Flow Domain Research Articles

Vertical line time domain green function and its applications in numerical simulation of ship seakeeping performance

In present study, a vertical line source time domain Green function is proposed and the subsequent numerical evaluation methods are described in detail. For which, Taylor expansion and Chebyshev approximation algorithms are originally extended from point source to line source for small parameters, as well as asymptotic expansions are extended for large parameters. Meanwhile, polynomials are originally proposed to replace the asymptotic expansions to improve the computational efficiency. The accuracy and stability of the proposed method are validated through several cases and good agreement can be obtained.Furthermore, a hybrid Green function method based on the vertical line source Green function and Rankine source is developed for time domain simulation of seakeeping performances of ships. With respect to the hybrid method, the flow domain is divided into inner part and outer part by an artificial control surface. The first-order Taylor Expansion Boundary Element Method (TEBEM) based on the Rankine panel distribution is applied in the inner part, while three-dimensional panel method based on the vertical line source Green function distribution is adopted in the outer part. Under this circumstance, motions of the single-ship (Slender Wigley and Blunt Wigley) and two-ship (two side-by-side ships) in head waves are numerically investigated. The numerical results agree well with the experimental data, which proved the feasibility and the accuracy of present hybrid Green function method for motion prediction of both single-ship and two-ship.

Heat and mass transfer analysis of non‐miscible couple stress and micropolar fluids flow through a porous saturated channel

AbstractThis study examines the flow rate, Bejan number transportation, concentration distribution and thermal characteristics of an immiscible couple stress‐ micropolar fluids within a porous channel. The authors focus on the effects of heat radiation and an angled magnetic field on the thermal dispersion, concentration distribution and entropy formation of two different types of incompressible immiscible micropolar and couple stress fluids inside a porous channel. Here, the static walls of the channel are isothermal, and the pressure gradient in the flow domain's entrance zone is constant. In this flow problem, we tried to simulate thermal radiation in the energy equation by applying Rosseland's diffusion approximation. To solve the problem, the authors have used no‐slip conditions at the channel's immovable walls, a continuity of temperature profile, shear stresses, thermal flux, linear velocity, and micro‐rotational velocity over the fluid‐fluid interface. The equations that govern the flow of immiscible fluids are solved using a well‐defined methodology and both the temperature and flow field are then evaluated using a closed‐form solution. The mathematical results of the thermal distribution and flow velocity are used to derive the Bejan number distribution and the entropy generation number. Graphical discussions are used to illustrate the impact of different emerging factors on the model's flow and thermal properties, which describe the major impact of the proposed model. These variables involve the micropolarity parameter, Reynolds number, inclination angle parameter, radiation parameter, and Hartmann number. The outcomes of the present models are corroborated by previously established results available in the literature.

Real-time Bayesian inversion in resin transfer moulding using neural surrogates

In Resin Transfer Moulding (RTM), local variations in reinforcement properties (porosity and permeability) and the formation of gaps along the reinforcement edges result in non-uniform resin flow patterns, which may cause defects in the produced composite component. The ensemble Kalman inversion (EKI) algorithm has previously been used to invert in-process data to estimate local reinforcement properties. However, implementation of this algorithm in some applications is limited by the requirement to run thousands of computationally expensive resin flow simulations. In this study, a machine learning approach is used to train a surrogate model which can emulate resin flow simulations near-instantaneously. A partition of the flow domain into a low-dimensional representation enables an artificial neural network (ANN) surrogate to make accurate predictions, with a simple architecture. When the ANN is integrated within the EKI algorithm, estimates for local reinforcement permeability and porosity can be achieved in real time, as was verified by virtual and lab experiments. Since EKI utilises the Bayesian framework, estimates are given within confidence intervals and statements can be made on-line regarding the probability of defects within sections of the reinforcement. The proposed framework has shown good predictive capabilities for the set of laboratory experiments and estimates for reinforcement properties were always computed within 1 s.

