Cyclone simulation

The Reynolds stress model requires the solution of transport equations for each of the Reynolds stress components as well as for dissipation transport without the necessity to calculate an isotropic turbulent viscosity field. The Reynolds stress turbulence model yield an accurate prediction on swirl flow pattern, axial velocity, tangential velocity and pressure drop on cyclone simulation [7,6,13,10],... 

It should be added, however, that it is generally not necessary or even necessarily desirable to represent a particle distribution by fitting it to any particular distribution function, a priori. Computer programs for designing and evaluating cyclone performance normally utilize the raw particle size distribution data (often in cumulative form) in their internal computations. This technique eliminates any errors pertaining to any differences that may exist between the mathematical fit of the data and the actual data that is being fitted. Often it is observed that some distribution function does fit the majority of the measured distribution data but may, for example, fail to fit the smallest particle size fraction. Under such conditions, if it were important to know about the collection or losses of the fines , one would not want to use such a distribution model in practice. Instead, the actual measmed distribution data would be utilized for cyclone simulation pm-poses. 

Fig. 6.A.I. Dimensions, in meters, of the Hoffmann cyclone simulated herein and the size distribution of the feed solids...

We wish to conclude this discussion on the entrance solids-loading effect by simply stating that the effects of solids loading cannot be ignored if performance is to be accurately predicted. This applies not only to cyclone simulation models, but also to the use of the scaling laws developed in Chap. 8. 

The preceding section provides us with a technique for estimating the volume-averaged drop size of a collection of droplets flowing within a pipe under mist-annular flow conditions. And while this is often all that one may wish to know about the droplet distribution, it is sometimes of interest to know, or at least estimate, the entire drop size distribution. Such would be the case if one wished to perform a cyclone simulation study which required, as input, an estimate of the inlet drop size distribution which may exist within the upstream pipe feeding the cyclone. 

Numerical Simulations of the Fluid and Particulate Flows in the Cyclone... 

Due to the very low volumetric concentration of the dispersed particles involved in the fluid flow for most cyclones, the presence of the particles does not have a significant effect on the fluid flow itself. In these circumstances, the fluid and the particle flows may be considered separately in the numerical simulation. A common approach is to first solve the fluid flow equations without considering the presence of particles, and then simulate the particle flow based on the solution of the fluid flow to compute the drag and other interactive forces that act on the particles. 

An appropriate model of the Reynolds stress tensor is vital for an accurate prediction of the fluid flow in cyclones, and this also affects the particle flow simulations. This is because the highly rotating fluid flow produces a. strong nonisotropy in the turbulent structure that causes some of the most popular turbulence models, such as the standard k-e turbulence model, to produce inaccurate predictions of the fluid flow. The Reynolds stress models (RSMs) perform much better, but one of the major drawbacks of these methods is their very complex formulation, which often makes it difficult to both implement the method and obtain convergence. The renormalization group (RNG) turbulence model has been employed by some researchers for the fluid flow in cyclones, and some reasonably good predictions have been obtained for the fluid flow. 

Most of the simulation effort has been applied to fluidized bed combustors which use relatively large size particles. Simulation can also be used for other fluidization processes in the petrochemical industry. Research should be undertaken to identify the proper scaling parameters for beds fluidized with smaller particles. Similar simulations may also apply to components such as cyclones. 

Numerical Settings of Two Simulations on a Gas Cyclone With Different Numerical Schemes a RANS-Based Simulation (Hoekstra, 2000) and a LES due to Derksen and Van den... 

It makes sense to compare the implications (in terms of simulation times) of using FV vs. LB in simulating turbulent-flow fields in process devices. Hoekstra (2000) demonstrated the numerical implications of applying different numerical schemes in an industrial application. He compared the outcome of his RANS simulation for a gas cyclone with that of a LES carried out by Derksen and Van den Akker (2000). Table I presents a number of numerical features of the two types of simulations. 

The next experiments are conducted on a 500-pound-per-hour combustor. This combustor, which simulates commercial practice, is shown in Figure 2. It is a wall-fired, dry-bottom unit which uses cyclone ash collection. Where possible both coal and ash samples are obtained from commercial power plants to verify the results from the experimental combustors. The combination of the two programs will hopefully give... 

