Complex Fluid Flow Research Articles

Role of Surface-Active Element Sulfur on Thermal Behavior, Driving Forces, Fluid Flow and Solute Dilution in Laser Linear Welding of Dissimilar Metals

Understanding heat and mass transfer and fluid flow in the molten pool is very helpful in the selection and optimization of processing parameters, and the surface-active element has an important effect on the heat and mass transfer in laser welding of dissimilar metals. A three-dimensional (3D) numerical model coupled with a sub-model of surface tension, which considers the influence of local temperature and the concentration of surface-active element sulfur at the gas/liquid surface, is used to analyze the thermal behavior, driving forces, fluid flow, and solute dilution during laser linear welding of 304SS and Ni. The relationship between surface tension, driving forces, and the temperature coefficient of surface tension with the spatial distribution of temperature and the surface-active element sulfur is quantitatively analyzed. The simulation results show that the molten pool is fully developed at 45 ms, and the collision of inward and outward convection, with the maximum velocity reaching 1.7 m/s, occurs at the isotherm with a temperature between 2200 K and 2500 K. The temperature-gradient term and concentration-gradient term of surface shear stress play different roles in different positions of the free surface. The local sulfur concentration changes the temperature sensitivity of the surface tension at different sides of the free surface and further determines the transition of convection. Complex fluid flow promotes solute dilution, and the distribution of solute becomes uniform from the front to the rear of the molten pool. The Ni element is transferred to 304SS mainly at the rear side. The work provides theoretical support for the control of joint quality by changing the content of surface-active elements in dissimilar welding.

RETRACTED ARTICLE: Biological interactions between micro swimmers and cross fluid with inclined MHD effects in a complex wavy canal

The significance of studying biological interactions of micro swimmers in a complex wavy canal with MHD effects lies in its potential to provide insight into the behavior and dynamics of microorganisms in natural environments that contain complex fluid flow and magnetic fields. Current investigation explores the biological interactions between micro swimmers and Cross fluid with inclined MHD effects in a complex wavy canal. There are several factors of medium for micro swimmers like nature, magnetic field, liquid rheology and position. These interactions are hereby witnessed by utilizing the Cross fluid along with Taylor swimming sheet model under the influence of porous and inclined magnetic effect. Furthermore, two-dimensional complex wavy cervical canal is being utilized at inclined angle. By help of lubrication assumption, reduction of momentum equation is made and hence fourth-order differential equation associated with boundary conditions is obtained. Bvp4c command in Matlab is utilized for this boundary value problem. Obtained bvp4c solution is verified with finite difference method and found smooth agreement. Magnetic force enhances the swimming speed and reduction is seen for the power dissipation and effective role is seen for swimming motion with cross fluid rheology. The channel walls (peristaltic nature) and porous medium can be utilized as alternative factors to control the speed of the propeller.

Shear-thickening suspensions down inclines: from Kapitza to Oobleck waves

We investigate experimentally and theoretically the stability of a shear-thickening suspension flowing down an inclined plane. In a previous paper (Darbois Texier et al., Commun. Phys., vol. 3, 2020), we have shown that for particle volume fractions $\phi$ above the discontinuous shear-thickening fraction $\phi _{DST}$ , long surface waves grow spontaneously at a flow Reynolds number much below 1. This motivated a simplified analysis based on a purely inertialess mechanism, called the ‘Oobleck waves’ mechanism, which couples the negatively sloped rheology of the suspension with the free-surface deflection and captures well the experimental instability threshold and the wave speed, for $\phi >\phi _{DST}$ . However, neglecting inertia does not allow us to describe the inertial Kapitza regime observed for $\phi <\phi _{DST}$ , nor does it allow us to discriminate between Oobleck waves and other inertial instabilities expected above $\phi _{DST}$ . This paper fills this gap by extending our previous analysis, based on a depth-averaged approach and the Wyart–Cates constitutive shear-thickening rheology, to account for inertia. The extended analysis recovers quantitatively the experimental instability threshold in the Kapitza regime, below $\phi _{DST}$ , and in the Oobleck waves regime, above $\phi _{DST}$ . By providing additional measurements of the wave growth rate and investigating theoretically the effect of a strain delay in the rheology, it also confirms that the instability observed above $\phi _{DST}$ stems from the non-inertial Oobleck wave mechanism, which is specific to free-surface flows and dominates modes of inertial origin. These results emphasize the variety of instability mechanisms for shear-thickening suspensions and might be relevant to free-surface flows of other complex fluids displaying velocity-weakening rheology.

