow fluid motion estimation. In the first stage, the image segmentation network U-Net is used to mask the non-computational area of the image and extract the liquid phase. In the second stage, we adopt the improved deep optical flow network, which is known as recurrent allpairs field transforms (RAFT) to calculate velocity field. What’s more, the corresponding datasets are generated for training model parameters. Finally, our approach is tested on synthetic and experimental images. The experimental results indicate that our approach not only reaches accurate segmentation of the calculated liquid phase region, but also achieves a high-precision velocity field calculation. Meanwhile, this cascade CNN model has high efficiency toward real-time estimation.

In this paper, a numerical study is performed in order to investigate the effect of the virtual viscosity on simulation of separated two-phase flow of gas-liquid. The governing equations solved by shock capturing method which can provide predicting the interface without the flow field solving. In this work, in order to calculate the numerical flux term, first-order centred scheme (Force scheme) was applied cause of its accuracy and appropriate validation. Analysis approves that the obtained stability range of this research is consistent with the classic Kelvin-Helmholtz instability equation only for the long wavelength with small amplitude. Results reveal that when the wavelengths are reduced, the specified range is not consistent and wavelength affects on instability range and it is over predicted. An algorithm for water faucet problem was developed in Fortran language. Short wavelength perturbations induce unbounded growth rates and make it impossible to achieve converging solutions. The approach taken in this article has been to adding virtual viscosity as a CFD technique, is used to remedy this deficiency.

In the study, carbon fiber as porous medium, polycarbosilane-xylene solution and air as fluid, the heat and mass transfer in porous media was investigated. CT images of micro pores were obtained by applying Micro-CT technology. Based on the reconstruction model, 3D structure reconstruction was carried out. The surface tension model and contact angle model were introduced to establish the CFD model for simulation, which was verified by experiments. The effects of feed pressure P and liquid viscosity μ on the mass and heat transfer were systematically studied. It was found that under different P, the liquid holdup α, average temperature T and heat Q absorbed by liquid phase increase with time until stable. The larger the P, the smaller the stable values of T and Q. When μ was larger, the breakthrough time was longer, and the time for T and Q to reach stable values was longer.

The present study is focused on proposing, implementing, and optimizing machine learning based pipelines for estimating the pressure drop in evaporating R134a flow passing through micro-fin horizontal tubes. Accordingly, an experimental activity is first conducted, in which the pressure drop of the flow at various operating conditions is measured. Physical models that are available in the literature are then implemented and the corresponding accuracy, while being applied to the obtained dataset, is determined. Machine learning based pipelines, with dimensionless parameters provided as features and two-phase flow multipliers as targets, are then developed. In the next step, a feature selection procedure is performed and an optimization process is then conducted to find the algorithms and the corresponding hyper-parameters, using which results in the highest possible accuracy. The optimal pipeline is demonstrated to be the one in which the liquid only two-phase multiplier is chosen as the target and is provided with only 5 dimensionless parameters as selected input features. Employing the latter pipeline leads to a mean absolute relative deviation (MARD) of 6.27 % on the validation set and 6.41 % on the test set, which is notably lower than the one achieved using the most promising physical model (MARD of 18.74 % on the validation set and 18.08 % on the test set). Furthermore, as the dataset and the obtained optimal pipeline will be made publicly accessible, the proposed methodology also offers higher ease of use and reproducibility.

High-vapor-quality flow boiling on structured surfaces has the potential to enhance heat transfer performance and reduce temperature/pressure instability compared to conventional lower quality flow boiling on smooth surfaces. In this article, we discuss recent research progress on structured surface-enhanced flow boiling. We discuss lessons learned and focus on the challenges remaining. Although some degree of mechanistic understanding of the effect of surface structures on the flow boiling process has been gained, many important challenges remain to enable real utilization. Primarily, a need exists for a greater fundamental understanding of the processes if design tools are to be developed capable of guiding engineers to select and implement enhancements. The challenges discussed stem from a community-wide workshop focusing on high-exit-quality flow boiling, held in June 2020. Four key challenges were identified: 1) the need for better understanding of the mechanisms governing surface-structure-enhanced flow boiling and their effect on heat transfer coefficient, critical heat flux, pressure drop, and flow stability; 2) the need for rigorous quantification of surface durability and manufacturing scalability; 3) the need to understand effects of using working fluids other than water including refrigerants, supercritical fluids, and other dielectric fluids; and 4) the need to establish thermal resistance limits dependent on liquid film thickness. We end this article by providing conclusions detailing where we believe that the community should direct both fundamental and applied efforts in order to solve the identified challenges, which limits the implementation of high-vapor-quality flow boiling on surface structures.

