Cavitating flows entail phase change and hence very large and steep density variations in the low pressure regions. These are also very sensitive to: (a) the formation and transport of vapor bubbles, (b) the turbulent fluctuations of pressure and velocity, and (c) the magnitude of noncondensible gases, which are dissolved or ingested in the operating liquid. The presented cavitation model accounts for all these first-order effects, and thus is named as the “full cavitation model.” The phase-change rate expressions are derived from a reduced form of Rayleigh-Plesset equation for bubble dynamics. These rates depend upon local flow conditions (pressure, velocities, turbulence) as well as fluid properties (saturation pressure, densities, and surface tension). The rate expressions employ two empirical constants, which have been calibrated with experimental data covering a very wide range of flow conditions, and do not require adjustments for different problems. The model has been implemented in an advanced, commercial, general-purpose CFD code, CFD-ACE+. Final validation results are presented for flows over hydrofoils, submerged cylindrical bodies, and sharp-edged orifices. Suggestions for possible extensions of the model implementation, e.g., to nonisothermal flows, for ingestion and mixing of noncondensible gases, and for predictions of noise and surface damage are outlined.
A mixing technique based on the principle of bubble-induced acoustic microstreaming was developed. The mixer consists of a piezoelectric disk that is attached to a reaction chamber, which is designed in such a way that a set of air bubbles with desirable size is trapped in the solution. Fluidic experiments showed that air bubbles resting on a solid surface and set into vibration by the sound field generated steady circulatory flows, resulting in global convection flows and thus rapid mixing. The time to fully mix a 22 microL chamber is significantly reduced from hours (for a pure diffusion-based mixing) to tens of seconds. Numerical simulations showed that the induced flowfield and thus degree of mixing strongly depend on bubble positions. Optimal simulated mixing results were obtained for staggered bubble distribution that minimizes the number of internal flow stagnation regions. Immunomagnetic cell capture experiments showed that acoustic microstreaming provided efficient mixing of bacterial cell (Esherichia coli K12) matrix suspended in blood with magnetic capture beads, resulting in highly effective immunomagnetic cell capture. Bacterial viability assay experiments showed that acoustic microstreaming has a relatively low shear strain field since the blood cells and bacteria remained intact after mixing. Acoustic microstreaming has many advantages over most existing chamber micromixing techniques, including simple apparatus, ease of implementation, low power consumption (2 mW), and low cost.
Present-day computational fluid dynamics (CFD) techniques can be used to analyze the behavior of fluid flow in a variety of pumps. CFD can be a powerful tool during the design stage for rapid virtual prototyping of different designs, analyzing performance parameters, and making design improvements. Computational flow solutions provide information such as the location and size of stagnation zones and the local shear rate. These parameters can be correlated to the extent of hemolysis and thrombus formation and are critical to the success of a blood pump. CFD-ACE, an advanced commercial CFD code developed by CFD Research Corporation, has been applied to fluid flows in rotary machines, such as axial flow pumps and inducers. Preprocessing and postprocessing tools for efficient grid generation and advanced graphical flow visualization are integrated seamlessly with CFD-ACE. The code has structured multiblock grid capability, non-Newtonian fluid treatment, a variety of turbulence models, and an Eulerian-Langrangian particle tracking model. CFD-ACE has been used successfully to study the flow characteristics in an axial flow blood pump. An unstructured flow solver that greatly automates the process of grid generation and speeds up the flow simulation is under development.
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