Combined Computational Fluid Dynamics Research Articles

Dynamics and “falling deep” mechanism of submerged floating body under internal solitary waves

Internal solitary waves (ISWs) can result in notable alterations to the hydrodynamic characteristics of submerged bodies, affecting their motion and even leading to serious “falling deep” accidents. This study utilizes a combination of Computational Fluid Dynamics (CFD) and the modified Korteweg-de Vries (mKdV) theory. By combining techniques such as the Volume of Fluid (VOF) model and overset grid, a numerical method for simulating the interaction between ISWs and submerged bodies is established. The dynamics of submerged floating bodies under internal solitary waves are systematically investigated. The results demonstrate that the primary cause of changes in motion and force is the rotating velocity field induced by ISWs. Additionally, the influences of different locations and internal solitary wave amplitudes on the hydrodynamic characteristics of the submerged body are examined. The submerged body is more likely to experience “falling deep” due to the influence of ISWs when it is situated in the lower layer of the fluid.

Improvement of turbulent flow in a bend using rotational guide vane: A shape optimization

The present study aims to minimize velocity disruption using rotational guide vane (RGV) before the 90° pipe bend where the curvature ratio is r/D = 1.0 at Re = 3 × 104. The combination of computational fluid dynamics and genetic algorithm is used as a tool for the geometric optimization of RGV. The SST k–ω turbulence model was preferred in flow analysis. The number of blade, the blade angle, and the location of RGV are the geometric parameters to be optimized. A new evaluation method of the velocity distribution after the bend is proposed. The objective function is defined as velocity distribution mean squared error that is based on the comparison of the velocity distribution on the cross section after the bend with the fully developed flow in a straight pipe under the same conditions. As a result of a series of optimization processes, velocity distribution at the 90° bend exit is compared between optimized RGV and without guide vane. The results show that the optimized RGV improves velocity distribution at the bend exit. Compared to the without guide vane case, the optimized RGV has delayed flow separation from α = 37° to 56°, and the flow reattachment point moves from x/D = 0.5 to 0 at the bend exit. Thanks to optimized RGV, the counter-rotating Dean vortices merged to form a single vortex at the center of the pipe. In addition, significant reduction in turbulent kinetic energy was observed, approximately 50% when using the optimized RGV compared to the without guide vane case.

Computational fluid dynamics analysis of the effects of total weight and food on the water temperature distribution and flow in a pot during cooking

AbstractUniform quality of cooked foods regardless of its position in the pot is desirable. However, there are no research about the effect of cooking scale on the flow and water temperature distribution. This study investigated the effects of the weight and the presence of food on the water temperature distribution and flow during cooking. Using nylon cylinders with thermophysical properties similar to those of root vegetables, the water temperature near the inner surface of the pot was measured. The water temperature and flow were simulated with 2D models based on the finite element method. The water temperature was highly uniform at a total weight of 20 kg than at 3 kg due to the greater convection and faster flow velocity. The water flow velocity was lower with nylon cylinders than in its absence and decreases with decreasing sample size. This study demonstrates the usefulness of combining simulations with nylon cylinders.Practical applicationsThe evaluation of temperature distribution and water flow during cooking is important for ensuring uniform quality of products in a pot. Particularly, it is important to evaluate the effect of cooking scale, presence of food and ingredient size. However, the experimental verification of large‐scale cooking is costly and time‐consuming. This study shows the usefulness of combining computational fluid dynamics (CFD) simulation with the nylon cylinders having thermophysical properties similar to those of root vegetables as food analogs. The CFD analysis revealed that increasing cooking scale increases the water flow velocity and uniformity of water temperature. This result should be considered in the actual large‐scale cooking of prototypes, which are tested at small scale, for example, in laboratories. Furthermore, small‐sized foods hinder the flow and cause uneven water temperature distribution. The findings suggest that sample size affects the temperature and uniformity in quality of cooked products in a pot.

