Inferring the turbulent breakup of colloidal aggregates using Graph Neural Networks

Abstract

Solid aggregates in turbulent suspensions may break under the action of shear stresses. We explore the use of Graph Neural Networks (GNN) to infer aggregate fragmentation once the aggregate structure and flow velocity gradients are known. We consider two models: the first GNN is a classifier, trained to distinguish aggregates that break from those that do not; the second GNN is a regression model, trained to predict the maximal tensile force within each aggregate in a given flow condition. We show that both models complete their task with a high statistical accuracy, also generalizing to aggregates of different sizes, and generally performing better than the statistical prediction based on mean field quantities. This work paves the way for future use of GNN to quantify aggregate breakup in a large population of aggregates suspended in complex flow configurations, as it takes place in the wet production of fine powders and in the transport of sediments in environmental flows.