Unsteady flow of nanofluid over a sheet of variable thickness with nonlinear kinematics

The transport of heat in an unsteady flow of nanofluid has been discussed in this paper. The flow is maintained over a non-flat, variably porous, and moving sheet in the present simulation. Moreover, the sheet has both constant temperature and nanoparticle concentration at its surface. Unsteadiness in all field variables (i.e. the flow, temperature, and nanoparticles concentration) has been produced by the time-dependent quantities, specified at the surface of the sheet (i.e. kinematics of sheet). The well-known Buongiorno model of nanofluid has been undertaken in the present studies. Furthermore, the simulation includes the particular cases of uniform and linear (polynomial & algebraic functions) stretching/shrinking and injection/suction velocities. Besides that observations of the same nature have been recorded for the non-flat sheet. The boundary layer form of the governing PDEs has been utilized to evaluate the exact status of field variables in the flow domain. Appropriate conditions are imposed by considering the geometry of the flow problem. The problem at hand is simplified by introducing a new set of functions for the field variables because of boundary inputs. After this treatment, the system of boundary value ODEs, which contains several dimensionless numbers (parameters), has appeared, and the problem is solved numerically for various values of parameters, involved in it. More precisely, effects of unsteadiness, wall roughness, surface deformation parameters, Prandtl, Schmidt, thermophoretic, and Brownian motion numbers have been seen on profiles of four field variables. Besides that, the numerical results are also obtained for the skin friction coefficient, Nusselt, and Sherwood numbers. The present modelled problem and its solution are compared with classical models of that particular nature and with their solutions.

Double emulsion generation in shear-thinning fluids under electric field effects

Double emulsions are encapsulated microstructures with medical, food, and cosmetics applications. Precise control over their parameters, such as volume, thickness, and size distribution, is crucial. This study numerically investigates, for the first time, the generation of double emulsions in a shear-thinning outer fluid under the influence of an external DC electric field. A dual co-flowing microfluidic device is chosen as the flow domain, and the shear-thinning behavior of the outer fluid is approximated by the Carreau–Yasuda rheological model. The governing equations are discretized on structured numerical grids using the VOF-CSF model and the finite volume method. The combined effects of the shear-thinning behavior and electric forces on compound droplet formation parameters, including volume, eccentricity, maximum thickness, and jet length, are studied carefully. Results show that for highly shear-thinning fluids, the application of electric forces can ensure the generation of desired compound droplets, which is not feasible without electric field effects. However, excessive electric forces can disrupt the droplet generation process, especially for moderate to low shear-thinning fluids. These findings provide new insights into the role of external electric fields in double emulsion generation and highlight their potential to optimize the production process.

Optimum identification of aquifer parameter zone structures using the SHuffled Ant Lion Optimization approach considering general form of the Voronoi tessellation

A new simulation–optimization approach is proposed for solving the inverse parameter structure identification problems in groundwater systems. In the simulation part of the proposed approach, the numerical groundwater flow process is simulated by using MODFLOW. Parameter structures of the aquifer system are identified by partitioning the flow domain into a finite number of zones employing the general form of the Voronoi tessellation (VT). This MODFLOW and VT-based simulation model is then integrated into an optimization model where the SHuffled Ant Lion Optimization approach (SHALO) is used. This work is the first application of SHALO by considering the general form of the VT zonation approach for solving the inverse groundwater parameter structure identification problems. The applicability of the proposed simulation–optimization approach is evaluated on a hypothetical aquifer model in the literature. This evaluation is conducted by solving the same problem using Ant Lion Optimization (ALO) and its improved version, the self-adaptive ALO (saALO) for the same conditions. Furthermore, the identified results are compared with those obtained using harmony search (HS) and hybrid genetic algorithm (hybrid GA) based solution approaches in the literature. The obtained results indicated that the proposed approach provided better identification results than ALO, saALO, HS, and hybrid GA in terms of different evaluation metrics.