Resolved CFD-DEM Simulation of Free Settling of Polyhedral Particles with Various Orientations: Insights Provided by Oscillation Behavior in Quiescent Liquid

Particle settling is the process by which particulates move toward the bottom of a liquid, which can affect the sediment transport and energy balance of marine systems. However, the deficiency in understanding the resolved fluid–particle interactions with complex boundaries in the settling process awaits resolution. This study employs a hybrid approach that combines computational fluid dynamics (CFD) with the discrete element method (DEM) to fully simulate the free-settling behavior of polyhedral particles in water. The accuracy of the method is verified by comparing numerical results with experimental data of ellipsoidal particle settling. Two series of tests with horizontal and vertical particle release directions are established to investigate the effects of particle shape features, such as the aspect ratio (AR) and corner (C), on the particles’ mechanical behavior and hydrodynamic characteristics. The results demonstrate that particle shape exerts a substantial influence on fluid resistance, rotational motion, and fluid disturbance throughout the settling process. The maximum velocities in vertically released cases are roughly 1.2–1.3 times greater than those in horizontally released cases. The study highlights the potency of the resolved CFD-DEM method as a robust technique for comprehending fluid–particle phenomena within the marine geotechnical engineering, including sedimentation and erosion of seabed sediments.

Development of an Intelligent Passive Device Generator for Road Vehicle Applications

Flow control has a tremendous technological and economic impact, such as aerodynamic drag reduction on road vehicles which translates directly into fuel savings, with a consequent reduction in greenhouse gas emissions and operating costs. In recent years, machine learning has also been used to develop new approaches to flow control in place of more laborious methods, such as parametric studies, to find optimal parameters with few exceptions. This paper proposes an intelligent passive device generator (IPDG) that combines computational fluid dynamics (CFD) and genetic algorithm, more specifically, the Non-dominated Sorting Genetic Algorithm II (NSGA II). The IPDG is not application specific and can be applied to generate various devices in the given design space. In particular, it creates three-dimensional passive flow control devices with unique shapes that are aerodynamically efficient in terms of the cost function (i.e., aerodynamic drag and lift). In this paper, the IPDG is demonstrated using a rear flap and an underbody diffuser as passive devices. The three-dimensional Reynolds-averaged Navier-stokes (RANS) equations were used to solve the problem. Relative to the baseline, the IPDG generated flap-only, and diffuser-only provide drag reductions of 6.3% and 5.4%, respectively, whereas the flap-diffuser combination provides a drag reduction of 7.4%. Furthermore, the increase in the downforce is significant from 624.4% in flap-only to 4930% and 4595% in the diffuser and flap-diffuser combination. The proposed method has the potential to evolve into a universal passive device generator with the integration of machine learning.

Performance improvement of CO2 two-phase ejector by combining CFD modeling, artificial neural network and genetic algorithm

The ejector, as one of the core components of the CO2 trans-critical ejection refrigeration system, plays an important role in improving refrigeration capacity and reducing compressor power consumption. Although the ejector can be optimized by experiment and Computational Fluid Dynamics (CFD) simulation to improve its performance, these methods are time-consuming and complicated. This study aims to propose a method to improve the performance of the CO2 trans-critical two-phase ejector assisted by using CFD, artificial neural network (ANN), and genetic algorithm (GA). Firstly, the influence of geometric parameters on ejector efficiency was analyzed by using CFD technology, and the database was generated. Next, the complex and time-consuming CFD model was replaced by the ANN surrogate model established to predict the ejector performance. Finally, the GA method was used to optimize the ejector to maximize the ejector efficiency. The results show that the efficiency of the optimized ejector is 35.39%. The optimized ejector increases the secondary flow velocity and eliminates the vortex in ejector, and the efficiency of the optimized ejector is raised by more than 8% on average than that of initial ejector under different primary and secondary flow conditions. The research shows that the combination of CFD, ANN, and GA has higher reliability and better performance in the optimization design of CO2 two-phase ejector.