The separation circuitry has been proven in the past to improve the performance of microchannel condensers. In the vertical second header of the condenser, liquid separates from vapor mainly due to gravity. However, separation is usually not perfect, expressed through the separation efficiency. This study presents the phase separation result in the second header calculated by the Euler-Euler method of Computational Fluid Dynamics (CFD). Simulations are conducted for two-phase refrigerant R134a flow in the second header with 21 microchannel tubes in the 1st pass. The inlet mass flux to the second header (through the microchannels of the 1st pass) in the simulation is 166 kg m − 2 s − 1, 207 kg m − 2 s − 1, and 311 kg m − 2 s − 1. The inlet quality is 0.13 to 0.21. The results agree well with the experimental results with flow visualization and the results of a simpler 1-D numerical model. Results show that the liquid separation efficiency decreases as the vapor separation efficiency increases, following a linear trend in the experimental range. The void fraction result shows liquid mainly flows in the half of the header without microchannel tube intrusions. The velocity profile in the header is presented and reverse flow is identified on the exit planes of the inlet section connecting to the 2nd-upper pass and the 2nd-lower pass. The pressure profile in the header is also revealed and it indicates that the 1-D pressure assumption may still apply to two-phase flow in a header.

Purpose Offshore industries encounter severe production downtime due to high liquid carryovers in the T-junction. The diameter ratio and flow regime can significantly affect the excess liquid carryovers. Unfortunately, regular and reduce T-junctions have low separation efficiencies. Ansys as a commercial computational fluid dynamics (CFD) software was used to model and numerically inspect a novel diverging T-junction design. The purpose of diverging T-junction is to merge the specific characteristics of regular and reduced T-junctions, ultimately increasing separation efficiency. The purpose of this study is to numerically compute the separation efficiency for five distinct diverging T-junctions for eight different velocity ratios. The results were compared to regular and converging T-junctions. Design/methodology/approach Air-water slug flow was simulated with the help of the volume of the fluid model, coupled with the K-epsilon turbulence model to track liquid-gas interfaces. Findings The results of this study indicated that T-junctions with upstream and downstream diameter ratio combinations of 0.8–1 and 0.5–1 achieved separation efficiency of 96% and 94.5%, respectively. These two diverging T-junctions had significantly higher separation efficiencies when compared to regular and converging T-junctions. Results also revealed that over-reduction of upstream and downstream diameter ratios below 0.5 and 1, respectively, lead to declination in separation efficiency. Research limitations/implications The present study is constrained for air and water as working fluids. Nevertheless, the results apply to other applications as well. Practical implications The proposed T-junction is intended to reduce excessive liquid carryovers and frequent plant shutdowns. Thus, lowering operational costs and enhancing separation efficiency. Social implications Higher separation efficiency achieved by using diverging T-junction enabled reduced production downtimes and resulted in lower maintenance costs. Originality/value A novel T-junction design was proposed in this study with a separation efficiency of higher than 90%. High separation efficiency eliminates loss of time during shutdowns and lowers maintenance costs. Furthermore, limitations of this study were also addressed as the lower upstream and downstream diameter ratio does not always enhance separation efficiency.

Pulsating heat pipes have shown great potential in the field of engineering applications, such as the cooling of microelectronic devices. The correlation between the flow characteristics and the thermal performance of the heat pipe still needs to be further explored. In this paper, the operation state and flow patterns inside pulsating heat pipes with two inner diameters of 1 and 2 mm are experimentally visualised and analysed. The effect of the inner diameter on the thermal performance of heat pipes is evaluated. The results indicate that the operating modes, including small pulsations, bulk pulsations and circulation are sequentially obtained in pulsating heat pipes with the increase of the heat input. The decrease of the inner diameter of pulsating heat pipes results in the capillary instability and the increase of the flow frictional resistance. When the filling ratio is 55%, the thermal performance of a pulsating heat pipe with an inner diameter of 2 mm is better than that of a pulsating heat pipe with an inner diameter of 1 mm.

