GrainLearning

An artificial brain for interpreting and accelerating physics-based simulations of granular materials

image credits: Shutterstock

How to keep dikes safe with rising sea levels? Why are ripples formed in sand? What can we prepare for landing on Mars? At the center of these questions is the understanding of how the grains, as a self-organizing material, collide, flow, or get jammed and compressed. State-of-the-art algorithms allow for simulating millions of grains individually in a computer. However, these simulations can take very long, and the big data about particle motion is very difficult to interpret and generalize, say from a simulation of avalanches to free-standing sandcastles. In this project, we will use machine learning to (1) extract hidden links between grain, microstructural, and macroscopic properties, and (2) instantaneously generate microstructures that satisfy given macroscopic constraints, from an existing database. As proof of concept, the workflow will be deployed on the cloud and used to find optimal microstructure/grain properties that define a SMART granular material.

Participating organisations

Netherlands eScience Center
University of Twente

Team

Contact person

Luisa Orozco

Luisa Orozco

Netherlands eScience Center
Mail Luisa
AJ
Aron Jansen
eScience Research Engineer
Netherlands eScience Center
HC
Hongyang Cheng
Principal investigator
University of Twente
Luisa Orozco
Luisa Orozco
eScience Research Engineer
Netherlands eScience Center
Rena Bakhshi
Rena Bakhshi
eScience Coordinator
Netherlands eScience Center