Assessment of infection probability indices for airborne diseases in confined spaces: combination of CFD and analytical modelling

The paper presents a framework for probabilistic assessment of likelihood of infection from airborne diseases in confined spaces which are continuously occupied for relatively long periods (e.g., school classrooms). The proposed approach is based on a combination of computational fluid dynamics (CFD) pre-calculations and analytical post-processing, to define relevant indices of infection probability. The practical applicability of the method is demonstrated through a case study, where different ventilation scenarios are considered for a school classroom. Corresponding infection probability indices are determined globally for the group of occupants. Furthermore, since the method does not rely on the well-mixing assumption, local probability indices are determined for each occupied location. The obtained results confirm the intuition that an increase of ventilation and/or air filtration reduces the overall likelihood of infection, though the observed positive effect is not uniform within the space. The presented methodology can also be considered as complementary to simpler approaches.

Modeling the thermophoretic impact on nanoparticle production in an enclosed FSP reactor

For the accurate modeling and simulation of the flame spray pyrolysis (FSP) process, all the essential phenomena need to be taken into account. A validated model could then be used for process optimization and design with reasonably reduced costs. In that sense, this work uses a combination of computational fluid dynamics (CFD) and a population balance model (PBM) to investigate the influence of thermophoresis and other parameters (related to sintering and turbulent particle diffusion) in the final produced particle size. This work simulates the flame spray using CFD under an Eulerian–Lagrangian framework. Furthermore, a monodisperse solver for a bivariate PBM is implemented into ANSYS Fluent to simulate the formation and evolution of nanoparticles inside the reactor. We investigate the production of zirconium dioxide (ZrO2) nanoparticles and use experimental data from the literature for model validation. The model produces accurate results for some of the investigated cases.

Exploring the influence of nasal vestibule structure on nasal obstruction using CFD and Machine Learning method

Motivated by clinical findings about the nasal vestibule, this study analyzes the aerodynamic characteristics of the nasal vestibule and attempt to determine anatomical features which have a large influence on airflow through a combination of Computational Fluid Dynamics (CFD) and machine learning method. Firstly, the aerodynamic characteristics of the nasal vestibule are detailedly analyzed using the CFD method. Based on CFD simulation results, we divide the nasal vestibule into two types with distinctly different airflow patterns, which is consistent with clinical findings. Secondly, we explore the relationship between anatomical features and aerodynamic characteristics by developing a novel machine learning model which could predict airflow patterns based on several anatomical features. Feature mining is performed to determine the anatomical feature which has the greatest impact on respiratory function. The method is developed and validated on 41 unilateral nasal vestibules from 26 patients with nasal obstruction. The correctness of the CFD analysis and the developed model is verified by comparing them with clinical findings.

Combination of CFD and machine learning for improving simulation accuracy in water purification process via porous membranes

Membrane system for molecular separation was studied in this work using combined modeling approach. Computational fluid dynamics (CFD) was conducted and integrated to machine learning models for description of ozonation process in membrane contactors. For the machine learning modeling, we investigated the performance of boosted models, specifically AdaBoost KNN, AdaBoost DT, and AdaBoost ARD, for predicting the concentration (C) of ozone using the input variables, i.e., r and z. The hyper-parameter optimization is done using Successive Halving in this study. The results reveal that AdaBoost KNN achieves the highest performance among the three models, with an impressive R2 score of 0.9992. This indicates an excellent fit of the model to the data, implying that approximately 99.92% of the variance in the concentration can be explained by the input variables r and z. Moreover, AdaBoost KNN demonstrates a low RMSE of 1.5695E-02, indicating its ability to provide accurate predictions with small deviations from the actual values. The maximum error of 1.02733E-01 further confirms the model's robustness, as it represents the largest deviation between predicted and CFD values, which is relatively small.