On the choice of physical constraints in artificial neural networks for predicting flow fields

The application of Artificial Neural Networks (ANNs) has been extensively investigated for fluid dynamic problems. A specific form of ANNs are Physics-Informed Neural Networks (PINNs). They incorporate physical laws in the training and have increasingly been explored in the last few years. In this work, the prediction accuracy of PINNs is compared with that of conventional Deep Neural Networks (DNNs). The accuracy of a DNN depends on the amount of data provided for training. The change in prediction accuracy of PINNs and DNNs is assessed using a varying amount of training data. To ensure the correctness of the training data, they are obtained from analytical and numerical solutions of classical problems in fluid mechanics. The objective of this work is to quantify the fraction of training data relative to the maximum number of data points available in the computational domain, such that the accuracy gained with PINNs justifies the increased computational cost. Furthermore, the effects of the location of sampling points in the computational domain and noise in training data are analyzed. In the considered problems, it is found that PINNs outperform DNNs when the sampling points are positioned in the Regions of Interest. PINNs for predicting potential flow around a Rankine oval have shown a better robustness against noise in training data compared to DNNs. Both models show higher prediction accuracy when sampling points are randomly positioned in the flow domain as compared to a prescribed distribution of sampling points. The findings reveal new insights on the strategies to massively improve the prediction capabilities of PINNs with respect to DNNs.

Effects of cooler shape and position on solidification of phase change material in a cavity

BackgroundFor balancing the imbalance between the energy supply and demand, phase-change materials (PCMs) provide an efficient means in terms of thermal energy storage and release. The performance of the energy storage is primarily dependent on the melting as well as the solidification process of the storage medium. Faster charging or discharging of the thermal energy is a primary concern for any thermal energy storage unit. On this background, the present study explores the novel approach for enhancing the solidification process of PCM considering the effects of cooler shape (namely semi-circular, triangular, and rectangular) and their position (namely top, side, and bottom) in a molten PCM-filled enclosure. The middle portion of the cooler wall is curved; whereas the remaining cooler wall is straight maintaining the same cooler wall length. MethodsTo analyze the solidification process, the involved transport equations are solved numerically following a finite volume-based computational approach using Ansys Fluent solver in conjunction with the appropriate boundary conditions. The computational model is generated for all the geometry comprising different shapes, as well as positions of the cooler wall. The third-order upwind scheme (QUICK) technique is utilized to discretize the momentum and energy equations. This scheme is well capable to accurately capture the gradients in the temperature and flow domains. Furthermore, the semi-implicit pressure-linked equation (SIMPLE) technique is utilised to address the pressure-velocity coupling. The resolved data are then saved as selective variables (U, V, and θ), which undergo post-processing to produce a local thermo-fluid flow field and extract average data. Significant findingsThe shape, as well as the position of a cooler, dictates the solidification process in an energy storage system. Thermal energy storage with a triangular-shaped cold wall positioned at the top could be opted as an appropriate design approach of an efficient energy storage system compared to a semi-circular or rectangular-shaped cooler model. The shortest solidification time of PCM occurs when the cooler wall is positioned at the top. The top position of the cooler having a triangular shape with higher Grashof number (Gr) values leads to a faster solidification process. Some ideas for possible future research areas in this field are provided after a comprehensive examination.

Radiative squeezed flow of magnetized Prandtl-Eyring fluid over a sensor surface under Soret effect

