Computation of bubble mean diameters using probability distribution functions

Abstract

Bubble size is a relevant parameter of gas-liquid dispersions, which is the basis of several unit operations used in the chemical processes industries. For process and equipment design bubbles, size distributions are usually obtained from experimental data and used to compute mean diameters. This work evaluates the use of probability density functions to model experimental bubble size distributions and estimate bubble mean diameters from the raw moments of the adjusted distributions. Eight univariate probability density functions were tested. Among them the Gamma distribution is shown to fit better the experimental bubble size distributions, but the Rosin-Rammler distribution gives the best estimations of bubble mean diameters from the computed raw moments. The results may be valuable for computational fluid dynamics applications.Bubble size is a relevant parameter of gas-liquid dispersions, which is the basis of several unit operations used in the chemical processes industries. For process and equipment design bubbles, size distributions are usually obtained from experimental data and used to compute mean diameters. This work evaluates the use of probability density functions to model experimental bubble size distributions and estimate bubble mean diameters from the raw moments of the adjusted distributions. Eight univariate probability density functions were tested. Among them the Gamma distribution is shown to fit better the experimental bubble size distributions, but the Rosin-Rammler distribution gives the best estimations of bubble mean diameters from the computed raw moments. The results may be valuable for computational fluid dynamics applications.