Micro-convection caused by ponderomotive forces of the self-magnetic field of a magnetic fluid in the Hele-Shaw cell under the action of a vertical homogeneous magnetic field is studied both experimentally and numerically. It is shown that a non-potential magnetic force at magnetic Rayleigh numbers greater than the critical value causes fingering at the interface between the miscible magnetic and non-magnetic fluids. The threshold value of the magnetic Rayleigh number depends on the smearing of the interface between fluids. Fingering with its subsequent decay due to diffusion of particles significantly increases the mixing at the interface. Velocity and vorticity fields at fingering are determined by particle image velocimetry measurements and qualitatively correspond well to the results of numerical simulations of the micro-convection in the Hele-Shaw cell carried out in the Darcy approximation, which account for ponderomotive forces of the self-magnetic field of the magnetic fluid. Gravity plays an important role at the initial stage of the fingering observed in the experiments.

Highly monodispersed emulsions can be produced in microfluidic flow-focusing junctions (Anna et al 2003 Appl. Phys. Lett. 82 364–6, Baroud et al 2010 Lab Chip 10 2032–45). This is the reason why many industrial processes in the medical industry among others are based on droplet manipulation and involve at some point a step of dripping within a junction. However, only a few studies have focused on the flow field inside and outside the droplet, even though it is a necessary step for understanding the physical mechanism involved and for modeling the droplet formation process. Water-in-oil emulsions are produced in flow-focusing junctions of square cross sections. The fluids constituting the emulsion are (i) a 5.0 mPa·s silicon oil for the oil phase and (ii) distilled water containing 2.0 wt% of sodium dodecyl sulfate surfactant for the aqueous phase. Time-resolved shadow particle images are acquired using a microscale particle image velocimetry (µPIV) system and flow fields are calculated using an adaptive PIV algorithm in combination with dynamic masking. Inside the microchannel and in the permanent regime, the droplet has an internal circulation that has been well established by Sarrazin et al (AICHE J. 52 4061–70). But during the formation of a droplet in a flow-focusing junction, the flow field is not so well known, and the circulation in the finger flows forward along the sides and returns along the center. The mechanism can be described in terms of four distinct steps: droplet growth, necking, rupture, and recoil. The liquid expelled from the neck just before rupture is also well observed. The flow field and mixing are measured in detail during a complete cycle of formation of a main droplet and satellite droplets using high-speed imaging. This allows us to develop a better understanding of the different forces that are present and of the physical mechanism of droplet formation.

Data-driven decompositions of Particle Image Velocimetry (PIV) measurements are widely used for a variety of purposes, including the detection of coherent features (e.g., vortical structures), filtering operations (e.g., outlier removal or random noise mitigation), data reduction and compression. This work presents the application of a novel decomposition method, referred to as Multiscale Proper Orthogonal Decomposition (mPOD, Mendez et al 2019) to Time-Resolved PIV (TR-PIV) measurement. This method combines Multiresolution Analysis (MRA) and standard Proper Orthogonal Decomposition (POD) to achieve a compromise between decomposition convergence and spectral purity of the resulting modes. The selected test case is the flow past a cylinder in both stationary and transient conditions, producing a frequency-varying Karman vortex street. The results of the mPOD are compared to the standard POD, the Discrete Fourier Transform (DFT) and the Dynamic Mode Decomposition (DMD). The mPOD is evaluated in terms of decomposition convergence and time-frequency localization of its modes. The multiscale modal analysis allows for revealing beat phenomena in the stationary cylinder wake, due to the three-dimensional nature of the flow, and to correctly identify the transition from various stationary regimes in the transient test case.

Image-based sensor systems are quite popular in micro-scale flow investigations due to their flexibility and scalability. The aim of this manuscript is to provide an overview of current technical possibilities for Particle Image Velocimetry (PIV) systems and related image processing tools used in microfluidics applications. In general, the PIV systems and related image processing tools can be used in a myriad of applications, including (but not limited to): Mixing of chemicals, droplet formation, drug delivery, cell counting, cell sorting, cell locomotion, object detection, and object tracking. The intention is to provide some application examples to demonstrate the use of image processing solutions to overcome certain challenges encountered in microfluidics. These solutions are often in the form of image pre- and post-processing techniques, and how to use these will be described briefly in order to extract the relevant information from the raw images. In particular, three main application areas are covered: Micro mixing, droplet formation, and flow around microscopic objects. For each application, a flow field investigation is performed using Micro-Particle Image Velocimetry (µPIV). Both two-component (2C) and three-component (3C) µPIV systems are used to generate the reported results, and a brief description of these systems are included. The results include detailed velocity, concentration and interface measurements for micromixers, phase-separated velocity measurements for the micro-droplet generator, and time-resolved (TR) position, velocity and flow fields around swimming objects. Recommendations on, which technique is more suitable in a given situation are also provided.

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Particle image velocimetry (PIV) often employs the cross-correlation function to identify average particle displacement in an interrogation window. The quality of correlation peak has a strong dependence on the signal-to-noise ratio (SNR), or contrast of the particle images. In fact, variable-contrast particle images are not uncommon in the PIV community: Strong light sheet intensity variations, wall reflections, multiple scattering in densely-seeded regions and two-phase flow applications are likely sources of local contrast variations. In this paper, we choose an image pair obtained in a micro-scale mixing experiment with severe local contrast gradients. In regions where image contrast is sufficiently poor, the noise peaks cast a shadow on the true correlation peak, producing erroneous velocity vectors. This work aims to demonstrate that two image pre-processing techniques — local contrast normalization and Difference of Gaussian (DoG) filter — improve the correlation results significantly in poor-contrast regions.

The calibration of a multi-camera system for volumetric measurements is a basic requirement of reliable 3D measurements and object tracking. In order to refine the precision of the mapping functions, a new, tomographic reconstruction-based approach is presented. The method is suitable for Volumetric Particle Image Velocimetry (PIV), where small particles, drops or bubbles are illuminated and precise 3D position tracking or velocimetry is applied. The technique is based on the 2D cross-correlation of original images of particles with regions from a back projection of a tomographic reconstruction of the particles. The off-set of the peaks in the correlation maps represent disparities, which are used to correct the mapping functions for each sensor plane in an iterative procedure. For validation and practical applicability of the method, a sensitivity analysis has been performed using a synthetic data set followed by the application of the technique on Tomo-PIV measurements of a jet-flow. The results show that initial large disparities could be corrected to an average of below 0.1 pixels during the refinement steps, which drastically improves reconstruction quality and improves measurement accuracy and reliability.

This work discusses an approach to compute pressure fields from planar PIV measurement using standard CFD tools. In particular, we propose a combination of interpolation and mesh adaptation to import the PIV measurements on a grid that is morphed around objects, and is fine enough to solve the Poisson equation accurately. The whole process of meshing, interpolation and pressure computation is carried out using the popular open-source solver OpenFoam®. The method is tested and validated on a classic benchmark test case, namely, the unsteady flow past a cylinder. A 3D multiphase flow simulation is used to generate the reference data and analyze the impact of both, the PIV interrogation and the interpolation on the morphed grid. The simulation uses an Euler-Lagrangian one-way coupling approach to simulate the flow field and the dynamics of seeding particles. The analysis compares the pressure field from the 3D CFD simulation with the solution of a 2D Poisson equation based on the 2D velocity field obtained by either down-sampling the CFD data or by PIV interrogation of synthetic images built from the CFD data. Finally, we challenge the proposed method with the pressure reconstruction in a TR-PIV experiment in similar conditions.