Computational fluid dynamics particle

Relatively uncomphcated transparent tank studies with tracer fluids or particles can give a similar feel for the overall flow pattern. It is important that a careful balance be made between the time and expense of calculating these flow patterns with computational flirid dynamics compared to their apphcabihty to an actual industrial process. The future of computational fluid dynamics appears very encouraging and a reasonable amount of time and effort put forth in this regard can yield immediate results as well as potential (or future process evaluation. 

Particle trajectories can be calculated by utilizing the modern CFD (computational fluid dynamics) methods. In these calculations, the flow field is determined with numerical means, and particle motion is modeled by combining a deterministic component with a stochastic component caused by the air turbulence. This technique is probably an effective means for solving particle collection in complicated cleaning systems. Computers and computational techniques are being developed at a fast pace, and one can expect that practical computer programs for solving particle collection in electrostatic precipitators will become available in the future. 

Logtenberg, S. A., Nijemeisland, M., and Dixon, A. G., Computational fluid dynamics simulations of fluid flow and heat transfer at the wall-particle contact points in a fixed-bed reactor, Chem. Eng. Sci. 54, 2433-2439 (1999). 

There are many nonintrusive experimental tools available that can help scientists to develop a good picture of fluid dynamics and transport in chemical reactors. Laser Doppler velocimetry (LDV), particle image velocimetry (PIV) and sonar Doppler for velocity measurement, planar laser induced fluorescence (PLIF) for mixing studies, and high-speed cameras and tomography are very useful for multiphase studies. These experimental methods combined with computational fluid dynamics (CFDs) provide very good tools to understand what is happening in chemical reactors. 

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. 

Saastamoinen J.J., Huttunen M., and Kjaldman L., Modelling of Pyrolysis and Combustion of Biomass Particles , the fourth European Computational Fluid Dynamics Conference, 7-11 Sept, Athens, Greece, (1998)... 

Although much progress has been made in the last decade regarding operation, design and scale-up of spin-filters, in most works found in the literature either fouling or retention problems (or both) were observed. A better comprehension of the fluid and particle dynamics involved in spin-filter perfusion would improve this situation. In this context, a valuable tool that could be used is computational fluid dynamics (CFD), which has been recently employed for the design and performance prediction of other cell separation devices [46,114]. 

For dilute phase conveying numerical simulations with a commercial computational fluid dynamics code were carried out. The analysis of particle wall impact conditions in a pipe bend showed that they take place under low wall impact angles of 5-35° which results in low normal (5-25 m/s) and high tangential (33-44 m/s) impact velocity components. These findings lead to the conclusion that not only normal stresses caused by the impacts are important in dilute phase conveying but that sliding friction stresses play an important role as well. 

Bridging the gap between micro- and macro-scale is the central theme of the first contribution. The authors show how a so-called Energy-Minimization Multi-Scale (EMMS) model allows to do this for circulating fluid beds. This variational type of Computational Fluid Dynamics (CFD) modeling allows for the resolution of meso-scale structures, that is, those accounting for the particle interactions, and enables almost grid-independent solution of the gas-solids two-phase flow. 

Simulation techniques suitable for the description of phenomena at each length-scale are now relatively well established Monte Carlo (MC) and Molecular Dynamics (MD) methods at the molecular length-scale, various mesoscopic simulation methods such as Dissipative Particle Dynamics (Groot and Warren, 1997), Brownian Dynamics, or Lattice Boltzmann in the colloidal domain, Computational Fluid Dynamics at the continuum length-scale, and sequential-modular or equation-based methods at the unit operation/process-systems level. 

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