What makes a clog: characterizing 2D granular hopper flows using machine learning methods

In hopper flow grains discharge at a constant rate, independent of fill height, and can form stable arches that clog the system and arrest flow. Thomas and Durian (PRL 2015) supported a Poissonian formulation of hopper flow by measuring the fraction of flow microstates, F, which cause a clog. New states are brought to the outlet with a constant sampling time, until the flow randomly finds a stable state which forms an arch. Koivisto and Durian (PRE 2017) then showed the random states being sampled depend on configuration, rather than momenta, by measuring similar scaling of F with outlet diameter between submerged and dry hoppers. We expand on this work by characterizing the individual microstates. We use a vertical, 2D hopper to image and track individual states, a new protocol to isolate unique configurations, and a novel machine learning analysis to leverage all the data, whether it is flowing or clogging. We explore the ability to predict whether a state will cause a clog to form based on only it’s image.