Determination of Flame Plume Characteristics utilising CFD and Experimental Approaches

The potential for plumes to affect the safety of aircraft operations is often predicted using MITRE EPA Models. For many projects, key input parameters to MITER EPA are not available and conservative assumptions or models such as OHIO model are used to characterize the combustion and approximate the key input parameters to EPA plume rise model. These assumptions and conservative models lead to inaccurate results of simulations. The current study provides a novel approach to use a combination of Computational Fluid Dynamics (CFD) tool and EPA Models to reliably predict the risk of turbulence and upset being encountered by a range of aircraft types that operate through a rising plume. The main objective of the current study is to develop a Computational Fluid Dynamics (CFD) combustion model and a procedure to determine an improved set of flare inputs for the Air Quality (AQ) and MITER EPA models. A CFD model has been developed to determine flame plume characteristics (Effective Height, Effective Diameter, Temperature and Velocity) from two flare stacks which are part of a trailer-mounted Mobile Purge Burner (MPB) system. A subsequent experimental test of a similar trailer-mounted MPB system has validated the CFD results. Plume temperatures within the combustion zone of the flares were very much in line with the temperatures predicted by the CFD Simulation study. Plume temperatures above the MPB System appear to drop very quickly, such that the plume temperature fell from just under 500°C at 5 m above ground level to around 15-16°C (and close to the ambient temperature) at 22 m above ground. Again, this is consistent with the CFD Study results. The CFD simulations in the current study accounted for the turbulent flow with chemical species mixing and reaction and utilised an advanced radiation model to solve participating radiation in the combusted zones. This study assesses all the parameters that have impact on the accuracy of the numerical model including computational domain, mesh distribution, numerical scheme and flame plume characteristics including ambient conditions (wind speed and temperature) and combustion under various air to fuel ratio scenarios.

Formation and steady state characteristics of flow field effect in the header of a stripping prior to cutting combine harvester with CFD

In order to analyze the formation mechanism of the flow field in the header of a stripping prior to cutting combine harvester and compare the steady characteristics of the flow field generated by flat and concave combs, this study combined bench tests and computational fluid dynamics (CFD) to analyze them. During the formation of the flow field, the airflow produced “bottoming out” and “vortex” and other phenomena. At high speed, the maximum velocity of the airflow generated by the flat comb was 10.39 m s−1 and that of the concave comb was 10.59 m s−1; the flow field generated by the concave comb was better than that generated by the flat comb. Significant pulsation effects were generated in the airflow at the rice inlet and return outlet; the pulsation intensity was positively correlated with the pulsation mean velocity. At the return outlet, the turbulence of the airflow generated by the concave comb rotation was higher than that of the airflow generated by the flat comb rotation for both low and high speed conditions. This study explores the characteristics of stripping loss in the header of a stripping prior to cutting combine harvester from the perspective of flow field, which provides ideas for the structural optimization of a stripping prior to cutting combine harvester and the study of stripping loss reduction in the later stage, and also provides a reference for the rational use of air flow in the rotating domain in engineering applications.