Coal and gas outburst is still a major safety problem in the process of coal production in China. Correctly understanding of the migration law of outburst high gas and pulverized coal is an important basis for accurately predicting the occurrence time and possible scope of outburst. To reveal the airflow disturbance characteristics and coal-gas flow rule in coal and gas outburst process, outburst coal-gas migration simulations under different gas pressures were conducted using a self-developed visual outburst dynamic effect test device. The results showed that coal-gas flow state at the outburst port is divided into subcritical flow, critical flow, and supercritical flow state. The pulverized coal-gas flow migration in the roadway space can be divided into coal gas two-phase flow area, air compression area, and undisturbed area. Under the experimental conditions, the maximum propagation velocities of wave are 342.22~359.21 m/s, and the coal gas two-phase flow is far less than the propagation velocities of outburst wave, just 3.68~33.33 m/s. When the outburst energy is large, multiple compression waves can superimpose to form shock waves. The peak value of the wave does not necessarily appear in the first boosting range. The presence of pulverized coal leads to a faster attenuation of shock wave, but it makes a greater dynamic destructive force at the same speed.

Two-phase flow measurement is a key issue in the natural gas industry. Among the many different flow meters available, the swirl meter stands out for its reliability, ease of maintenance, large measurement range, and strong output signal. In this study, computational fluid dynamics (CFD) simulations were conducted, using RNG k-e turbulence and Eulerian multiphase models, to investigate the gas–liquid two-phase flow characteristics in swirl meters. The CFD simulation results were then validated against the tested precession frequency of our experiment, and a satisfactory match was found between the outcomes. A detailed analysis was then conducted to generate profiles for velocity, pressure, etc. Based on examinations of the flow field distributions, it was found that gas flow inside swirl meters is sensitive to the presence of the liquid phase; the influence increased with the volume fraction of the liquid present. Further investigation indicated that the vortex precession was attenuated in the presence of the liquid phase. This led to the variation of the entire field; the pressure fluctuations at the end of the throat, in particular, resulted in metering errors of the gas flows.

Improving the yield and selectivity of chemical reactions is one of the challenging tasks in paving the way for a more sustainable and climate-friendly economy. For the industrially highly relevant gas–liquid reactions, this can be achieved by tailoring the timescales of mixing to the requirements of the reaction. Although this has long been known for idealized reactors and time- and space-averaged processes, considerable progress has been made recently on the influence of local mixing processes. This progress has become possible through joint research between chemists, mathematicians, and engineers. We present the reaction systems with adjustable kinetics that have been developed, which are easy to handle and analyze. We show examples of how the selectivity of competitive-consecutive reactions can be controlled via local bubble wake structures. This is demonstrated for Taylor bubbles and bubbly flows under technical conditions. Highly resolvednumerical simulations confirm the importance of the bubble wake structure for the performance of a particular chemical reaction and indicate tremendous potential for future process improvements.

In a variable refrigerant flow (VRF) system, the refrigerant is supplied from one outdoor unit to multiple indoor units through a header or refrigerant distributor. When the two-phase refrigerant flows into the distributor, uneven distribution occurs and deteriorate system performance. An experimental apparatus simulating the refrigerant distributor of a VRF system was constructed and air-water two-phase flow distribution characteristics were investigated. The air-water two-phase flow was more mal-distributed as the void fraction increased. A change in void fraction affected the distribution more than a change in mass flow rate in the experimental range. The flow distribution in the header was little affected by variation of the downstream flow resistance. Previous empirical correlations were investigated to predict the air-water two-phase flow distribution measured in this experiment, but performance was poor. To estimate the refrigerant two-phase flow distribution, computational fluid dynamics (CFD) simulations were performed using R410A as a working fluid. The mal-distribution of R410A appeared to be mitigated compared with air-water flow due to the large vapor phase density of R410A.

This work assesses the possibility/feasibility of utilizing the CFD to predict water-air separation in a mini-manifold at ambient conditions. The manifold consists of a header with three reduced T-junctions. A combination of the manifold size and the operating parameters yields a computationally demanding surface tension dominant flow. A computational model of separation is based on the interface tracking technique and the FVM framework. The results are validated against the experiment. Findings show that the separation can be successfully predicted, even under such conditions. Analysis of computational cost revealed that the long computational time is the only limiting factor for regular CFD use in such applications. The paper's novelty is in the successful prediction of phase separation. Such an approach requires flow pattern prediction capabilities in the straight parts of the manifold and physically sound interface split prediction at the T-junctions that have not been explored previously in such a way.