The central aim of this numerical analysis is to describe the effects of magnetic body force and thermal radiation on the boundary layer flow of Prandtl-Eyring fluid over a sensor surface suspended between two parallel plates. The considered fluid flow is two-dimensional unsteady electrically conducting and viscous incompressible in nature. A novel Soret effect is included in the concentration equation to reveal the significant features of mass diffusion mechanism under the influence of external squeezing mechanism. Surface permeable velocity concept is also included in the boundary conditions to describe the suction/injection process. However, squeezing flows find their abundant applications in bio-medicine, medical and bio-technology areas. Particularly, the micro-cantilevers with physical or chemical sensor surfaces are effectively used in the bio-medical applications for the finding of various hazardous or bio-warfare agents’, infections, hazardous virus, nuclear reactor cooling, polymer processing, pharmaceutical, blood flow, injection molding and many more. Thus, inspired by these practical applications of squeezing flows, the present problem is modeled based on the investigated geometry. The present physical problem gives the highly nonlinear coupled unsteady two-dimensional partial differential equations and which are not amenable to any of the direct methods. Owing to this reason, the appropriate similarity transformations are used to diminish the dimensional complexity of the emerged partial differential equations (PDEs). Thus, a robust Runge-Kutta -4th order scheme (RK-4) is used as a basic tool to obtain the similar solutions of the governing equations. The graphical visualization is used to analyze the computer generated numerical data in the boundary layer regime for the different set of physical parameters. It is observed that, the enhancing magnetic number ( 0.1 ≤ M ≤ 2.7 ) in the flow region 0 ≤ η ≤ 3 increases the velocity field and decreases the temperature and concentration fields. Increasing squeezed flow index ( 0.1 ≤ b ≤ 2.5 ) in the flow domain 0 ≤ η ≤ 3 decreased the fluid velocity, temperature and concentration fields. This result may be useful in sensor surface cooling process. Rising Prandtl-Eyring fluid parameters in the range 0 ≤ η ≤ 3 decreased the velocity field and enhanced the temperature and concentration profiles. Increasing Soret number ( 0.1 ≤ Sr ≤ 0.9 ) in the flow domain 0 ≤ η ≤ 3 enhances the concentration field. Enhancing magnetic and squeezed flow numbers amplifies the skin-friction coefficient on the sensor surface. Enhancing radiation parameter increases the heat transport rate. Increasing Soret number magnifies the concentration transport rate. Finally, it is explored that, the current similar results displaying good agreement with the formerly published results in the literature and this fact confirms the accuracy and guarantee of the RK-4 method and present similar results.

Computational Hemodynamic Analysis of a Patient Specific Abdominal Aortic Aneurysm

Abdominal aortic aneurysm (AAA) is a cardiovascular disease caused by the enlargement of the aorta in the abdomen over time. Unless treated, the growth of AAA continues, resulting in 80% death in the case of rupture. Today, the width of the aneurysm diameter is taken into account in clinical practice to examine the status of AAA. Although there are aneurysms that do not rupture despite reaching a diameter of 9 cm, it is reported that aneurysms with a diameter of 3 cm are ruptured in several cases. Therefore, analyzing only the AAA diameter is not a reliable method, and a deeper investigation is necessary for the rupture risk assessment. In this study, a patient's situation is analyzed using computational fluid dynamics (CFD) simulations, which allows to elucidate the flow dependent parameters such as velocity, vorticity, pressure, and wall shear stress (WSS). First, the patient-specific geometry was obtained and boundary conditions were defined at the inlet and the outlet of the flow domain. The effects of intraluminal thrombus (ILT) formation and patient’s effort conditions were also included in the analysis. According to the results, WSS and vorticity increase with the increasing blood flow velocity. In terms of the rupture risk, it has been found that the effect of patient’s effort level is more critical than the amount of ILT in the AAA.

Analytical Solution for Transient Electroosmotic and Pressure-Driven Flows in Microtubes

This study focuses on deriving and presenting an infinite series as the analytical solution for transient electroosmotic and pressure-driven flows in microtubes. Such a mathematical presentation of fluid dynamics under simultaneous electric field and pressure gradients leverages governing equations derived from the generalized continuity and momentum equations simplified for laminar and axisymmetric flow. Velocity profile developments, apparent slip-induced flow rates, and shear stress distributions were analyzed by varying values of the ratio of microtube radius to Debye length and the electroosmotic slip velocity. Additionally, the “retarded time” in terms of hydraulic diameter, kinematic viscosity, and slip-induced flow rate was derived. A simpler polynomial series approximation for steady electroosmotic flow is also proposed for engineering convenience. The analytical solutions obtained in this study not only enhance the fundamental understanding of the electroosmotic flow characteristics within microtubes, emphasizing the interplay between electroosmotic and pressure-driven mechanisms, but also serve as a benchmark for validating computational fluid dynamics models for electroosmotic flow simulations in more complex flow domains. Moreover, the analytical approach aids in the parametric analysis, providing deeper insights into the impact of physical parameters on electroosmotic and pressure-driven flow behavior, which is critical for optimizing device performance in practical applications. These findings also offer insightful implications for diagnostic and therapeutic strategies in healthcare, particularly enhancing the capabilities of lab-on-a-chip technologies and paving the way for future research in the development and optimization of microfluidic systems.

