Machine-Learning models for the prediction of the drag force exerted by a shear-thinning viscoelastic fluid in a sphere

Abstract

[Excerpt] Non-Newtonian fluid suspensions are widely used in several areas of our daily life, e.g., to produce bags, toys, car components, textiles, etc., and they are also commonly encountered in many advanced manufacturing and industrial operations, such as processing of battery slurries or hydraulic fracturing operations. However, an efficient numerical solver capable of simulating such processes is still missing in the scientific literature. For this purpose, a 3D CFD-DEM viscoelastic solver is developed in this work to handle particle-laden viscoelastic flows using a new approach, based on machine learning and deep learning models [1-3], to compute a particulate-phase drag model valid for a wide range of material parameters.