Analysis of fluid dynamic behavior and impact load on oblique water entry of a two-dimensional seaplane based on VOF method

Seaplane landing is a typical water entry impact phenomenon. However, our understanding on the development of jet flow and air cavity, and corresponding load characteristic is still limited. In this paper, the impact problem of oblique water entry of a two-dimensional seaplane is studied to clarify the relationship between theses complex flows and loads. A gas-liquid two-phase model is established to model these complex fluid flows, where the air-water interface is captured based on a VOF algorithm. The accuracy is validated by the published experimental data. Some hydrodynamic impact characteristics related to seaplane model under different oblique water entry postures, including air-water free surface and local impact pressure are discussed. Complicated flows (air cavity and jet flow impact) are the internal cause to some unusual load characteristics such as multiple pressure peaks, negative pressure, simultaneity of impact load on the airfoil, and great difference of load at different positions on the cabin. The evolution of jet flow and air cavity is further discussed through combing free surface and pressure distribution. Results clarified the mechanism of complex flows under the influence of bottom cabin and inclined postures, which will be helpful to further understand the impact phenomenon of seaplane landing.

A New Insight into the Flow and Pressure Behavior of a Stopper Rod Flow Control System

A previously unnoticed anomaly is recently identified in a stopper rod flow control system for the continuous casting of steel by comparing full‐scale water model experiments with plant data. Despite extensive research previously done on the continuous casting fluid flow phenomena, this anomaly and its background physics are not well understood by researchers. Computational fluid dynamics (CFD) modeling of the molten steel flow in the stopper rod system is performed with predictions matching closely with water model data but deviating from plant measurements. It is thus suggested by this comparison that certain aspects of the liquid steel flow and pressure behavior in the stopper rod system cannot be effectively described by traditional modeling techniques due to their embedded limitations. Through water modeling, CFD simulations, plant measurements, and theoretical analysis, the current study for the first time presents a thorough discussion of this observed anomaly, which brings new insights into understanding the complex fluid flow behavior and pressure distributions inside the stopper rod control system during the continuous casting process.

Theoretical and experimental research on two-phase flow image reconstruction and flow pattern recognition.

Two-phase flow is a kind of complex fluid flow state, and the flow pattern characteristics are very difficult to obtain accurately. First, the principle of two-phase flow pattern image reconstruction based on electrical resistance tomography technology and the complex flow pattern recognition method are developed. Next, the back propagation (BP), wavelet, and radial basis function (RBF) neural networks are applied to the two-phase flow pattern image identification process. The results show that the RBF neural network algorithm has higher fidelity and faster convergence speed than the BP and wavelet network algorithms, and the fidelity is more than 80%. Then, deep learning of the pattern recognition algorithm fusing the RBF network and convolution neural network is proposed to improve the precision of the flow pattern identification. Additionally, the recognition accuracy of the fusion recognition algorithm is more than 97%. Finally, a two-phase flow test system is constructed, the test is finished, and the correctness of the theoretical simulation model is verified. The research process and results provide important theoretical guidance for the accurate acquisition of two-phase flow patterns.

A reduced-order model of concentration polarization in reverse osmosis systems with feed spacers

Feed spacers in reverse osmosis systems generate complex fluid flows that limit computational fluid dynamics (CFD) simulations to small length and time scales. That limits our ability to simulate mineral scaling and other membrane fouling phenomena, which occur over longer length and time scales. Thus motivated, we develop a reduced model that replaces the CFD simulation of the velocity field with an analytical model that mimics spacers. This focuses the remaining numerical effort on simulating the advection–diffusion equation governing solute transport. We motivate and validate the model with CFD simulations and bench-scale experiments of spacer filaments in three different arrangements, including cases of unsteady vortex shedding. We show that the model produces a roughly 10,000-fold speedup compared to CFD, and accurately reproduces CFD predictions of not only the average and maximum concentrations, but also the local concentration distribution along the membrane. We also demonstrate the model for simulating a feed channel with a length-to-height ratio of 200. The model provides a simple testbed for exploratory studies of multispecies transport, precipitation, and membrane fouling phenomena for which simulating spacers is often prohibitive.

Pore Structure Evolution in Sandstone of Underground Gas Storage during Cyclic Injection and Production Based on Nuclear Magnetic Resonance Technology

The underground gas storage (UGS) in depleted sandstone reservoirs forms the largest proportion of the UGS market in China. Multiple cycles of natural gas injection and production in the sandstone cause the rapid increase and drawdown of pore pressure, which may induce damage to the rock skeleton structure, and cause complex fluid flow paths in the sandstone reservoir. In this paper, transverse relaxation time (T2), nuclear magnetism resonance imaging, and high-pressure mercury intrusion analysis are combined to evaluate the variation in pore structure of medium-grained sandstone. The results show that cyclic injection and production of fluid leads to a slight increase in total pore volume, indicating that weak damage to rocks occurs. The T2 spectrum at the low pore pressure (10 MPa) and high pore pressure (25 MPa) both show that the shrinkage of the medium-size pores occurs after multiple cycles of injection and production. The pore volume of large-size pores was not highly correlated with the number of cycles. With the increase in pore pressure, the pore volume ratio under high pore pressure increased with the number of cycles, while it fluctuated strongly under low pore pressure.