The ability of small and microchannels to enhance mass transfer, reduce diffusional limitations, and create a safer process has made them the best choice for process intensification. This study provides a review of the available experimental and computational literature on the hydrodynamics of liquid–liquid two-phase flow in microscale, namely, flow regimes, pressure drop, and flow structure. When two immiscible liquids flow in small channels, various flow patterns can be observed, with the development of those patterns being influenced by several parameters, such as the geometry of the mixing junction and channel, the channel material, the fluids properties, and the orientation of the flow. As part of the flow patterns observations and measurement, the velocity fields and internal circulation reported in the literature were reviewed in the context of the microparticle velocimetry (μPIV) implementation. Also, relevant to our study is the literature outlining, pressure drop, and the available models that have been used to predict it. These models are discussed in detail with special reference to flow patterns and fluids viscosities. Moreover, the numerical studies results using computational fluid dynamics (CFD) have also been reviewed and discussed, in terms of the validity of the simulation and the different parameters affecting slug formation and flow patterns. Each section of the discussion also points out the gaps in the research and proposes future work that could further develop the technology under a review of liquid–liquid two-phase flow in microchannels.

The present study is focused on determining the most promising set of dimensionless features and the optimal machine learning algorithm that can be employed for data-driven frictional pressure drop estimation of water (single-phase) and air-water mixture (two-phase) flow in micro-finned horizontal tubes. Accordingly, an experimental activity is first conducted, in which the frictional pressure drop of both water and air-water flows, at various flow conditions, is measured. Next, machine learning based pipelines are developed, in which dimensionless parameters are provided as features and the friction factor (for the single-phase case) and the two-phase flow multipliers (for the two-phase case) are considered as the targets. Next, the feature selection procedure is performed, in which the most promising set of features, while employing a benchmark algorithm, is determined. An algorithm optimization procedure is then performed in order to choose the most suitable algorithm (and the corresponding tuning parameters) that lead to the highest possible accuracy. Moreover, the state-of-the-art physical models are implemented and the corresponding accuracy, while being applied to the experimentally obtained dataset, is determined. It is demonstrated that only 5 dimensionless features are selected (among 23 provided features) in the obtained pipeline developed for the estimation of the two-phase gas multiplier (in the extraction procedure of which, the single-phase friction factors are determined only using the Reynolds number and two geometrical parameters). Therefore, the latter procedures notably reduce the complexity of the model, while providing a higher accuracy (MARD of 6.72% and 7.05% on the training and test sets respectively) compared to the one achieved using the most promising available physical model (MARD of 15.21%). Finally, through implementing the forward feature combination strategy on the optimal pipeline, the contribution of each feature to the achieved accuracy is shown and the trade-off between the model's complexity (number of features) and the obtained accuracy is presented. Thus, the latter step provides the possibility of utilizing an even inferior number of features, while achieving an acceptable accuracy. Moreover, since these pipelines will be made publicly accessible, the implemented models also offer a higher reproducibility and ease of use.

The results of modeling the behavior of liquid residues of the rocket fuel component in the fuel tank of the worked-off rocket stage on a ballistic trajectory are presented. The simulation is carried out for the following variants: during the controlled descent of the rocket stage and when using the technology of evaporation of liquid rocket fuel residues in the tanks of the spent stage. It is established that during the controlled descent of the spent rocket stage along the ballistic trajectory at the site of its turn and up to heights of 20 km, the liquid under the influence of overloads is distributed in the form of a film in the area of the bottom and side surface with a coverage area of up to 35 %. At the height of the maximum value of the axial overload, liquid fuel residues in the form of a film move to the area of the bottom and the adjacent side surface of the fuel tank. The total coverage area is about 22 %. The introduction of a hot coolant into the fuel tanks to vaporize the liquid remnants of rocket fuel radically changes the picture of the behavior of the liquid. Due to the high speed of the coolant in the tank, axial overload has little effect on the distribution of fuel residues in the rocket tank

