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Published in Mark J. Mezger, Kay J. Tindle, Michelle Pantoya, Lori J. Groven, Dilhan M. Kalyon, Energetic Materials, 2017
Jonghyun Park, Heng Pan, Mark J. Mezger, Steven M. Nicolich, John M. Centrella, Frank T. Fisher, Nezahat Boz, Moinuddin Malik, Seda Aktas, Jing He, Dilhan M. Kalyon
The numerical simulation source codes can be used to track nondiffusive tracer particles and generate measures of mixedness [10,13–15,22,26]. The application of tools of dynamics including particle tracking, predicting the Lyapunov exponents and Poincaré sections, allows the differentiation of conditions under which regular or chaotic motion conditions occur. In regular motion, the distance of separation between neighboring particles changes linearly with residence time. On the other hand, in chaotic motion the distance of separation between neighboring particles changes exponentially with residence time. Furthermore, in chaotic motion, there is an extreme sensitivity to initial conditions [13]. Kneading disks generally give rise to chaotic mixing conditions with excellent distributive mixing capabilities. However, even with some types of kneading disks (those staggered in 30° forward or reverse), there are pipeline flows with little interchange of material to the outside of the pipeline flow region (Kolmogorov–Arnold–Moser [KAM] surfaces) [13]. Such analysis can be very useful for the selection of optimum geometries for the tailoring of the screw configuration for a specific operation and proper selection of the operating conditions.
Three-dimensional numerical investigation of hybrid nanofluids in chain microchannel under electrohydrodynamic actuator
Published in Numerical Heat Transfer, Part A: Applications, 2023
Milad Amiri, Dariusz Mikielewicz
To obtain the desired performance, the shape of microchannels is an important design variable. The usage of chain microchannels as a micromixer is one of its applications and micromixers are important elements of microfluidic technology. The difficulty of mixing performance on the microscales depends on the small size of the micromixers. At high Reynolds numbers (typically > 2400), two fluids can easily be mixed by turbulence. It will be difficult to achieve in microchannel with less than one millimeter of cross-sections. The Reynolds numbers of liquids flowing through these microchannels are very small (typically <10). At a low Reynolds number, turbulent mixing does not happen and the solution is homogenized only through diffusion processes. At a low Reynolds number, flow in micromixers is intrinsically a diffusion-dominated laminar flow due to the microscale. Because of geometrical effects, it is evident that separating the layers of a mixture and recombining them results in quick mixing at low Reynolds numbers. A straight laminar flow arises when the Reynolds number is low, i.e. when there is no inertia in the flow. Even in bends, the path lines of flow follow the channel curvature as the number of flow layers and the contact surface area between the mixing fluids increases. Diffusion and convection are the basic mechanisms of fluid mixing in mixers. So, at the micro scale, molecular diffusion overcomes whilst convection is limited; consequently, mixing by diffusion at low Reynolds numbers needs too much time and a very long channel. Therefore, to accelerate the process, the contact area between the fluids to be mixed must be increased, and the fluids must be stretched, split, and recombined. Low energy requirements and high mixing efficiency are two keys characteristics of mixers. The cost of production would be reduced if the device’s pressure drop was reduced. Mixing is ensured at low Reynolds numbers by generating multiple fluid lamellae. As the Reynolds number rises, vortexes emerge within the flow, eventually resulting in chaotic mixing. In fact, most micromixers can operate satisfactorily at either high or low Reynolds numbers – and chain micromixer has high energy requirements due to their complex structure. Therefore, the improvement of high-performance devices working in a wide range of Reynolds numbers with an acceptable pressure drop is presently a major focus of this section. Figures 10 and 11 illustrated the counter of pressure for poor water and hybrid nanofluid at low Reynolds number (0.416 Pressure drop for poor water at Re = 0.416, 1.666, 2.499 and 4.166 is 1.05, 4.16, 6.25 and 10.45 Pascal, respectively, whilst it has been obtained 1.18, 4.75, 7.15 and 12 Pascal for hybrid nanofluid at aforementioned Reynolds, respectively. On the other word, using hybrid nanofluid, in aforementioned Reynolds number, leads to increase of 12.38%, 14.18%, 14.40% and 14.83% of pressure drop, respectively. It can be found that at low Reynolds range, the lower Reynolds results in decreasing of percentage of pressure drop.