An energy-stable Smoothed Particle Hydrodynamics discretization of the Navier-Stokes-Cahn-Hilliard model for incompressible two-phase flows

Varieties of energy-stable numerical methods have been developed for incompressible two-phase flows based on the Navier-Stokes–Cahn–Hilliard (NSCH) model in the Eulerian framework, while few investigations have been made in the Lagrangian framework. Smoothed particle hydrodynamics (SPH) is a popular mesh-free Lagrangian method for solving complex fluid flows. In this paper, we present a pioneering study on the energy-stable SPH discretization of the NSCH model for incompressible two-phase flows. We prove that this SPH method inherits mass and momentum conservation and the energy dissipation properties at the fully discrete level. With the projection procedure to decouple the momentum and continuity equations, the numerical scheme meets the divergence-free condition. Some numerical experiments are carried out to show the performance of the proposed energy-stable SPH method for solving the two-phase NSCH model. The inheritance of mass and momentum conservation and the energy dissipation properties are verified numerically.

Unravelling the impact of geological heterogeneities in the reservoir management. An integrated study of the deep-water Akpo field (Niger delta)

Geological and seismic data have been integrated to describe the impact of structural and sedimentological heterogeneities on fluid dynamics for one of the reservoirs (Reservoir X) of the Akpo field (deep offshore of Niger Delta). Akpo is a sinuous 4-way anticline formed because of strong deformation occurring within the over pressurized shales of the Akata Formation. Weak deformation may have started since 9 Ma; however, most of the strain associated with shortening was accumulated between ca 7 Ma and ca 3 Ma. The cumulative shortening has been estimated in the order of 4% (ca 400 m). From 3 Ma (during the latest stages of growth of the Akpo anticline) up to Present-day, the residual strain was taken up by oblique (right lateral strike-slip component) extensional faults, between 2 km and ca 15 km in length, organized into two main trends: N-dipping, ENE-WSW to NE-SW striking faults, and NNW-SSE striking faults mainly dipping to the east. These faults are segmented and have a maximum throw in the order of 70 m. Based on the architectural elements identified in this work, the Reservoir X of the Akpo field has been subdivided into three main intervals: a basal lobe (Interval 1a) and a related channel (Interval 1 b); two intermediate generations of meandering channels (Interval 2a and 2 b); and an upper and strongly erosional constructive channel (Interval 3). The 4D signal has been integrated to better understand the complex fluid flow within Reservoir X in time and space. The extensional faults play a role in baffling mainly in the central part of the field, where across-fault flow is likely to occur in correspondence with the spill points. Facies distribution and the nature of the contact between various reservoir intervals are also important because vertical and lateral communication between sand bodies can be complex. Levees or mud barriers can inhibit flow, impacting both efficiency of support between injectors and producers. On the other hand, sand-to-sand communication due to the strong erosional character of the contacts enhances dynamic connectivity between sands belonging to different reservoir intervals. As an example, even if sands belonging to Interval 3 totally erode (i.e. dynamically isolate) those of Interval 2 in the southern part of the field, in the northern sector the two reservoir intervals are in (vertical) communication as suggested by the 4D signal. A key result of this integrated work is that of documenting in detail the modes in which faults and sedimentary features impact fluid flow within and across reservoir intervals in a deep-water environment.

Phase Change Materials Energy Storage Enhancement Schemes and Implementing the Lattice Boltzmann Method for Simulations: A Review

Utilizing phase change materials (PCMs) is one of the most effective methods of storing thermal energy and is gaining popularity in renewable energy systems. In order to analyze PCM performance, various numerical methods have been deployed to study the transient behaviour during phase changes. PCMs’ low thermal conductivity prevents their use as pure PCMs in industrial applications. There are various efficient methods of enhancing PCM thermal conductivity, which are addressed in this article. On the other hand, the lattice Boltzmann method (LBM) is very inclusive in the numerical simulation of complex fluid flows, thermal transport, and chemical interactions because of its ability to simply represent various complex physical phenomena, suitability for parallel programming, and easy coding and implementation. Many numerical studies have been conducted on PCMs using the LBM. This study aims to review these studies and categorize them in a way so that one may thoroughly understand the LBM’s capabilities in the simulation of PCM-related investigations. First, PCM characteristics and applications are presented, then the LBM implementation in PCM problems is addressed. Afterward, the fabrication and types of PCMs are mentioned. Next, the improvement of thermal energy storage methods of PCMs is stated. Furthermore, governing equations are reviewed. Lastly, the opportunities and challenges of the LBM in PCMs are discussed.