BIO29: A Microfluidic Model Of Extracorporeal Circuit Thrombosis

Purpose: Cardiovascular medical devices such as extracorporeal membrane oxygenation (ECMO) machines are critical for the treatment and management of cardiorespiratory illnesses. However, blood contact with medical device biomaterials causes dangerous blood clot formation (thrombosis). In ECMO, blood clots can grow large enough to occlude circuitry and fail the device, or clots may dislodge and potentiate stroke and embolism in patients. While medical device thrombosis is clinically managed with systemic anticoagulation, thrombosis and its associated complications still exist, and anticoagulation frequently worsens patient outcomes by causing severe bleeding. There remains an urgent need to understand the mechanisms of biomaterial thrombosis and develop thromboresistant biomaterials. We hereby demonstrate a novel biomaterial thrombosis on-a-chip model system for real-time, high-throughput evaluation of ECMO thrombosis. Methods: Our study combines computational fluid dynamics (CFD) and microfluidic technology to design in vitro models with customizable, clinically relevant biomaterials and flow conditions, which are two key factors influencing thrombus formation. ANSYS Fluent was used to model the flow parameters within ECMO tubing and tubing-connector junctions (TCJ). Microfluidic devices were fabricated using standard photolithography and soft lithography with unplasticized polyvinylchloride (UPVC) and polycarbonate (PC), replicating ECMO tubing and TCJ respectively. Biomaterial thrombosis was visualized in real-time via confocal microscopy, and analyzed as the total fluorescent area of platelet adhesion (CD41-FITC) and human fibrin/ogen (AF647) accumulation. Results: CFD revealed that shear rates of 500 – 5000 s-1 and decelerating flow regimes are relevant to ECMO tubing thrombosis. Microfluidic evaluation at such conditions showed that compared to the low platelet adhesion observed at continuous high shear (3000 s-1) (340 ± 266 µm2), platelet adhesion increased by 16-fold at continuous low shear (1000 s-1) (5510 ± 3631 µm2), increased 5-fold at deceleration into high shear (1703 ± 598 µm2), and increased 2.7-fold at stagnant into high shear conditions (912 ± 308 µm2). These results may be used to guide clinical practice and ECMO redesign to minimize low, decelerating shear. Conclusion: We have developed a model system capable of evaluating biomaterial thrombosis at tailorable, clinically relevant biomaterial and flow combinations. Additionally, this system may be used to test novel anti-thrombotic biomaterials or patient-specific blood-biomaterial compatibility. Ultimately, this model will improve ECMO treatment efficacy by advancing the field’s knowledge in scientific mechanisms underlying biomaterial thrombosis, inform clinicians of thrombotic flow regimes which should be minimized, and identify thrombotic biomaterial and flow combinations to guide bioengineers to design the next generation of safer ECMO.Figure 1. A microfluidic device incorporating extracorporeal circuit biomaterials is used to visualize thrombus formation under clinically relevant flow.

Tablet coating in lab-scale drum coaters: Combining DEM simulations and spray experiments to predict tablet coating

In pharmaceutical industry the coating of granulates or tablets is an essential production step, which is often realised in a rotating drum moving the tablet bed and a number of spray nozzles installed above this bed. The nozzles atomise the coating liquid through a high-velocity annular air-stream and apply steering air nozzles for producing an oval-shaped spray. The operational conditions determine the produced droplet size spectrum. The collision of the droplets with the solid particles or tablets in the drum and the resulting coating and film thickness strongly influences the product properties of granulates or tablets. In order to be able to predict the coating result, without running time-consuming lab-scale experiments, it is intended to numerically simulate the entire tablet coating process by combining Computational Fluid Dynamics (CFD), the Discrete Element Method (DEM) and the Lagrangian Parcel Concept (LPC) for simulating the spray propagation originating at the nozzle. For doing so, knowledge on the spray droplet properties produced by the two-fluid spray nozzles is indispensable. Therefore, it is necessary to characterise as a first step the local droplet size and velocity distributions in different spray cross-sections, which is the main focus of the present contribution. Such measurements are efficiently done by applying Phase Doppler Anemometry (PDA). It was found that the velocity of the spray emitted from the two-fluid nozzle exceeds almost 200 m/s and the number mean droplet diameter was well below 20 μm. In the initial spray region droplet break-up was observed whereas downstream of 50 mm a slight growth of the mean droplet size indicated further coalescence. Due to the applied pattern air, the spray flux distribution at the normally located tablet bed (i.e., 100–150 mm downstream the nozzle) has an oval shape and the mean droplet velocity still exceeds almost 50 m/s with a mean droplet diameter of about 10 μm. The cross-sectional measurements are used to provide proper inlet conditions for the spray simulations, while the other cross-sections are used for validating the computed spray expansion and dispersion. For demonstrating the effect of a real measured spray flux distribution on the tablet coating quality, additionally DEM simulations were conducted. Here, the measured spray flux data were mapped onto the temporarily evolving and moving tablet surface simulated for a lab-scale test drum by DEM. The uniformity of tablet coating (inter-tablet variability) was judged by calculating the Coefficient Of Variation (COV) for the considered different tablet shapes and operational conditions.

