Flow models classification

For multiphase systems a rough distinction can be made between systems with separated flows and those with dispersed flows. This classification is not only important from a physical point of view but also from a computational perspective since for each class different computational approaches are required. For multiphase systems involving multiphase flow both Eulerian, mixed Eulerian-Lagrangian, and two-material free surface methods can be used. An excellent review on models and numerical methods for multiphase flow has been presented by Stewart and Wendroff (1984). A similar review with emphasis on dilute gas-particle flows has been presented by Crowe (1982). 

One should know turbulence characteristics to estimate the characteristic time of reagents mixing. They proposed a lot of various methods for estimation of mixing time in turbulent flows particularly the review on this theme and mixing models classification are presented in [130]. Since in [125] liquid movement is described by average over Reynolds Navie-Stocks equations with the use of K-e closing, so the characteristic time of turbulence mixing can be estimated as [1,32,125] ... 

A broad classification of the various quasi-continuum models is presented in Table 12.1. The simplest is clearly the one-dimensional pseudohomogeneous plug-flow model (Al-a) in which the radial gradients of heat and mass within a tube are neglected. Then complicating factors can be added, one at a time, to allow for increasing reality,... 

The dynamic behavior of an adsorption system may be classified according to the nature of the mass transfer front and the comptexityjof the mathematical model required to describe the system. The nature of the mass transfer front is determined solely by the form of the equilibriiim relationship, its outlined above, while the complexity of the mathematiciii model depends oh the concentration level, the choice of rate equation, anid the choice of flow model. The following classification scheme provides a useful framework for more detailed analysis. 

Rajamani, R. K. and Milin, L., Fluid flow model of the hydrocyclone for concentrated slurry classification , in L. Svarovsky and M. T. Thew (Eds), Hydrocyclones, Analysis and Applications, Kluwer Academic Publishers, Dordrecht, 95-108 (1992)... 

In this chapter, an integrated four-stage supply chain network is considered with forward and reverse product flows with commercial returns, which could be potentially recovered by light repair operations or by refurbishing. Further to the literature in CLSC network design models, classification of the product returns in the supply chain based on their quality and customer behavior toward buying refurbished products are considered in the model. 

Modelling of steady-state free surface flow corresponds to the solution of a boundary value problem while moving boundary tracking is, in general, viewed as an initial value problem. Therefore, classification of existing methods on the basis of their suitability for boundary value or initial value problems has also been advocated. 

The way in which the force /j j is modeled clearly determines the type of the pneumatic flow this has been discussed earlier in Section 14.2.2, where we considered the classification of different types of flow. In the following we will give a detailed description for the force in a way that suits a particular type of flow. This approach will be adequate for so-called dilute-phase flow or, more generally speaking, for homogeneous flow where the particles move separately. 

Brayshaw, M.D., 1990. Numerical model for the inviscid flow of a fluid in a hydrocyclone to demonstrate the effects of changes in the vorticity function of the flow field on particle classification. International Journal of Mineral Processing, 29, 51. 

Use of multivariate approaches based on classification modelling based on cluster analysis, factor analysis and the SIMCA technique [98,99], and the Kohonen artificial neural network [100]. All these methods, though rarely implemented, lead to very good results not achievable with classical strategies (comparisons, amino acid ratios, flow charts) and, moreover it is possible to know the confidence level of the classification carried out. 

The prime difficulty of modeling two-phase gas-solid flow is the interphase coupling, which deals with the effects of gas flow on the motion of solids and vice versa. Elgobashi (1991) proposed a classification for gas-solid suspensions based on the solid volume fraction es, which is shown in Fig. 2. When the solid volume fraction is very low, say es< 10-6, the presence of particles has a negligible effect on the gas flow, but their motion is influenced by the gas flow for sufficiently small inertia. This is called one-way coupling. In this case, the gas flow is treated as a pure fluid and the motion of particle phase is mainly controlled by the hydrodynamical forces (e.g., drag force, buoyancy force, and so... 

The long-term goal in the science of thermochemical conversion of a solid fuel is to develop comprehensive computer codes, herein referred to as a bed model or CFSD (computational fluid-solid dynamics). Firstly, this CFSD code must be able to simulate basic conversion concepts, with respect to the mode, movement, composition and configuration of the fuel bed. The conversion concept has a great effect on the behaviour of the thermochemical conversion process variables, such as the molecular composition and mass flow of conversion gas. Secondly, the bed model must also consider the fuel-bed structure on both micro- and macro-scale. This classification refers to three structures, namely interstitial gas phase, intraparticle gas phase, and intraparticle solid phase. Commonly, a packed bed is referred to as a two-phase system. 

Because the flow equations are nonlinear and coupled, they defy simple classification. Nevertheless, it is valuable to identify and consider the behaviors of certain attributes of the equations as represented by the linear model equations. 

Coal combustion processes can be classified based on process type (see Table 9.1), even though classification based on the particle size, the flame type, the reactor flow type, or the mathematical model complexity is also possible [7]. 

Classification of MEISs. Models with variable parameters with variable flows and spatially inhomogeneous systems with constraints on the macroscopic kinetics and without them. Specific features of modifications and their comparative capabilities. 

The hemodynamic effects of compounds supposed to affect the cardiovascular system are evaluated by measuring preload and afterload of the heart, contractility, heart rate, cardiac output and peripheral or coronary flow. To measure these cardiovascular parameters accurately, the use of larger animals such as dogs or pigs is necessary. This experimental model allows the classification of test drugs according to their action as having ... 

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