Blood is a non-homogeneous fluid that flows inside the human artery system and provides the cells with nutrients. In this study the auto rotation effect of blood’s microstructure on its flow inside a human carotid model is studied by using a micropolar fluid model. The study aims to investigate the flow differences that occur due to its microstructure as compared to a Newtonian fluid. We focus on the vortex viscosity effect, i.e., the ratio of microrotation viscosity to the total one, because this is the only parameter that affects directly the fluid flow. Simulations in a range of vortex viscosities, are carried out in a $3D$ human carotid model that is computationally reconstructed. All of the simulations are conducted at the diastolic Reynolds number that occurs in the human carotid. Results indicate that micropolarity affects blood velocity in the range of parameters studied by $4\%$. As micropolarity is increased, higher velocities in the center of vessels and lower near the boundaries are found as compared to a Newtonian fluid consideration. This is an indication that the increase of the fluid’s micropolarity leads to an increase of the boundary layer thickness. More importantly, an increase in vortex viscosity and the resulting increase in microrotation result in decreased shear stress in the carotid’s walls; this finding can be significant in regards to the onset and the development of atherosclerosis. Finally, the flow distribution at the carotid seems to heavily be affected by the geometry and the micropolarity of the fluid.

The turbulent regime of non-Newtonian flows presents a particular interest as flow behavior is directly affected by the internal microstructure type of the fluid. Differences in the dispersed phase of a particle laden flow can either lead to drag reduction and turbulence attenuation or to drag and turbulence enhancement in polymer flows and dense suspensions, respectively. A general concept of non-Newtonian fluid flow may be considered in a continuous manner through the micropolar theory, recognizing the limitations that bound this theory. In recent articles [Sofiadis and Sarris, “Microrotation viscosity effect on turbulent micropolar fluid channel flow,” Phys. Fluids 33, 095126 (2021); Sofiadis and Sarris, “Turbulence intensity modulation by micropolar fluids,” Fluids 6, 195 (2021)], the micropolar viscosity effect of the turbulent channel flow under constant Reynolds number and its turbulent modulation were investigated. The present study focuses on the investigation of the turbulent micropolar regime as the Reynolds number increases in a channel flow. Findings support that the micropolar stress, which was found to assist turbulence enhancement in the present model, attenuates as Re increases. Effects on the friction behavior of the flow, as Reynolds number increases, become more important for cases of higher micropolar viscosity, where a reverse drag behavior is observed as compared to lower micropolar viscosity ones. Finally, turbulence intensification for these cases declines close to the wall in contrast to lower micropolar viscosity flows, which manage to sustain high turbulence and increase drag in the near-wall region along with Re.

A large number of microfluidic applications are based on effective mixing. In the application of water purification, the contaminated water needs to be effectively mixed with a solution that is loaded with nanoparticles. In this work, the Tesla valve was used as a micromixer device in order to evaluate the effect of this type of geometry on the mixing process of two streams. For this reason, several series of simulations were performed in order to achieve an effective mixing of iron oxide nanoparticles and contaminated water in a duct. In the present work, a stream loaded with Fe₃O₄ nanoparticles and a stream with contaminated water were numerically studied for various inlet velocity ratios and initial concentrations between the two streams. The Navier–Stokes equations were solved for the water flow and the discrete motion of particles was evaluated by the Lagrangian method. Results indicate that the Tesla valve can be used as a micromixer since mixing efficiency reached up to 63% for V_p/V_c = 20 under various inlet nanoparticles rates for the geometry of the valve that was used in this study.

Fluids with an internal rigid microstructure, the so-called micropolar fluids, gain significant attention in many industrial, natural, and biological systems. Here, we study in detail the microrotation viscosity effect on turbulent flows by considering an alternative formulation of the Navier–Stokes equation in which the linear and angular momentum is conserved for the fluid and its microstructure, respectively. The case of low-turbulence channel flow with Re = 5600, based on mean velocity, channel height, and the fluid kinematic viscosity, is used to study the effect of polarity. The present results are discussed and compared against the usual channel flow statistics, from Newtonian, dense suspensions with rigid spheres and polymer turbulent flows in similar conditions. It is found that turbulence tends to increase near the wall as micropolar effects get stronger. This enhancement is attributed to a turbulence generation mechanism that seems to be connected with the micropolar stress tensor and is well described by the present model. At the same time, shear stress inclines near the wall, while drag increase is observed throughout the flow regime.

We demonstrate that, as in the forward cascade of three-dimensional turbulence that displays intermittency (a lack of self-similarity) due to the concentration of energy dissipation in a small set of fractal dimension less than three, the inverse cascade of two-dimensional turbulence can also display a lack of self-similarity and intermittency if the energy injection is constrained in a fractal set of dimension less than two. A series of numerical simulations of two-dimensional turbulence are examined, using different forcing functions of the same forcing length scale but different fractal dimension $D$ that varies from the classical $D=2$ case to the point vortex case $D=0$. It is shown that as the fractal dimension of the forcing is decreased from $D=2$, the self-similarity is lost and intermittency appears. The present model thus provides a unique example that intermittency is controlled and can thus shed light and provide test beds for multifractal models of turbulence.

Convolutional neural networks (CNN) have been widely adopted in fluid dynamics investigations over the past few years due to their ability to extract and process fluid flow field characteristics. Both in sparse-grid simulations and sensor-based experimental data, the establishment of a dense flow field that embeds all spatial and temporal flow information is an open question, especially in the case of turbulent flows. In this paper, a deep learning (DL) method based on computational CNN layers is presented, focusing on reconstructing turbulent open channel flow fields of various resolutions. Starting from couples of images with low/high resolution, we train our DL model to efficiently reconstruct the velocity field of consecutive low-resolution data, which comes from a sparse-grid Direct Numerical Simulation (DNS), and focus on obtaining the accuracy of a respective dense-grid DNS. The reconstruction is assessed on the peak signal-to-noise ratio (PSNR), which is found to be high even in cases where the ground truth input is scaled down to 25 times.