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ESCI SJR: 0.206 SNIP: 0.446 CiteScore™: 0.5

ISSN Печать: 2152-5102
ISSN Онлайн: 2152-5110

# International Journal of Fluid Mechanics Research

DOI: 10.1615/InterJFluidMechRes.v24.i4-6.200
pages 643-652

## Theoretical Determination of Spray Drop Size Distributions Part 1: Description of the Procedure

Jean Cousin
UMR 6614-CORIA, Technopole du Madrillet, B.P. 12 Avenue de l'Université 76801 Saint-Etienne du Rouvray Cedex, France
Christophe Dumouchel
Université et INSA de Rouen France

### Краткое описание

The study presented in this paper reconsiders the use of the Maximum Entropy Formalism (M.E.F.) for the prediction of spray drop size distribution. This formalism is a statistical tool that allows the prediction of a probability distribution consistently with information related to the process studied. The study first shows that the formalism has to be adapted according to the distribution sought. Number and volume distributions are commonly used to describe a spray drop size distribution. However they do not contain similar information on the spray as the volume distribution is based on the relationship between the volume and the diameter of the drops and therefore always includes the knowledge of the shape of the particles. The difference between the two distributions is of paramount importance as far as the application of the Maximum Entropy Formalism is concerned and has to be taken into account when the entropy of the distribution is expressed. Furthermore, the precaution taken in the definition of the entropy leads to the prediction of distributions consistent with each other. Second, the tricky step of the writing of the constraints is tackled. The constraints must contain physical information specific to the problem studied. It is found that this could be achieved by using a single constraint based on the definition of a mean drop diameter of the Dqp series. Finally, an autonomous procedure for the prediction of drop size distributions is suggested in situations where the linear theory may be applied and leads to the prediction of a mean diameter of the distribution.

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