Stochastic methods for slip prediction in a sheared granular system

TL;DR

This paper uses stochastic state space models to analyze force network changes in a sheared granular system, enabling earlier slip event detection and identifying critical thresholds for slip initiation.

Contribution

It introduces a novel application of stochastic models to detect slip precursors from force network measures in granular systems.

Findings

Force measures predict slips earlier than wall movement.

Global force network changes trigger slip events when exceeding a critical size.

Local force changes often do not lead to global slip.

Abstract

We consider a sheared granular system experiencing intermittent dynamics of stick-slip type via discrete element simulations. The considered setup consists of a two-dimensional system of soft frictional particles sandwiched between solid walls, one of which is exposed to a shearing force. The slip events are detected using stochastic state space models applied to various measures describing the system. We show that the measures describing the forces between the particles provide earlier detection of an upcoming slip event than the measures based solely on the wall movement. By comparing the detection times obtained from the considered measures, we observe that a typical slip event starts with a local change in the force network. However, some local changes do not spread globally over the force network. For the changes that become global, we find a sharp critical value for their size. If the size of a global change exceeds the critical value, then it triggers a slip event; if it does not, then a much weaker micro-slip follows. Quantification of the changes in the force network is made possible by formulating clear and precise measures describing their static and dynamic properties.