Competition analysis of grain flow versus clogging by means of information theory

The different flow regimes in a two-dimensional silo with a vibrated base are studied in terms of the usual statistical techniques and information theory. The passage of granular material through the mouth of the silo is analyzed by real-time analysis of images captured by a standard video camera. The brightness of these images is measured and recorded at very small time intervals (100 frames per second). The experiment is repeated for different values of the vibration intensity. Data recognizer wlzip directly treats the resulting b(t) files (brightness time series) based on data compressor techniques, yielding the information content measured by the mutability \(\mu\) $\mu$ function. \(\mu\) $\mu$ has not previously been considered as part of the conventional treatment of flow in granular media. The results obtained here clearly demonstrate the usefulness of mutability as a tool for distinguishing between different flowing regimes directly from the brightness sequence, with no manipulation of the series. This shows that information theory techniques can provide a complementary description of the discharge of granular materials and its control through data compression algorithms.