Prediction of droplets characteristic diameters and polydispersity index induced by a bifluid spraying nozzle by the means of dimensional analysis

This work focuses on the study of sprays generated through a bifluid nozzle and modelling characteristic spray properties (two diameters polydispersity index) using dimensional analysis. Two types dimensionless models were identified for each target property from 75 experimental points considered. The first type used conventional monomial-exponential shape equation, second applied identification machine-learning. Although mostly satisfactory when considering diameters, they nevertheless showed clear limitations addressed by machine-learning models. approach also failed to identify equation index. provided an identifying this index main parameters governing atomization. provides foundation proposing two-parameters model that predicts particle size distribution. combination analysis with thus paves way physically rigorous easy-to-use capable predicting full distributions.