Numerical Investigation of Performance and Flow Characteristics of Francis Turbine in Part Load Condition

Abstract Due to the complexity of the flow phenomena inside Francis turbines, it is very time-consuming and expensive to determine their performance and flow characteristics under different operating conditions through model tests and field measurements. The use of computational fluid dynamics (CFD) to improve turbine design and performance is a more cost-effective alternative to traditional methods of forecasting flow characteristics in different flow domains across the whole turbine channel. The performance and flow characteristics of the turbine under various part load working circumstances are studied by numerical simulations employing the shear stress transfer SST (k − ω) turbulence model. Four different operating conditions from 50%,70%,80% and 100% of the maximum and minimum power were analyzed, and the flow characteristics across the entire turbine channel were investigated in detail. The study results shows the calculated efficiencies of four operating condition have good agreement with measured ones, and the efficiency differences between numerical simulation and measurement are below 0.44%. Visualization of velocity and pressure in the whole Francis turbine shows that output torque, output power, and performance are in good agreement with experiment results. This study’s findings can be utilized to improve the design, efficiency, and hydraulic stability of Francis turbines.

Numerical Investigation on the Spatiotemporal Correlation between Hydraulic Loss and Vortex at Turbine Mode of a Pump-turbine

Abstract Hydraulic loss and vortex analysis are two most widely-used methods investigating flow characteristics from macroscopic view and microscopic view respectively although the correlation between these two methods are still not fully clarified. Based on kinetic energy equation and Boussinesq hypothesis, hydraulic loss is resulted from the joint work of the dissipation loss and the transportation loss in flow domain while vorticity can be further divided into local rigid rotational part and deformational part with the help of the newly proposed concept Liutex. Thereafter, enstrophy as well as vorticity transport intensity is selected as the count part of hydraulic loss through dimensional analysis. Finally, the spatial correlation between hydraulic loss and vortex evolution in small guide vane opening at turbine mode is analyzed with the help of SST k–ω model and the temporal correlation at runaway point is analyzed through DES model. For spatial correlation, the dissipation loss and transportation loss are mainly caused by the deformational enstrophy Ω S and the rigid vorticity transport intensity T R , respectively. For temporal correlation, the correlation order nearly remains unchanged while the degree of correlation decreases to some extent. Based on our work, the hydraulic loss caused by different structure of vortex can be quantified and compared.

Research on the spiral case and external concrete structural response under limit operation state

Abstract As the key component of hydroelectric generating units, the spiral case is primarily responsible to make the fluid flow out in a uniform and axisymmetric way. The structural response of the spiral case and external concrete under hydraulic pressure will have an effect on the operational security and power generation efficiency of the whole unit. In this paper, a three-dimensional fluid-structure coupling model of the spiral case and concrete under limit operation state is established. Based on the computational fluid mechanics theory, the flow domain inside the spiral case is simulated to study the stress and deformation structural response of the spiral case and external concrete under the action of maximum inner hydraulic pressure. The value and position of the maximum stress and deformation are determined. The fluid-structure coupling simulation shows that severe turbulence occurs in the middle of the flow channel. The maximum equivalent stress of the spiral case is located near its tongue and there would possibly be a risk of local structural damage. The external concrete can effectively share part of the load from the spiral case, so as to keep the strength and stiffness of the overall structure being within safe levels under the limit state. The results can provide support to the structural optimization and construction of the spiral case, which is helpful for the safety and stability of station operation.

