Viscoelastic effects of immiscible liquid-liquid displacement in microchannels with bends

The displacement flow of an organic Newtonian fluid by a pure viscoelastic aqueous solution is experimentally investigated inside a circular microchannel of 200 µm. Displacement is commonly encountered in many industrial applications, from cleaning to enhanced oil recovery. In this study, a pure viscoelastic fluid (known as Boger fluid) made up of polyethylene oxide (PEO), polyethylene glycol (PEG) and zinc chloride (ZnCl2) is used to displace an immiscible organic liquid (silicone oil). The results were compared with the Newtonian fluid displacement of similar density and viscosity as the viscoelastic one. High speed imaging is used to track both the residual film thickness of the organic phase and the interface deformations during displacement. It is found that the Boger fluid displacing phase produces a thinner displaced phase film compared to the Newtonian fluid, particularly at high capillary numbers. A correlation is proposed for the film thickness which includes the Weissenberg number for the viscoelastic case. After the displacement front, the interface becomes unstable with two modes of instability identified. In the case of the Boger fluid, the two modes of instability are core shifting, which is also present in the Newtonian case, and a periodic instability from the elastic stresses during displacement. Additionally, the shape of the interfacial instabilities switches freely from asymmetric to axisymmetric ones throughout the flow. The frequency of the periodic instabilities increases with the displacing phase flowrate. It was also found that microchannel bends downstream of the observation point affect the shape and frequency of the instabilities

Enhancing Microdroplet Image Analysis with Deep Learning

Microfluidics is a highly interdisciplinary field where the integration of deep-learning models has the potential to streamline processes and increase precision and reliability. This study investigates the use of deep-learning methods for the accurate detection and measurement of droplet diameters and the image restoration of low-resolution images. This study demonstrates that the Segment Anything Model (SAM) provides superior detection and reduced droplet diameter error measurement compared to the Circular Hough Transform, which is widely implemented and used in microfluidic imaging. SAM droplet detections prove to be more robust to image quality and microfluidic images with low contrast between the fluid phases. In addition, this work proves that a deep-learning super-resolution network MSRN-BAM can be trained on a dataset comprising of droplets in a flow-focusing microchannel to super-resolve images for scales ×2, ×4, ×6, ×8. Super-resolved images obtain comparable detection and segmentation results to those obtained using high-resolution images. Finally, the potential of deep learning in other computer vision tasks, such as denoising for microfluidic imaging, is shown. The results show that a DnCNN model can denoise effectively microfluidic images with additive Gaussian noise up to σ = 4. This study highlights the potential of employing deep-learning methods for the analysis of microfluidic images

Effect of a canopy patch made of streamwise-oriented plates on turbulence in an open-channel flow

The paper examines the flow through a highly porous canopy patch made of streamwise-oriented thin plates arranged in a staggered configuration and placed in a rough-bed open channel. This patch geometry contrasts with the patches made of bluff bodies, which are nearly exclusively used in the literature. Particle Image Velocimetry was used to measure the flow upstream, within and downstream of the patch. The canopy patch has the effect of drastically reducing the turbulence level of the incoming flow, especially the turbulence shear stress, which is reduced by 85%. Spectral analysis of the velocity shows that the reduction in turbulent kinetic energy occurs at all length scales. Yet, at the entrance of the patch, the energy from the smallest scales up to the scale of the water surface increases. This suggests a spectral shortcut mechanism by which the large-scale structures of the incoming flow are disintegrated by the group of plates instead of decaying through the energy cascade. The increased small-scale turbulent energy then dissipates through the patch

Near wake of emergent vegetation patches in shallow flow

Vegetation patches are particularly difficult to quantify in terms of flow resistance due to their complex geometry and topological behaviour under hydrodynamic loading. They not only influence the water level and mean velocities due to the drag they exert, but they also affect the turbulence and hence all transfer processes such as the sediment transport dynamics in the surrounding area. Existing studies dealing with the interaction of flow and vegetation concern mostly measurements of the drag of single plants followed by analyses of the flow through and above homogeneous canopies. However, studies of the flow around single patches are uncommon and are mostly restricted to arrays of cylindrical elements. For leafy plants there is very limited information and understanding of how the flow evolves through and around the plants. This work aims at filling these gaps via complementary physical lab-scale and numerical experiments of the flow through and around an artificial vegetation patch. The experimental work focuses on PIV measurements in the wake of the patches whereas the method of large-eddy simulation is employed to provide additional insights of the flow inside the patch. Here we focus on results based on the PIV measurements

