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Accurate prediction of the time required to heat up granular materials to a target temperature is crucial for several processes. However, we do not have quantitative models to predict the average temperature or the temperature distribution of the particles. Here, we computationally investigate the scaling of heat transfer in granular flows in rotating drums. Based on our simulations, which include a wide range of system and material properties, we identify the appropriate characteristic time that is used to derive equations that predict the particles' average temperature and the particles' temperature distribution.

A novel method for determining the thermal efficiency of the SparkJet is proposed. A SparkJet is attached to the end of a pendulum. The motion of the pendulum subsequent to a single spark discharge is measured using a laser displacement sensor. The measured displacement vs time is compared with the predictions of a theoretical perfect gas model to estimate the fraction of the spark discharge energy which results in heating the gas (i.e., increasing the translational-rotational temperature). The results from multiple runs for different capacitances of c = 3, 5, 10, 20, and 40µF show that the thermal efficiency decreases with higher capacitive discharges.