Machine Learning Prediction of Nanoparticle Transport with Two-Phase Flow in Porous Media

Reservoir simulation is a time-consuming procedure that requires a deep understanding of complex fluid flow processes as well as the numerical solution of nonlinear partial differential equations. Machine learning algorithms have made significant progress in modeling flow problems in reservoir engineering. This study employs machine learning methods such as random forest, decision trees, gradient boosting regression, and artificial neural networks to forecast nanoparticle transport with the two-phase flow in porous media. Due to the shortage of data on nanoparticle transport in porous media, this work creates artificial datasets using a mathematical model. It predicts nanoparticle transport behavior using machine learning techniques, including gradient boosting regression, decision trees, random forests, and artificial neural networks. Utilizing the scikit-learn toolkit, strategies for data preprocessing, correlation, and feature importance are addressed. Furthermore, the GridSearchCV algorithm is used to optimize hyperparameter tuning. The mean absolute error, R-squared correlation, mean squared error, and root means square error are used to assess the models. The ANN model has the best performance in forecasting the transport of nanoparticles in porous media, according to the results.

Numerical study on the effect of turbulence intensity on the separation behavior of magnetic particles under different magnet arrangements

ABSTRACT The crucial problem of magnetic separation is improving the ability to capture magnetic particles in complex fluid flow. This paper develops a numerical model of an open gradient magnetic separation (OGMS) system to study the capture process of magnetic particles in laminar flow and turbulent flow under four kinds of magnet arrangement modes. The particle capture efficiencies of four inlet turbulence intensities are analyzed and compared with that of laminar flow. The results show that the particle capture efficiency of turbulent flow is higher than that of laminar flow under the same inlet velocity distribution in a small Stokes number. Moreover, the particle capture efficiency of high turbulence intensity is significantly higher than that of low turbulence intensity. The results also indicate that the axial alternating arrangement (AAA) can provide a stronger magnetic field, which is beneficial to particle capture.

A note about convected time derivatives for flows of complex fluids.

We present a direct derivation of the typical time derivatives used in a continuum description of complex fluid flows, harnessing the principles of the kinematics of line elements. The evolution of the microstructural conformation tensor in a flow and the physical interpretation of different derivatives then follow naturally.

Lattice Boltzmann model for freezing of French fries

In this paper we present a Lattice Boltzmann model for food freezing, using the enthalpy method. Simulations are performed using the case study of freezing par-fried french fries. The action of par-frying leads to moisture removal from the crust region, which was treated via the initial conditions for the freezing model. Simulations show that under industrial-relevant freezing conditions, the crust region remains either unfrozen or only partially frozen. This result is important for the practical quality problem of dust, which is the phenomenon of fracturing of the crust during finish-frying.Next to the insight, the Lattice Boltzmann freezing model rendered for the case study of par-fried french fries, we pose that this freezing application is a comprehensive tutorial problem, via which food scientists can be conveniently introduced to the Lattice Boltzmann method. Commonly, the Lattice Boltzmann method has its value in solving complex fluid flow problems, but the complexity of these problems is possibly withholding food scientists to get familiar with the method. Our freezing is solved in 2D, and on a simple square lattice with only 5 particle velocities (a D2Q5 lattice). We hope via this simple tutorial problem, the Lattice Boltzmann method becomes more accessible.

Modelling of rheologically complex fluid flow in hard-to-recover fractal reservoirs

Modelling of rheologically complex fluid flow in hard-to-recover fractal reservoirs

Experimental and CFD Analysis on the Effect of Various Cold Orifice Diameters and Inlet Pressure of a Vortex Tube

An experimental investigation was conducted to investigate the effects of different cold orifice diameters and operating pressures of the vortex tube. A vortex tube test rig was employed to conduct the experiments for various cold orifice diameters and operating pressures. Cold orifice diameters range from 1 mm to 6 mm, whereas the pressure condition ranges from 2 to 5 bar. The vortex generators were made up of brass material having six inlet nozzles. It was found that the temperature separation of the vortex tube significantly depends on the cold orifice diameter of the vortex tube and operating pressure. The study demonstrates the deviation of cold temperature separation with respect to the cold orifice diameters and inlet pressure for different cold mass fractions. In addition, present experimental results are used to determine the optimum cold orifice diameter, which is 5 mm at 5 bar inlet pressure. The percentage improvement in average cold temperature separation for 5 mm cold orifice diameter is 66.18% compared to rest of the cold orifice diameters at an inlet pressure of 5 bar. The maximum cooling power separation is 0.08 kW at 0.3 cold mass fraction and inlet pressure of 5 bar. The CFD technique was approached to discuss the complex fluid flow inside the tube at various radial distances. A three-dimensional numerical study was done and validated with the present experimental work. It was found that the numerical results are in good agreement with the present experimental data.

