Dimensional analysis meets AI for non-Newtonian droplet generation.

ABSTRACT: Non-Newtonian droplets are used across various applications, including pharmaceuticals, food processing, drug delivery and material science. However, predicting droplet formation using such complex fluids is challenging due to the intricate multiphase interactions between fluids with varying viscosities, elastic properties and geometrical constraints. In this study, we introduce a novel hybrid machine-learning architecture that integrates dimensional analysis with machine learning to predict the flow rates required to generate droplets with specified sizes in systems involving non-Newtonian fluids. Unlike previous approaches, our model is designed to accommodate shear-rate-dependent viscosities and a simple estimate of the elastic properties of the fluids. It provides accurate predictions

SUBMITTER: Hormozinezhad F 

PROVIDER: S-EPMC11834948 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature