Prediction of droplets characteristic diameters and polydispersity index induced by a bifluid spraying nozzle by the means of dimensional analysis. (16th January 2023) - Lachin, K.; Niane, M.; Person, M.; Mazet, J.; Delaplace, G.; Turchiuli, C.
Graphical abstract: Highlights: A rigorous dimensional analysis (DA) was performed on bifluid spray properties. Two characteristic spray diameters and a polydispersity index were considered. Models were established with conventional equation shapes and machine-learning (ML) Spray properties could be accurately modelled through a DA/ML approach. Particle Size Distributions could also be predicted using this methodology. Abstract: This work focuses on the study of sprays generated through a bifluid nozzle and the modelling of characteristic spray properties (two characteristic diameters and a polydispersity index) using dimensional analysis. Two types of dimensionless models were identified for each spray target property from the 75 experimental points considered. The first type used a conventional monomial-exponential shape equation, and the second applied shape identification through machine-learning. Although conventional models of the first type were mostly satisfactory when considering the characteristic diameters, they nevertheless showed clear limitations addressed by the machine-learning identified models. The conventional approach also failed to identify a satisfactory equation for the polydispersity index. The machine-learning approach provided an equation identifying this index to the main dimensionless parameters governing atomization. This identification provides a foundation for proposing a two-parameters dimensionless model that predicts spray particle size distribution. The combination of dimensional analysis with machine-learning equation identification thus paves the way to physically rigorous and easy-to-use models capable of predicting characteristic properties and full distributions.