Subgrid Variational Optimized Optical Flow Estimation Algorithm for Image Velocimetry

The variational optical flow model is used in this work to investigate a subgrid-scale optimization approach for modeling complex fluid flows in image sequences and estimating their two-dimensional velocity fields. To solve the problem of lack of sub-grid small-scale structure information in variational optical flow estimation, we combine the motion laws of incompressible fluids. Introducing the idea of large eddy simulation, the instantaneous motion can be decomposed into large-scale motion and a small-scale turbulence in the data term. The Smagorinsky model is used to model and solve the small-scale turbulence. The improved subgrid scale Horn-Schunck (SGS-HS) optical flow algorithm provides better results in velocity field estimation of turbulent image sequences than the traditional Farneback dense optical flow algorithm. To make the SGS-HS algorithm equally competent for the open channel flow measurement task, a velocity gradient constraint is chosen for the canonical term of the model, which is used to improve the accuracy of the SGS-HS algorithm in velocimetric experiments in the case of the relatively uniform flow direction of the open channel flow field. The experimental results show that our algorithm has better performance in open channel velocimetry compared with the conventional algorithm.

Solution of Unsteady Flow Equations for Nanoscale Heat and Mass Transfer in Diverse Applications

Abstract A new innovative stable time-dependent compressible flow solution over the order of nanoseconds is provided here for wide-ranging critically important challenging applications. Specifically, a solution of the highly complex unsteady high speed oscillating compressible flow field inside a cylindrical tube, closed at one end with a piston oscillating at very high resonant frequency at the other end, has been obtained numerically, assuming one-dimensional, viscous, and heat-conducting flow, by solving the appropriate fluid dynamic and energy equations. An iterative implicit finite difference scheme is employed to obtain the solution. The scheme permits arbitrary boundary conditions at the piston and the end wall and allows assumptions for transport properties. In successfully predicting the time-dependent results/data, an innovative simple but stable solution of unsteady fluid dynamic and energy equations is provided here for wide ranging research, design, development, analysis, and industrial applications in solving a variety of complex fluid flow heat transfer problems. The method is directly applicable to pulsed or pulsating flow and wave motion thermal energy transport, fluid-structure interaction heat transfer enhancement, nanoscale heat and mass transfer, diverse range of advanced fluidics, biofluidics/bio-engineering, and fluidic pyrotechnic initiation devices. It can further be easily extended to cover muzzle blasts and high energy nuclear explosion blast wave propagations in one-dimensional and/or radial spherical coordinates with or without including energy generation/addition terms. No other solution exists for such applications.

CNN-Based Fluid Motion Estimation Using Correlation Coefficient and Multiscale Cost Volume

Motion estimation for complex fluid flows via their image sequences is a challenging issue in computer vision. It plays a significant role in scientific research and engineering applications related to meteorology, oceanography, and fluid mechanics. In this paper, we introduce a novel convolutional neural network (CNN)-based motion estimator for complex fluid flows using multiscale cost volume. It uses correlation coefficients as the matching costs, which can improve the accuracy of motion estimation by enhancing the discrimination of the feature matching and overcoming the feature distortions caused by the changes of fluid shapes and illuminations. Specifically, it first generates sparse seeds by a feature extraction network. A correlation pyramid is then constructed for all pairs of sparse seeds, and the predicted matches are iteratively updated through a recurrent neural network, which lookups a multi-scale cost volume from a correlation pyramid via a multi-scale search scheme. Then it uses the searched multi-scale cost volume, the current matches, and the context features as the input features to correlate the predicted matches. Since the multi-scale cost volume contains motion information for both large and small displacements, it can recover small-scale motion structures. However, the predicted matches are sparse, so the final flow field is computed by performing a CNN-based interpolation for these sparse matches. The experimental results show that our method significantly outperforms the current motion estimators in capturing different motion patterns in complex fluid flows, especially in recovering some small-scale vortices. It also achieves state-of-the-art evaluation results on the public fluid datasets and successfully captures the storms in Jupiter’s White Ovals from the remote sensing images.