Application of machine learning to model the pressure poisson equation for fluid flow on generic geometries

This study addresses the importance of enhancing traditional fluid-flow solvers by introducing a Machine Learning procedure to model pressure fields computed by standard fluid-flow solvers. The conventional approach involves enforcing pressure–velocity coupling through a Poisson equation, combining the Navier–Stokes and continuity equations. The solution to this Poisson equation constitutes a substantial percentage of the overall computational cost in fluid flow simulations, therefore improving its efficiency can yield significant gains in computational speed. The study aims to create a versatile method applicable to any geometry, ultimately providing a more efficient alternative to the conventional pressure solver. Machine Learning models were trained with flow fields generated by a Computational Fluid Dynamics solver applied to the confined flow over multiple geometries, namely wall-bounded cylinders with circular, rectangular, triangular, and plate cross-sections. To achieve applicability to any geometry, a method was developed to estimate pressure fields in fixed-shape blocks sampled from the flow domain and subsequently assemble them to reconstruct the entire physical domain. The model relies on multilayer perceptron neural networks combined with Principal Component Analysis transformations. The developed Machine Learning models achieved acceptable accuracy with errors of around 3%. Furthermore, the model demonstrated enhanced computational efficiency, outperforming the classical PISO algorithm by up to 30 times.

Axial Green Function Method in an Efficient Projection Scheme for Incompressible Navier–Stokes Flow in Arbitrary Domains

We introduce a new approach to solving incompressible Navier–Stokes flow. This method combines a projection scheme with the Axial Green Function Method (AGM). Based on the Kim and Moin methods, our methodology employs a predictor–corrector mechanism to achieve stable and accurate velocity corrections. Using axial Green functions, we transform complex differential equations into simpler one-dimensional integral equations. These are strategically placed along a minimal axis-parallel lines, known as axial lines, within the flow domain. This transformation makes computation and analysis more efficient from a numerical viewpoint. A significant innovation in our approach is using one-dimensional axial Green functions tailored explicitly for the reaction–diffusion ordinary differential operator. These functions efficiently handle the discrete-time derivative and viscous terms of the momentum equation. Furthermore, our approach allows for the arbitrary construction of axis-parallel lines, facilitating analysis near critical flow regions and even enabling the random distribution of these lines. We validate our proposed method through numerical examples, demonstrating the convergence of numerical solutions, the effectiveness of arbitrarily constructed axis-parallel lines, and the potential extension of our method to three-dimensional flow problems. Additionally, this study provides a robust and adaptable alternative way to solve incompressible Navier–Stokes flows, proving its effectiveness in practical applications such as Tesla valves.

How to consider groundwater flow systems in the Earth's Critical Zone? – Demonstration in the Central Pannonian Basin, Hungary

Study regionCentral Pannonian Basin, Hungary. Study focusCritical Zone (CZ) Science generally focuses on the soil and weathered bedrock in a few or tens of meters depth, thus influence of deeper groundwater on the CZ is understudied. Here we aim to introduce a hydrogeological methodology that can separate normal and abnormal pressure regimes and determine the groundwater flow pattern to characterize the connection of different groundwater flow systems to the CZ. Basin-scale evaluation of about 5500 measured hydraulic data were carried out by p(z) and h(z) profiles, tomographic maps and hydraulic cross sections. New hydrological insights for the RegionThree flow domains were separated and characterized. Namely, i) the uppermost topography-driven flow systems, which penetrate only a few hundred meters, ii) a deep overpressured regime below 1600–2100 m depth, which drives fluids upward; and iii) a newly identified transition zone between the former two, which gains its energy from overpressure dissipation and contains non-renewable water resources. Topography-driven flow systems and discharge areas of the transition zone, where its upwelling saline water contributes to surface salinization, are parts of the CZ. Discharge areas of the transition zone cover about 50% of the Great Hungarian Plain. The overpressured system can only influence the CZ through the transition zone. The approach and methodology can be used in any terrestrial sedimentary basin where a deep overpressured regime exists.