A systematic model development for oil flow in quasi-3D (1D + 2D) is presented. Our approach provides a unified modeling scheme. Besides, additional terms are obtained, which allows for tubing area variation along the flow direction. The area variation can be modeled as analytic function or random fluctuation, as it could be the result of deposits or tubing internal surface roughness. The proposed approach can be used to obtain analytic solutions which provide physical insight into the phenomena under scrutiny, including the validation of software tools, sensor development and sensor placement. One starts from conservation laws as given by kinetic theory and applies the transverse averaging technique (TAT) to extract the one-dimensional approximation in formal grounds. To demonstrate its application, the steady-state Ramey's model, the Hasan's transient model and a simple two-phase model are generated from the obtained equations., Comment: 19 pages, 3 figures

Two-phase flow rate estimation of liquid and gas flow through wellhead chokes is essential for determining and monitoring production performance from oil and gas reservoirs at specific well locations. Liquid flow rate (QL) tends to be nonlinearly related to these influencing variables, making empirical correlations unreliable for predictions applied to different reservoir conditions and favoring machine learning (ML) algorithms for that purpose. Recent advances in deep learning (DL) algorithms make them useful for predicting wellhead choke flow rates for large field datasets and suitable for wider application once trained. DL has not previously been applied to predict QL from a large oil field. In this study, 7245 multi-well data records from Sorush oil field are used to compare the QL prediction performance of traditional empirical, ML and DL algorithms based on four influencing variables: choke size (D64), wellhead pressure (Pwh), oil specific gravity (γo) and gas–liquid ratio (GLR). The prevailing flow regime for the wells evaluated is critical flow. The DL algorithm substantially outperforms the other algorithms considered in terms of QL prediction accuracy. The DL algorithm predicts QL for the testing subset with a root-mean-squared error (RMSE) of 196 STB/day and coefficient of determination (R2) of 0.9969 for Sorush dataset. The QL prediction accuracy of the models evaluated for this dataset can be arranged in the descending order: DL > DT > RF > ANN > SVR > Pilehvari > Baxendell > Ros > Glbert > Achong. Analysis reveals that input variable GLR has the greatest, whereas input variable D64 has the least relative influence on dependent variable QL.

Two-phase flow exists widely in the industrial field, and its research is of great significance for industrial production. In this brief, we design a wire-mesh sensor system with 256 measurement points and conduct vertical upward gas-liquid two-phase flow experiments to capture the flow information under different flow conditions. We find that flow structure can be characterized by the gas-liquid distribution imaging obtained through the wire-mesh sensor. To obtain more detailed imaging, we apply super-resolution methods based on deep learning to improve the imaging quality and choose No-Reference Structural Sharpness to evaluate the effects. The results show that compared with traditional interpolation method, the deep learning-based super resolution can restore more abundant details such as edges and textures, thereby characterizing the flow structure more finely. This provides an important support for further analysis of the flow characteristics and flow regularity, which can effectively optimize the industrial process.

As an increasingly popular flow metering technology, Coriolis mass flowmeter exhibits high measurement accuracy under single-phase flow condition and is widely used in the industry. However, under complex flow conditions, such as two-phase flow, the measurement accuracy is greatly decreased due to various factors including improper signal processing methods. In this study, three digital signal processing methods—the quadrature demodulation (QD) method, Hilbert method, and sliding discrete time Fourier transform method—are analyzed for their applications in processing sensor signals and providing measurement results under gas-liquid two-phase flow condition. Based on the analysis, specific improvements are applied to each method to deal with the signals under two-phase flow condition. For simulation, sensor signals under single- and two-phase flow conditions are established using a random walk model. The phase difference tracking performances of these three methods are evaluated in the simulation. Based on the digital signal processor, a converter program is implemented on its evaluation board. The converter program is tested under single- and two-phase flow conditions. The improved signal processing methods are evaluated in terms of the measurement accuracy and complexity. The QD algorithm has the best performance under the single-phase flow condition. Under the two-phase flow condition, the QD algorithm performs a little better in terms of the indication error and repeatability than the improved Hilbert algorithm at 160, 250, and 420 kg/h flow points, whereas the Hilbert algorithm outperforms the QD algorithm at the 600 kg/h flow point.

