Droplet distribution function

The formulation that has been given here is not the only approach to the description of two-phase flows with nonequilibrium processes. Many different viewpoints have been pursued textbooks are available on the subject [43], [44], and a reasonably thorough review recently has been published [45]. Combustion seldom has been considered in this extensive literature. Most of the work that has addressed combustion problems has not allowed for a continuous droplet distribution function but instead has employed a finite number of different, discrete droplet sizes in seeking computer solution sets of conservation equations [5]. The present formulation admits discrete sizes as special cases (through the introduction of delta functions in fj) but also enables influences of continuous distributions to be investigated. A formulation of the present type recently has been extended to encompass thick sprays [25]. Some other formulations of problems of multiphase reacting flows have been mentioned in Sections 7.6 and 7.7. 

The boundary conditions for the spray equations specify the droplet-wall interactions, as well as the mass flow rate and the droplet distribution function at the nozzle exit. Additional details and references can be found in Ref. [4]. 

Most distribution functions contain an average size and a variance parameter typicaUy based on the cumulative droplet number or volume distributions. For example, the Rosin-Rammler function uses the cumulative Hquid volume as a means of expressing the distribution. It can be expressed as... 

In order to characterize quantitatively the polydisperse morphology, the shape and the size distribution functions are constructed. The size distribution function gives the probability to find a droplet of a given area (or volume), while the shape distribution function specified the probability to find a droplet of given compactness. The separation of the disconnected objects has to be performed in order to collect the data for such statistics. It is sometimes convenient to use the quantity v1/3 = [Kiropiet/ ]1 3 as a dimensionless measure of the droplet size. Each droplet itself can be further analyzed by calculating the mass center and principal inertia momenta from the scalar field distribution inside the droplet [110]. These data describe the droplet anisotropy. 

Many droplet size distributions in random droplet generation processes follow Gaussian, or normal distribution pattern. In the normal distribution, a number distribution function/(D) may be used to determine the number of droplets of diameter D ... 

Mugele and Evans14231 proposed the upper-limit distribution function based on their analyses of various distribution functions and comparisons with experimental data. This distribution function is a modified form of the log-normal distribution function, and for droplet volume distribution it is expressed as ... 

The upper-limit distribution function assumes a finite minimum and maximum droplet size, corresponding to a y value of -oo and +oo, respectively. The function is therefore more realistic. However, similarly to other distribution functions, it is difficult to integrate and requires the use of log-probability paper. In addition, it usually requires many trials to determine a most suitable value for a maximum droplet size. 

Some other distribution functions have also been derived from analyses of experimental data,1429114301 or on the basis of probability theory J431] Hiroyasu and Kadota 3l l reported a more generalized form of droplet size distribution, i.e., /-scpta/e distribution. It was shown that the -square distribution fits the available spray data very well. Moreover, the -square distribution has many advantages for the representation of droplet size distribution due to the fact that it is commonly used in statistical evaluations. 

To characterize a droplet size distribution, at least two parameters are typically necessary, i.e., a representative droplet diameter, (for example, mean droplet size) and a measure of droplet size range (for example, standard deviation or q). Many representative droplet diameters have been used in specifying distribution functions. The definitions of these diameters and the relevant relationships are summarized in Table 4.2. These relationships are derived on the basis of the Rosin-Rammler distribution function (Eq. 14), and the diameters are uniquely related to each other via the distribution parameter q in the Rosin-Rammler distribution function. Lefebvre 1 calculated the values of these diameters for q ranging from 1.2 to 4.0. The calculated results showed that Dpeak is always larger than SMD, and SMD is between 80% and 84% of Dpeak for many droplet generation processes for which 2left-hand side of Dpeak. The ratio MMD/SMD is... 

A).632 Characteristic diameter 63.2% of total volume of droplets are of smaller diameters than this value V=63.2% X (X in Rosin-Rammler distribution function)... 

Detailed modeling study of practical sprays has a fairly short history due to the complexity of the physical processes involved. As reviewed by O Rourke and Amsden, 3l() two primary approaches have been developed and applied to modeling of physical phenomena in sprays (a) spray equation approach and (b) stochastic particle approach. The first step toward modeling sprays was taken when a statistical formulation was proposed for spray analysis. 541 Even with this simplification, however, the mathematical problem was formidable and could be analyzed only when very restrictive assumptions were made. This is because the statistical formulation required the solution of the spray equation determining the evolution of the probability distribution function of droplet locations, sizes, velocities, and temperatures. The spray equation resembles the Boltzmann equation of gas dynamics[542] but has more independent variables and more complex terms on its right-hand side representing the effects of nucleations, collisions, and breakups of droplets. 

Two numerical methods have been used for the solution of the spray equation. In the first method, i.e., the full spray equation method 543 544 the full distribution function / is found approximately by subdividing the domain of coordinates accessible to the droplets, including their physical positions, velocities, sizes, and temperatures, into computational cells and keeping a value of / in each cell. The computational cells are fixed in time as in an Eulerian fluid dynamics calculation, and derivatives off are approximated by taking finite differences of the cell values. This approach suffersfrom two principal drawbacks (a) large numerical diffusion and dispersion... 

In the second method, i.e., th particle method 546H5471 a spray is discretized into computational particles that follow droplet characteristic paths. Each particle represents a number of droplets of identical size, velocity, and temperature. Trajectories of individual droplets are calculated assuming that the droplets have no influence on surrounding gas. A later method, 5481 that is restricted to steady-state sprays, includes complete coupling between droplets and gas. This method also discretizes the assumed droplet probability distribution function at the upstream boundary, which is determined by the atomization process, by subdividing the domain of coordinates into computational cells. Then, one parcel is injected for each cell. 

Packer and Rees [3] extended the work of Tanner and Stejskal by the development of a theoretical model using a log-normal size distribution function. Measurements made on two water-in-oil emulsions are used to obtain the self-diffusion coefficient, D, of the water in the droplets as well as the parameters a and D0 0. Since then, NMR has been widely used for studying the conformation and dynamics of molecules in a variety of systems, but NMR studies on emulsions are sparse. In first instance pulsed field gradient NMR was used to measure sdf-diffusion coefficients of water in plant cells (e.g. ref. [10]). In 1983 Callaghan... 

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