There are a number of different forms of cyclone but the reverse flow cyclone represented in Fig. 1 is the most common design used in the industry. The cyclone consists of four main parts the inlet, the separation chamber, the dust chamber and the vortex finder. Tangential inlets are preferred for the separation of solid particles from gases [1]. In this study, the numerical simulation deals with the standard case of reverse flow cyclone with a tangential rectangular inlet. Cyclone dimension used in this simulation are as shown in Table 1. 

The Fluent code with the RSM turbulence model, predict very well the pressure drop in cyclones and can be used in cyclone design for any operational conditions (Figs. 3, 5, 7 and 8). In the CFD numerical calculations a very small pressme drop deviation were observed, with less than 3% of deviation at different inlet velocity which probably in the same magnitude of the experimental error. The CFD simulations with RNG k-e turbulence model still yield a reasonably good prediction (Figs. 3, 5, 7 and 8) with the deviation about 14-20% of an experimental data. It considerably tolerable since the RNG k-e model is much less on computational time required compared to the complicated RSM tmbulence model. In all cases of the simulation the RNG k-< model considerably underestimates the cyclone pressme drop as revealed by Griffiths and Boysan [8], However under extreme temperature (>850 K) there is no significant difference between RNG k-< and RSM model prediction. 

Figure 1.115 Top photographs of vortex-flow pattern formation at various flow rates when contacting water and dyed water (acid blue) solutions in the tangential-flow cyclone micro mixer. Bottom comparison of the experimentally derived image of one vortex pattern with the predicted image by CFD simulation [131] (source IMM).

Several other types of hand-held vacuum samplers have been used to collect dust from residential surfaces. One of these, the Baltimore Repair and Maintenance Study Cyclone Sampler (BRMCS) (Farfel et al 1994), has been evaluated against the HVFS. The BRMCS uses the same cyclone and catch bottle assembly as the HVS3, but a different nozzle and vacuum source. The vacuumed dust is sucked into the cyclone via a semi-rigid Tygon hose (2.54-cm o.d.) that is notched on the sampling end to simulate a nozzle. Suction is provided by a small, hand-held vacuum device (Royal Hand Vac , Model 553, 2 A). The collection efficiency for the BRMCS was determined to be 44.1 % n =6, s = 3.8) for plush uylou carpet, 61.1 % (n = 6, = 6.7) for level loop carpeting, 71.8-87.8 % n =6, s = 3.5) for upholstery and 84.7 % n = 3, s = 2.3) for wood surfaces (USEPA, 1996b). 

In studies that involve the CFD analysis of turbulent fluid flow, the k-t model is most frequently used because it offers the best compromise between width of application and computational economy (Launder, 1991). Despite its widespread popularity the k-e model, if used to generate an isotropic turbulent viscosity, is inappropriate for simulation of turbulent swirling flows as encountered in process equipment such as cyclones and hydrocyclones (Hargreaves and Silvester, 1990) and more advanced turbulence models such as the ASM or the RSM should be considered. Because these models are computationally much more demanding and involve an increased number of empirical parameters compared to the k-e model, other strategies have been worked out (Boysan et al, 1982 Hargreaves and Silvester, 1990) to avoid the isotropic nature of the classical k-e model. 

The SEPR block must be used to simulate the cyclone. However, since this block only accepts a single input stream, the reactor effluent and air feed must first be combined in a MIX operation, even though there is no physical mixing stage in the process. 

The commercial simulation programs were originally developed mainly for petrochemical applications, and none of them has a complete set of solids-handling operations. Although models for filters, crystallizers, decanters, and cyclones are present in most of the simulators, the designer may have to add user models for operations such as... 

Units are sometimes placed in series to provide multiple stages of separation. Hydrocyclones are used on ships and drilling platforms for removing oil from water [Bednarski and Listewnik, Filtration and Sep., pp. 92-97 (March/April 1988)]. Numerical simulations ofhydro-cyclone performance and flow profiles are described by Bai and Wang [Chem. Eng. Technol., 29(10), pp. 1161-1166 (2006)] and by Murphy et al. [Chem. Eng. Sci., 62, pp. 1619-1635 (2007)]. 

MM5 is a multiscale (10-1000 km) weather prediction system consisting of data analysis and initialization, dynamical prediction, and postprediction diagnosis and verification codes. The MM5 model (Grell et al. 1994) system source codes and documentation are in the public domain (i.e., the Internet) for use by any person. The MM5 model has demonstrated high skill in mesoscale simulation of tropical cyclone-orography interaction (Wu et al. 1999) as well as various mesoscale... 

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