Inferring the Turbulent Breakup of Colloidal Aggregates Using Graph Neural Networks

TL;DR

This paper demonstrates the effective use of Graph Neural Networks to predict and classify the breakup of colloidal aggregates in turbulent flows, outperforming traditional statistical methods.

Contribution

It introduces GNN-based models for predicting aggregate fragmentation and tensile forces, advancing the application of machine learning in complex fluid dynamics problems.

Findings

GNN classifiers accurately distinguish breaking from non-breaking aggregates.

Regression models predict maximal tensile forces with high statistical accuracy.

GNN models outperform traditional statistical predictions based on mean flow quantities.

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, and generally perform better than the statistical prediction based on mean field quantities. This work paves the way for future use of Graph Neural Networks to quantify aggregate breakup in large population of aggregates suspended in complex flow configurations.