Predicting a clog: finding signatures of clog formation in hopper flow using machine learning methods

If a bucket is filled with grains and a hole is cut in the bottom, the grains will flow out of this makeshift hopper. In contrast to a fluid like water, this hopper flow exhibits some unusual properties. With water, the flow rate decreases with the fill height as the pressure falls, but the granular flow rate is constant; that is, until the whole system suddenly arrests due to a clog at the outlet. Previous work by Thomas and Durian (PRL 2015) suggests that this hopper flow is Poissonian, where the system samples microstates until it finds one that can cause a clog. Koivisto and Durian (PRE 2017) then found the microstates being sampled are primarily configuration states, rather than momenta states. So what causes this sudden transition from a flowing state to a clogged one? In order to capture the incredibly large configuration space of grain positions, we constructed an automated, self refilling hopper. We then pair our large data set with machine learning techniques in order to explore general signatures of clog formation within granular flows.