Gas–liquid two-phase flow widely exists in petroleum, natural gas, and other industries. Recognition of flow structure is an important issue in the study of two-phase flow, and it is of great significance for the optimization of industrial processes. Therefore, how to recognize complex flow structures effectively represents a challenge. To cope with this problem, we develop a novel deep learning network to recognize flow structures under different flow conditions. In particular, we conduct vertical upward gas–liquid two-phase flow experiments to obtain the flow structure data set on the basis of images collected by a high-speed camera. Then, we design a branch-aggregation network (BAN), where the branch structure is utilized to increase the width of the network, and multilevel aggregation structure is used to fuse features of different levels. In recognition of flow structure, the proposed network achieves an accuracy of 99.60% with a fast convergence speed and shows the advantages in antinoise ability, which is significant for online recognition in industrial processes. The results indicate that BAN can be a feasible method to recognize complex flow structures.

Commercial code CFX was used to examine the performance of a two-fluid model to predict the details of upward isothermal bubbly flow of air and water in a vertical pipe. The model equations are volume-averaged Navier-Stokes equations that require closure models for interfacial forces and bubble-induced turbulence effects. Two-equation SST and k-epsilon RANS turbulence models were also used. A parametric study of closure models included both standard options in CFX and previously published novel closure models that were implemented with user-defined functions. The CFD simulations were compared with two cases from the MTLoop experiments by Lucas et al. at the Helmholtz-Zentrum Dresden Rossendorf: one with wall-peak void fraction profile (MT039), and another with a core-peak void fraction profile (MT118). The effect of changing the drag force closures was not significant for the set examined. Poor predictions were found when the lift force and wall lubrication models were incompatible in magnitude. There was no significant effect of changing the liquid phase turbulence model. Changing the bubble-induced turbulence models, however, had a significant impact on the radial void fraction profile. The novel wall force from Lubchenko et al. at the Massachusetts Institute of Technology significantly improved the prediction of the near wall void fraction in the wall peak profile.

Owing to the destructiveness of the coal–gas outburst (CGO) two-phase flow, a custom-made large-scale test system was used to perform CGO experiments under different gas pressures in this study. The effects of the gas pressure on the migration, the CGO intensity, and the impact characteristics of the CGO two-phase flow were examined. Finally, a physical model containing three key structures was established. The results revealed that a higher initial gas pressure resulted in a higher solid–gas ratio of the coal–gas two-phase flow, a higher initial motion speed of the CGO coal, and a higher CGO intensity. Considerable choking of the two-phase flow occurred in the roadway, and it became more severe with the increasing gas pressure. During the attenuation of the impact force, strong and weak disturbances occurred intermittently. During the CGO process, a strong-disturbance zone developed in the middle of the roadway.

The 2-D numerical simulation of two-phase NH3-water flowing under uniformly heated tube is used. The ANSYS FLUENT is used to predict the time evolution of thermal and hydrodynamic parameters of the bubble pump. Phase-dependent turbulent models are used to calculate the turbulent viscosity of each phase. Through user-defined functions, different interfacial force models and the wall boiling model are implemented in the code. The simulation results show a slow oscillation of hydrodynamic parameters such as: pressure, mass flux, vapor velocity, and liquid velocity during the initial stage of operation. However, a vigorous oscillation is detected for the temperature behavior. The amplitude and period of oscillation decrease with the heat input increasing. By using the void fraction contour, it is possible to predict the flow regime along the bubble pump at different times of the operation. The domination of flow regime is the function of heat flux too. It is bubbly to slug for heat fluxes less than 5 kW/m? and transits from churn to annular for 15 kW/m? and 50 kW/m? of heat flux.

A comprehensive understanding of the marine environment in the offshore area requires phase-resolved wave information. For far-field wave propagation, computational efficiency is crucial, as large spatial and temporal scales are involved. For the near-field extreme wave events and wave impacts, high resolution is required to resolve the flow details and turbulence. The combined use of a computationally efficient large-scale model and a high-resolution local-scale solver provides a solution that combines accuracy and efficiency. This article introduces a coupling strategy between the efficient fully nonlinear potential flow (FNPF) solver REEF3D::FNPF and the high-fidelity computational fluid dynamics (CFD) model REEF3D::CFD within the open-source hydrodynamics framework REEF3D. REEF3D::FNPF solves the Laplace equation together with the boundary conditions on a sigma-coordinate. The free surface boundary conditions are discretized using high-order finite difference methods. The Laplace equation for the velocity potential is solved with a conjugated gradient solver preconditioned with a geometric multigrid provided by the open-source library Hypre. The model is fully parallelized following the domain decomposition strategy and the message passing interface protocol. The waves calculated with the FNPF solver are used as wave generation boundary conditions for the CFD-based numerical wave tank REEF3D::CFD. The CFD model employs an interface capturing two-phase flow approach that can resolve complex wave structure interaction, including breaking wave kinematics and turbulent effects. The presented hydrodynamic coupling strategy is tested for various wave conditions and the accuracy is fully assessed.

