Searching for clog formation in hopper flow through comparative machine learning analyses

From salt in saltshakers to sheep in pens, particles flowing through outlets is ubiquitous. In granular hoppers, gravity causes grains to emerge from the outlet at a constant rate until, suddenly, a stable arch or dome forms and arrests any further flow. The formation of such clogs is a Poisson process (Thomas and Durian, PRL 2015); intuitively, the discharge randomly brings new flow microstates into the outlet region until one arises that causes a clog. These clog-causing flow microstates seem to be primarily configurational, rather than momentum-based (Koivisto and Durian, PRE 2017), but their exact nature remains to be elucidated. The high dimensional nature of the configuration states foils manual attempts to categorize them as flowing or clog-causing, so we instead employ machine learning techniques. We collect a large number of free-flowing, clog-causing, and totally-clogged configurations using a quasi-2d automated hopper and a high-speed camera. We then compare a variety of machine learning algorithms to probe for a signature of incipient clog formation.