Spray computations

Raju, M. S., and W. A. Sirignano. 1990. Multicomponent spray computations in a modified centerbody combustor. J. Propulsion Power 6 97-105. 

The FSCBG aerial spray computer program is the result of more than a decade of refinement and verification of spray dispersion models used by the USDA Forest Service and the U. S. Army for predicting the drift, deposition and canopy penetration of particles and drops downwind from aircraft releases. This paper describes the mathematical framework of the models and selected applications of the models to military and Forest Service projects. 

T. L. Georjon, R. D. Reitz A drop-shattering collision model for multidimensional spray computations, Atomization Sprays 9, 231-254 (1999). 

Hybrid Droplet-Parcel Algorithm for Spray Computations... 

Spray characteristics are those fluid dynamic parameters that can be observed or measured during Hquid breakup and dispersal. They are used to identify and quantify the features of sprays for the purpose of evaluating atomizer and system performance, for estabHshing practical correlations, and for verifying computer model predictions. Spray characteristics provide information that is of value in understanding the fundamental physical laws that govern Hquid atomization. 

Fine water spray systems may be potentially superior to CO9 apphcations and may replace halon environments such as telephone central offices and computer rooms. In the fine spray dehveiy system, water is delivered at relatively high pressure (above 100 psi [0.689 MPa]) or by air atomization to generate droplets significantly smaUer than those generated by sprinklers. Water flow from a fine spray nozzle potentially extinguishes the fire faster than a sprinkler because the droplets are smaUer and vaporize more quickly. Preliminaiy information indicates that the smaller the droplet size, the lower the water flow requirements and the less chance of water damage. 

Figure 7.6 shows the computer-controlled spray system ChromaJet DS 20 (with permission of Desaga). 

Application of the test substance to the test system is without doubt the most critical step of the residue field trial. Under-application may be corrected, if possible and if approved by the Study Director, by making a follow-up application if the error becomes known shortly after the application has been made. Over-application errors can usually only be corrected by starting the trial again. The Study Director must be contacted as soon as an error of this nature is detected. Immediate communication allows for the most feasible options to be considered in resolving the error. If application errors are not detected at the time of the application, the samples from such a trial can easily become the source of undesirable variability when the final analysis results are known. Because the application is critical, the PI must calculate and verify the data that will constitute the application information for the trial. If the test substance weight, the spray volume, the delivery rate, the size of the plot, and the travel speed for the application are carefully determined and then validated prior to the application, problems will seldom arise. With the advent of new tools such as computers and hand-held calculators, the errors traditionally associated with applications to small plot trials should be minimized in the future. The following paragraphs outline some of the important considerations for each of the phases of the application. 

Mathematical models are also used for estimating releases, but these are usually relatively simple. For example, if it is known that X kg of a chlorofluorocarbon is manufactured annually, and Y percent enters spray cans, and Z percent of a spray can is usually left unexhausted, then XY(100-Z)/10 kg of that CFC are released to the atmosphere per year. The average discharge rate (kg/sec) nationwide then can be computed easily. (For simplicity in this example, we ignore the contributions from leaking discarded cans and changes in production and use levels.)... 

This review paper is restricted to stirred vessels operated in the turbulent-flow regime and exploited for various physical operations and chemical processes. The developments in the field of computational simulations of stirred vessels, however, are not separated from similar developments in the fields of, e.g., turbulent combustion, flames, jets and sprays, tubular reactors, and multiphase reactors and separators. Fortunately, there is a strong degree of synergy and mutual cross-fertilization between these various fields. This review paper focuses on aspects specific to stirred vessels (such as the revolving impeller, the resulting strong spatial variations in turbulence properties, and the macroinstabilities) and on the processes carried out in them. 

Fluidized-bed spray coating, 10 272-273 Fluidized-bed technologies, 15 497 Fluid mechanical pressure, 11 740 Fluid mechanics, 11 735-791. See also Flow(s) Fluid motion Rheology computational, 11 777—781 dimensional analysis in,... 

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. 

Due to computer storage and run time limitations, it is not yet possible to accurately model the details of flows around each individual droplet in a spray. Thus, empirical or semi-empirical correlations are typically used to model the exchange processes between droplets and gas. Correlations for drag coefficients have been suggested by many researchers.[45l[559h568Fl571l For thin sprays, the drag... 

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