Computational Fluid Dynamics (CFD) is used to predict continuous phase axial dispersion coefficient for liquid-liquid two-phase flow in a pulsed-sieve plate column (PSPC). The CFD model is based on...

This paper investigates numerically and experimentally heat transfer forced convective two-phase flow (i.e. air and water) over a rectangular ribbed channel with a vertical orientation. Three distinct rib–groove shapes have been examined. Ribs - groove shapes are; Triangle, Trapezoid, and Semi-Trapezoid ribs-groove. The present study has been performed with continuous heat flux through range of water and air superficial inlet velocity values between 0.105 – 0.316m/s, and 0.263 – 1.320 m/s, respectively. Continuity, momentum and energy calculations have been formulated using the Finite Volume Approach (FVM). Results indicate that the triangle rib-groove has the high heat transfer coefficient and lower temperature difference than other cases against a different number of Reynolds. The experimental data has been compared to numerical results for ribs –grooved channel with deviation of about 1.0% - 7.5%. The channel fitted with triangle ribs shows the highest heat transfer, which is about 59% higher than the smooth channel; 56% for trapezoidal rib, and 44% for channel fitted by semi-trapezoidal rib. Finally, the triangle rib-groove gives a better heat transfer improvement value in comparison with trapezoidal and semi trapezoidal rib-groove channel at constant pumping power.

This work studied the capability of a commercial CFD code in modelling two-phase flow from a header through two branches. The geometry considered in this study consists of a rectangular tank with two horizontal outlet branches of the same diameter d = 6 . 35 mm, separated by a distance L centre to centre L ∕ d = 1 . 5 with their centrelines falling in a plane inclined an angle θ = 30° from the horizontal. A block-structured, hexahedral computational mesh was used. The gas–liquid flow was computed using the inhomogeneous, free surface model of ANSYS CFX. Mesh-independence tests were conducted and the solutions of the discretized equations were progressed in time using a small time step size ( ≈ 0.0001 s). Results were obtained for the critical heights of the interface at the onsets of gas and liquid entrainment at both branches, as well as the outlet mass flow rate and quality during two-phase discharge. Good agreement was found between the numerical results and the available experimental data and correlations.

Numerical modeling of the flow of combustion products of a fuel containing a boron–aluminum admixture in the nozzle of a solid-propellant rocket engine has been carried out. The advantage of boron–aluminum over aluminum in using in composite solid propellants has been shown.

In this research, it is investigated to numerically evaluate the performance of a polymer electrolyte membrane fuel cell (PEMFC). The performance is investigated through the nonuniformity gradient loading at the catalyst layer (CL) of the considered PEMFC. Computational fluid dynamics is used to simulate a 2D domain in which a steady‐state laminar compressible flow in two‐phase for the PEMFC has been considered. In this case, a particular nonuniform variation inside the CL along the channel is assumed. The nonuniform gradient is created using a nonisothermal domain to predict the flooding effects on the performance of the PEMFC. The computational domain is considered as the cathode of PEMFC, which is divided into three regions: a gas channel, a gas diffusion layer, and a CL. The loading variation inside the catalyst is defined as a constant slope along the channel. In order to find the optimum slope, different slope angles are analyzed. The results point out that the nonuniform loading distribution of the catalyst (platinum) along the channel could improve the fuel cell performance up to 1.6% and 5% for power density and voltage generation, respectively. It is inferred that it is better to use more catalyst in the final section of the channel if the performance is the main concern.

