Dimensional analysis meets AI for non-Newtonian droplet generation
Farnoosh Hormozinezhad, Claire M. Barnes, Alexandre Fabregat, Salvatore Cito, Francesco Del Giudice
Abstract
values of up to 0.82 for unseen data. The significance of our work lies in its ability to generalize across a broad range of non-Newtonian systems having different viscosity curves, offering a powerful tool for optimizing droplet generation. This model represents a significant advancement in the application of machine learning to microfluidics, providing new opportunities for efficient experimental design in complex multiphase systems.
Topics & Concepts
Non-Newtonian fluidNewtonian fluidDrug deliveryNanotechnologyMechanicsProcess engineeringMaterials scienceComputer scienceEngineeringPhysicsElectrohydrodynamics and Fluid DynamicsInnovative Microfluidic and Catalytic Techniques Innovation