Turbulent structure inside and above shallow to deep canopies

Multi-plane PIV measurements were performed in an open-channel flume filled with elongated prisms of height k and width l to investigate the effect of the deepening of the canopy on the flow structure. Velocity measurements were performed both inside the canopy and above it. Analysis of the spatial convergence for the double-averaged quantities shows that for canopy flow investigations (z k). Three canopy aspect ratios, k/l = [1, 3, 6] were investigated for a fixed modified-submergence ratio β = (h - k)=l = 3 where h is the water depth. As the canopy deepens, the hydraulic roughness decreases and the velocity near the bottom of the canopy becomes gradually constant, as expected for deep canopies. We show how the highly converged (both in space and time) profiles of double-averaged longitudinal velocity and total shear stress can be used to calculate the vertical distribution of drag in the canopy. With this methodology, values of the drag coefficient CD(z) can be calculated, and are found to be always close to unity, even in the upper part of the canopy

Near wake of emergent vegetation patches in shallow flow

Vegetation patches are particularly diffcult to quantify in terms of flow resistance due to their complex geometry and topological behaviour under hydrodynamic loading. They not only influence the water level and mean velocities due to the drag they exert, but they also affect the turbulence and hence all transfer processes such as the sediment transport dynamics in the surrounding area. Existing studies dealing with the interaction of flow and vegetation concern mostly measurements of the drag of single plants followed by analyses of the flow through and above homogeneous canopies. However, studies of the flow around single patches are uncommon and are mostly restricted to arrays of cylindrical elements. For leafy plants there is very limited information and understanding of how the flow evolves through and around the plants. This work aims at filling these gaps via complementary physical lab-scale and numerical experiments of the flow through and around an artificial vegetation patch. The experimental work focuses on PIV measurements in the wake of the patches whereas the method of large-eddy simulation is employed to provide additional insights of the flow inside the patch. Here we focus on results based on the PIV measurements

Near wake of emergent vegetation patches in shallow flow

Vegetation patches are particularly diffcult to quantify in terms of flow resistance due to their complex geometry and topological behaviour under hydrodynamic loading. They not only influence the water level and mean velocities due to the drag they exert, but they also affect the turbulence and hence all transfer processes such as the sediment transport dynamics in the surrounding area. Existing studies dealing with the interaction of flow and vegetation concern mostly measurements of the drag of single plants followed by analyses of the flow through and above homogeneous canopies. However, studies of the flow around single patches are uncommon and are mostly restricted to arrays of cylindrical elements. For leafy plants there is very limited information and understanding of how the flow evolves through and around the plants. This work aims at filling these gaps via complementary physical lab-scale and numerical experiments of the flow through and around an artificial vegetation patch. The experimental work focuses on PIV measurements in the wake of the patches whereas the method of large-eddy simulation is employed to provide additional insights of the flow inside the patch. Here we focus on results based on the PIV measurements

Surfactant-laden droplet size prediction in a flow-focusing microchannel::a data-driven approach

The control of droplet formation and size using microfluidic devices is a critical operation for both laboratory and industrial applications, e.g. in micro-dosage. Surfactants can be added to improve the stability and control the size of the droplets by modifying their interfacial properties. In this study, a large-scale data set of droplet size was obtained from high-speed imaging experiments conducted on a flow-focusing microchannel where aqueous surfactant-laden droplets were generated in silicone oil. Three types of surfactants were used including anionic, cationic and non-ionic at concentrations below and above the critical micelle concentration (CMC). To predict the final droplet size as a function of flow rates, surfactant type and concentration of surfactant, two data-driven models were built. Using a Bayesian regularised artificial neural network and XGBoost, these models were initially based on four inputs (flow rates of the two phases, interfacial tension at equilibrium and the normalised surfactant concentration). The mean absolute percentage errors (MAPE) show that data-driven models are more accurate (MAPE = 3.9%) compared to semi-empirical models (MAPE = 11.4%). To overcome experimental difficulties in acquiring accurate interfacial tension values under some conditions, both models were also trained with reduced inputs by removing the interfacial tension. The results show again a very good prediction of the droplet diameter. Finally, over 10 000 synthetic data were generated, based on the initial data set, with a Variational Autoencoder (VAE). The high-fidelity of the extended synthetic data set highlights that this method can be a quick and low-cost alternative to study microdroplet formation in future lab on a chip applications, where experimental data may not be readily available