Development of an Inline Tube-in-Tube Solvent Exchanger for Continuous-Flow Swap from High- to Low-Boiling Solvent

Solvent swap from high- to low-boiling solvents still presents a huge challenge in the development of multistep continuous-flow processing. Herein, a miniaturized inline tube-in-tube solvent exchanger combining intensified convective mass transfer with droplet evaporation is presented. The design involves intimate contact between droplet and gas phases in countercurrent mode within a mesofluidic tube. Its solvent removal performance was evaluated by using pure solvents and binary mixtures. The results revealed that the commonly used solvents with low to medium boiling points (Tb < 100 °C) can be completely evaporated at room temperature without the need of heating. Significantly, high-boiling solvents can be efficiently removed at temperatures much lower than their boiling points. Its capacity for the separation of binary mixtures was corroborated by removing dichloromethane from a 50:50 mol % dichloromethane–dimethylformamide mixture and acetic acid from a 50:50 mol % p-nitroacetophenone–acetic acid mixture. A computational fluid dynamics (CFD) model based on the phase field approach was developed to quantitatively describe the droplet evaporation process, providing indepth evaporation dynamics and furthering our understanding on the concomitant phase change phenomenon. The comparison with other types of evaporators highlights its superior evaporative performance. To enable fast design and optimization of this type of evaporators, a correlation model was proposed to calculate the evaporation rate with good prediction performance. The usefulness of this device was demonstrated by performing a two-step continuous-flow synthesis of 4-aminoacetophenone. The combined experimental and CFD studies demonstrate that this tube-in-tube device can serve as a convenient and efficient tool for continuous solvent exchange, particularly from high- to low-boiling solvents, in continuous-flow synthesis.

Power consumption and oxygen transfer optimization for C5 sugar acid production in a gas-liquid stirred tank bioreactor using CFD-Taguchi method

Power consumption and oxygen transfer are considered the two most important direct indexes, affecting the effective and cost-efficient C5 sugar acid fermentation from C5 sugar. In this work, an effective optimization approach combining computational fluid dynamics (CFD) modeling and the Taguchi method, was presented to evaluate C5 sugar acid fermentation in a gas-liquid stirred tank bioreactor. Three most critical control factors of impeller type (Rushton turbine (RT), concaved blade disc turbine (CBDT), and four wide-blade hydrofoil impeller pumping down (WHd)), agitation speed and aeration rate involved three levels, were quantitatively studied by numerical simulation based on a validated CFD model. Results indicated that, the WHd impeller gave the best energy-saving performance, the CBDT impeller gave the best oxygen transfer performance, while the RT impeller was the most balanced one. The agitation speed contributed most to both the power consumption and oxygen transfer, while exerting minor impact on compromise indexes. The aeration rate had little contribution to power consumption and oxygen transfer, while deserving more attention when energy efficiency and trade-off optimization are considered. These findings can be used as guidelines to achieve efficient and economical C5 sugar acid production.

Enhancing performance of nanofluid mini-channel heat sinks through machine learning and multi-objective optimization of operating parameters

The improvement in the performance of power systems in new energy vehicles has posed new demands for the performance of thermal management systems, leading to an increased interest in the application of nanofluid mini-channel heat sinks. Despite their potential, studying nanofluids is challenging due to the complexity of their preparation. To mitigate the computational and optimization costs, this study proposed a combination of Computational Fluid Dynamics (CFD) and machine learning in a multi-objective optimization algorithm for optimizing the operating parameters of nanofluid mini-channel heat sinks. Firstly, a numerical model of the nanofluid mini-channel was developed using the Mixture model to generate the dataset for machine learning models. Secondly, SVR, GPR, and RF models were utilized to establish the mapping relationships between the parameters of the nanofluid mini-channel, including the inputs of the inlet Reynolds number (Re), volume fraction (φ), and heat flow density (q), and the outputs of the pressure drop (△P) and the average temperature of the heating wall (Tave). The results indicated that the GPR model was the most suitable, with R2 values of 0.9939 and 0.9985 for Tave and △P, respectively. By employing the NSGA-II multi-objective optimization algorithm, the optimal value of φ was determined for different operating conditions, with values of around 3% at low Re and around 2% at high Re.

