Prédiction, par le biais de l'analyse dimensionnelle, des diamètres caractéristiques des gouttelettes et de l'indice de polydispersité lors de la pulvérisation par une buse bifluide

Autor: K. Lachin, M. Niane, M. Person, J. Mazet, G. Delaplace, C. Turchiuli
Přispěvatelé: Paris-Saclay Food and Bioproduct Engineering (SayFood), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), SOREDAB, Soparind Bongrain, Unité Matériaux et Transformations - UMR 8207 (UMET), Centrale Lille-Institut de Chimie du CNRS (INC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Funding of the internship of Mamadou Lamine Niane by Soredab
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Chemical Engineering Science
Chemical Engineering Science, 2023, 265, pp.118187. ⟨10.1016/j.ces.2022.118187⟩
ISSN: 0009-2509
Popis: International audience; 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.
Databáze: OpenAIRE
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