Advanced statistics to improve the physical interpretation of atomization processes

This paper reports an analysis of the physics of atomization processes using advanced statistical tools. Namely, finite mixtures of probability density functions, which best fitting is found using a Bayesian approach based on a Markov chain Monte Carlo (MCMC) algorithm. This approach takes into account eventual multimodality and heterogeneities in drop size distributions. Therefore, it provides information about the complete probability density function of multimodal drop size distributions and allows the identification of subgroups in the heterogeneous data. This allows improving the physical interpretation of atomization processes. Moreover, it also overcomes the limitations induced by analyzing the spray droplets characteristics through moments alone, particularly, the hindering of different natures of droplet formation. Finally, the method is applied to physically interpret a case-study based on multijet atomization processes.

► Finite pdf mixtures improves physical interpretation of sprays.
► Bayesian approach using MCMC algorithm is used to find the best finite mixture.
► Statistical method identifies multiple droplet clusters in a spray.
► Multiple drop clusters eventually associated with multiple atomization mechanisms.
► Spray described by drop size distribution and not only its moments.