Effect of surfactants during drop formation in a microfluidic channel: a combined experimental and computational fluid dynamics approach

The effect of surfactants on the flow characteristics during rapid drop formation in a microchannel is investigated using high-speed imaging, micro-particle image velocimetry and numerical simulations; the latter are performed using a three- dimensional multiphase solver that accounts for the transport of soluble surfactants in the bulk and at the interface. Drops are generated in a flow-focusing microchannel, using silicone oil ( $4.6$ mPa s) as the continuous phase and a 52 % w/w glycerol solution as the dispersed phase. A non-ionic surfactant (Triton X-100) is dissolved in the dispersed phase at concentrations below and above the critical micelle concentration. Good agreement is found between experimental and numerical data for the drop size, drop formation time and circulation patterns. The results reveal strong circulation patterns in the forming drop in the absence of surfactants, whose intensity decreases with increasing surfactant concentration. The surfactant concentration profiles in the bulk and at the interface are shown for all stages of drop formation. The surfactant interfacial concentration is large at the front and the back of the forming drop, while the neck region is almost surfactant free. Marangoni stresses develop away from the neck, contributing to changes in the velocity profile inside the drop.

Fabrication of Hydrogel-Based Composite Fibers and Computer Simulation of the Filler Dynamics in the Composite Flow.

Fibrous structures with anisotropic fillers as composites have found increasing interest in the field of biofabrication since they can mimic the extracellular matrix of anisotropic tissues such as skeletal muscle or nerve tissue. In the present work, the inclusion of anisotropic fillers in hydrogel-based filaments with an interpenetrating polymeric network (IPN) was evaluated and the dynamics of such fillers in the composite flow were analyzed using computational simulations. In the experimental part, microfabricated rods (200 and 400 μm length, 50 μm width) were used as anisotropic fillers in extrusion of composite filaments using two techniques of wet spinning and 3D printing. Hydrogels such as oxidized alginate (ADA) and methacrylated gelatin (GelMA) were used as matrices. In the computational simulation, a combination of computational fluid dynamics and coarse-grained molecular dynamics was used to study the dynamics of rod-like fillers in the flow field of a syringe. It showed that, during the extrusion process, microrods are far from being well aligned. Instead, many of them tumble on their way through the needle leading to a random orientation in the fiber which was confirmed experimentally.

Pore-scale morphology effects on colloid deposition by trajectory tracking simulations

The migration and deposition of colloids in natural porous media is a phenomenon of high importance in hydrocarbon recovery, wastewater treatment and contaminant hydrogeology. Commonly described by colloid filtration theory (CFT), and using a single representative collector element, the theoretical framework is not accounting for morphological effects and heterogeneity of realistic porous media, including the local variations of balance of forces or presence of hydraulically stagnant regions in the vicinity of grain-to-grain contacts. So far developed numerical models of colloidal transport in porous media accounting for all major interactions and pore structure were applied to 2D systems. Known 3D simulations are limited to particulate flow with a reduced set of interactions.We propose a coupled colloid transport model combining computational fluid dynamics (CFD) and discrete element model (DEM) approaches, where the former provides the velocity field at a given time step, while the second calculates the updated colloid positions accounting for a set of forces acting on each colloid. Relationships between colloid retention and morphological characteristics, such as the pore coordination number and surface to volume ratio of a random sphere pack are investigated by pore partitioning of a digitized representation. The role of stagnation zones is quantified by expressing the capture probability of colloids as a function of distance to the nearest grain-to-grain contact measured along the curved surface.The analysis of pore space morphology of the collector and the simulated filtration process reveals a strong relation between colloid retention and pore connectivity. The simulations successfully mimic the expected effects of ionic strength of the carrier fluid, such as the increasing role of grain-to-grain contacts in colloid retention at low ionic strength.