Computational studies are employed to construct our understanding of systems and to reduce the monetary and temporal demands of research and development. Proton exchange membrane water electrolysis (PEMWE) devices, with their intricate two-phase transport phenomena and numerous realizable applications, provide an excellent opportunity to utilize computational fluid dynamics (CFD). Toghyani et al.1 modeled the performance of a high-temperature, vapor-fed PEMWE device using CFD simulations, finding fair agreement with prior experimental studies. For low-temperature electrolysis, Nie and Chen2 used a mixture model to study the flow regimes in liquid-fed channels. We employed a CFD model to simulate a PEMWE device, exploring the viability of isothermal and non-isothermal mixture models for a liquid-fed cell. The flow rate was altered to study the change in cell temperature and performance, and the model results were compared to experimental data. Additionally, bulk properties of the porous transport layer at the anode were altered to examine performance effects; such effects were expected to be virtually non-existent in light of experimental observations.3,4 The results demonstrate when a continuum assumption is best utilized to predict electrolysis efficiency. References: S. Toghyani, E. Afshari, and E. Baniasadi, Journal of Thermal Analysis and Calorimetry, 135, 1911–1919 (2019). J. Nie and Y. Chen, International Journal of Hydrogen Energy, 35, 3183–3197 (2010). T. Schuler, T. J. Schmidt, and F. N. Büchi, Journal of The Electrochemical Society, 166, F555–F565 (2019). J. Lopata et al., Journal of The Electrochemical Society, 167, 064507 (2020). Figure 1

Gas–liquid two-phase flow widely exits in production and transportation of petroleum industry. Characterizing gas–liquid flow and measuring flow parameters represent challenges of great importance, which contribute to the recognition of flow regime and the optimal design of industrial equipment. In this paper, we propose a novel complex network-based deep learning method for characterizing gas–liquid flow. Firstly, we map the multichannel measurements to multiple limited penetrable visibility graphs (LPVGs) and obtain their degree sequences as the graph representation. Based on the degree distribution, we analyze the complicated flow behavior under different flow structures. Then, we design a dual-input convolutional neural network to fuse the raw signals and the graph representation of LPVGs for the classification of flow structures and measurement of gas void fraction. We implement the model with two parallel branches with the same structure, each corresponding to one input. Each branch consists of a channel-projection convolutional part, a spatial–temporal convolutional part, a dense block and an attention module. The outputs of the two branches are concatenated and fed into several full connected layers for the classification and measurement. At last, our method achieves an accuracy of 95.3% for the classification of flow structures, and a mean squared error of 0.0038 and a mean absolute percent error of 6.3% for the measurement of gas void fraction. Our method provides a promising solution for characterizing gas–liquid flow and measuring flow parameters.

In order to explore the influence of obstacle shapes on the filling process of pulse detonation rocket engine under valveless self-adaptive working mode, the numerical simulations of two-phase single filling process and multi-cycle experiment were carried out, in which gasoline was utilized as fuel, and oxygen-enriched air as oxidizer. The effects of four kinds of obstacles with different shapes on the filling process of PDRE were studied. The results showed that the existence of obstacles blocks the filling of fuel into the tail of detonation tube, resulting in a great non-uniformity of filling results in the axial and radial directions. Fuel droplets collect near the head and wall of detonation tube, and droplets are seriously blocked by orifice plates. The detonation initiation and stable propagation can be achieved by Shchelkin spirals, annular grooves and spiraling grooves, while the detonation initiation cannot be achieved by orifice plates.

Dust control in underground coal mines has long been of great concern to mine operators, and the use of an auxiliary ventilation system is essential to address dust pollution. The present study focuses on analyzing gas–solid two-phase flow in a typical coal roadway with a fully mechanized heading face in the presence of an auxiliary ventilation system with an air curtain. The two-phase flow was simulated by the Euler–Lagrange method. The dust particles were simulated via the discrete phase model (DPM) based on certain sample particles in the form of parcels. To control dust more effectively, the air curtain generator was optimized based on a recently proposed structure. It was found that the air curtain and the improved form have obvious effects on the air flow field, the dust distribution and the amount of particulate matter (PM2.5) in the front part of the roadway (within approximately 15 m of the heading face). PM2.5 around the driver is substantially reduced after the improvement but is limited when the initial supply air velocity is increased. Selected simulation results were verified by relative field measurements. The results obtained in this study provide useful information on auxiliary ventilation design for use in mining activities.

This study investigates the degradation of the heat transfer performance of a closed-circuit intermediate natural circulation heat transport loop used as a passive safety system in a nuclear power ...

The transfer of batch processes to continuous flow is a major driver for the application of microreactors. Here, we present a methodology for the transfer of (bio-)chemical reactions in batch mode ...

Computational fluid dynamics – population balance (CFD-PB) simulations are performed to predict Sauter mean drop diameter and dispersed phase holdup for liquid–liquid two-phase flow in a pulsed dis...