A finite element framework for fluid–structure interaction of turbulent cavitating flows with flexible structures

We present a finite element framework for the numerical prediction of cavitating turbulent flows interacting with flexible structures. The vapor–fluid phases are captured through a homogeneous mixture model, with a scalar transport equation governing the spatio-temporal evolution of cavitation dynamics. High-density gradients in the two-phase cavitating flow motivate the use of a positivity-preserving Petrov–Galerkin stabilization method in the variational framework. A mass transfer source term introduces local compressibility effects arising as a consequence of phase change. The turbulent fluid flow is modeled through a dynamic subgrid-scale method for large eddy simulations. The flexible structure is represented by a set of eigenmodes, obtained through a modal decomposition of the linear elasticity equations. While a partitioned iterative approach is adopted to couple the structural dynamics and cavitating fluid flow, the deforming flow domain is described by an arbitrary Lagrangian–Eulerian frame of reference. We establish the fidelity of the proposed framework by comparing it against experimental and numerical studies for both rigid and flexible hydrofoils in cavitating flows. Under unstable partial cavitating conditions, we identify specific vortical structures leading to cloud cavity collapse. We further explore features of cavitating flow past a rigid body such as re-entrant jet and turbulence-cavity interactions during cloud cavity collapse. Based on the validation study conducted over a flexible NACA66 rectangular hydrofoil, we elucidate the role of cavity and vortex shedding in governing the structural dynamics. Subsequently, we identify a broad spectrum frequency band whose central peak does not correlate to the frequency content of the cavitation dynamics or the natural frequencies of the structure, indicating the induction of unsteady flow patterns around the hydrofoil. Finally, we discuss the coupled fluid–structure dynamics during a cavitation cycle and the underlying mechanism associated with the promotion and mitigation of cavitation.

An Enhanced Mapping Interpolation ISPH–FVM Coupling Method for Simulating Two-Phase Flows with Complex Interfaces

In this paper, an enhanced mapping interpolation ISPH–FVM coupling method is developed to improve the accuracy and stability of two-phase flows simulation. Similar to the original ISPH–FVM coupling method, the coupling framework between the numerical schemes of ISPH and FVM is established at the overlapping region of the ISPH particles and the FVM grids. Although the original ISPH–FVM coupling method can effectively predict movement and deformation of the two-phase interface, it is difficult to ensure the mass conservation in the flow domain during the information transfer between ISPH particles and FVM grids, thus weakening the numerical accuracy of interaction between different fluid phases. To improve the mapping interpolation accuracy and ensure the mass conservation, a volume fraction correction scheme combined with the particle approximate interpolation technique is introduced to form an enhanced mapping interpolation ISPH–FVM coupling method. The effect of surface tension is assessed by the continuum surface force (CSF) model. Several 2D/3D numerical cases are given to verify the efficiency and robustness of the present ISPH–FVM coupling method. Numerical results demonstrate that the enhanced mapping interpolation developed in the ISPH–FVM coupling method can reproduce the two-phase flow with complex interfaces effectively.

Visualization and Analysis of Mapping Knowledge Domain of Fluid Flow Related to Microfluidic Chip.

Microfluidic chips are important tools to study the microscopic flow of fluid. To better understand the research clues and development trends related to microfluidic chips, a bibliometric analysis of microfluidic chips was conducted based on 1115 paper records retrieved from the Web of Science Core Collection database. CiteSpace and VOSviewer software were used to analyze the distribution of annual paper quantity, country/region distribution, subject distribution, institution distribution, major source journals distribution, highly cited papers, coauthor cooperation relationship, research knowledge domain, research focuses, and research frontiers, and a knowledge domain map was drawn. The results show that the number of papers published on microfluidic chips increased from 2010 to 2023, among which China, the United States, Iran, Canada, and Japan were the most active countries in this field. The United States was the most influential country. Nanoscience, energy, and chemical industry and multidisciplinary materials science were the main fields of microfluidic chip research. Lab on a Chip, Microfluidics and Nanofluidics, and Journal of Petroleum Science and Engineering were the main sources of papers published. The fabrication of chips, as well as their applications in porous media flow and multiphase flow, is the main knowledge domain of microfluidic chips. Micromodeling, fluid displacement, wettability, and multiphase flow are the research focuses in this field currently. The research frontiers in this field are enhanced oil recovery, interfacial tension, and stability.