Mixing fluid dynamics simulation

P 28] A 3-D solid model of the cross-shaped micro mixer is meshed to a sufficiently fine scale with brick elements of 2 pm for the simulations [71]. Simulation results were intended at very short time scales, e.g. in intervals of 50 ps, to verify the mixing patterns at the initial state after application of pressure. The numerical values of the mass fraction are taken to give quantitative measures of the mixing efficiency. The pre-processor fluidics solver and post-processor of ConventorWare were used for the simulations. The software FLUENT 5 was used for verification of these results, since the former software is so far not a widely established tool for fluid dynamic simulation. 

Mixing properties and flow fields in stirred tanks are usually studied on a laboratory scale. Practical scale-up of a stirred tank cannot be performed requiring that every individual mixing and fluid mechanical parameters in the small scale tank should be maintained in the larger one. Therefore, scale-up procedures for different types of processes have been determined through experience, testing and computational fluid dynamics simulations. 

Appropriate design and layout of a bioreactor system for production scale are a key issue for the successful production of biopharmaceuticals. Here, techniques such as characterization of the reactors with respect to power input, mass transfer, and mixing are applied, often coupled with computational fluid dynamic simulations [34,113-119]. 

One tool that can help solve these challenges is to employ Computational Fluid Dynamics simulation. This gives an insight into the inside of dosing and mixing plants. The processes involved can be studied in quasi slow motion and this provides valuable information about the various flow anomalies which are constantly observed in practice. 

So far, some researchers have analyzed particle fluidization behaviors in a RFB, however, they have not well studied yet, since particle fluidization behaviors are very complicated. In this study, fundamental particle fluidization behaviors of Geldart s group B particle in a RFB were numerically analyzed by using a Discrete Element Method (DEM)- Computational Fluid Dynamics (CFD) coupling model [3]. First of all, visualization of particle fluidization behaviors in a RFB was conducted. Relationship between bed pressure drop and gas velocity was also investigated by the numerical simulation. In addition, fluctuations of bed pressure drop and particle mixing behaviors of radial direction were numerically analyzed. 

Jiang, T.-L. and E. E. O Brien (1991). Simulation of scalar mixing by stationary isotropic turbulence. Physics of Fluids A Fluid Dynamics 3, 1612-1624. 

McCarthy LG, Kosiol C, Healy AM, Bradley G, Sexton JC, Corrigan OI. Simulating the hydrodynamic conditions in the United States Pharmacopeia paddle dissolution apparatus. AAPS Pharm Sci Tech 2003 4(2) Article 22. McCarthy LG, Bradley G, Sexton JC, Corrigan OI, Healy AM. Computational fluid dynamics modeling of the paddle dissolution apparatus agitation rate, mixing patterns, and fluid velocities. AAPS Pharm Sci Tech 2004 5(2) Article 31. 

Computational fluid dynamics were used to describe the flow which undergoes a fast transition from laminar (at the fluid outlets) to turbulent (in the large mixing chamber) [41]. Using the commercial tool FLUENT, the following different turbulence models were applied a ke model, an RNC-ki model and a Reynolds-stress model. For the last model, each stream is solved by a separate equation for the two first models, two-equation models are applied. To have the simulations at... 

These models require information about mean velocity and the turbulence field within the stirred vessels. Computational flow models can be developed to provide such fluid dynamic information required by the reactor models. Although in principle, it is possible to solve the population balance model equations within the CFM framework, a simplified compartment-mixing model may be adequate to simulate an industrial reactor. In this approach, a CFD model is developed to establish the relationship between reactor hardware and the resulting fluid dynamics. This information is used by a relatively simple, compartment-mixing model coupled with a population balance model (Vivaldo-Lima et al., 1998). The approach is shown schematically in Fig. 9.2. Detailed polymerization kinetics can be included. Vivaldo-Lima et a/. (1998) have successfully used such an approach to predict particle size distribution (PSD) of the product polymer. Their two-compartment model was able to capture the bi-modal behavior observed in the experimental PSD data. After adequate validation, such a computational model can be used to optimize reactor configuration and operation to enhance reactor performance. 

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