Clog prediction in granular hoppers using machine learning methods

Grains discharge from a hopper at constant rate, proceeding probabilistically until a stable arch forms. Thomas and Durian (PRL 2015) showed that recasting the characteristic measure of a flow event from the average mass discharged to the fraction of flow microstates that precede (i.e. cause) a clog explains why the former grows as an exponential function of hole diameter, rather than a critical power law. This makes clear that clogs form as the flow brings new microstates into the vicinity of the outlet, which are randomly sampled until a stable arch is found. Characterizing the flow microstates that cause clogs should then better inform a predictive framework. As a first step, Koivisto and Durian (PRE 2017) found that the same statistics governed hoppers in air or submerged in a viscous fluid. This implies that clog formation depends primarily on position degrees of freedom; however, the phase space of grain microstates in a hopper is extremely high-dimensional. Here, we apply deep learning to probe the function space of position microstates in a two-dimensional hopper, to identify and separate out characteristic structures responsible for clogging. Preliminary analysis of a small dataset gives a cross validation success rate of 90%.