Thermal conductivity modeling of monodispersed microspheres using discrete element method

Particle beds are widely used in various systems and processes, such as particle heat exchangers, granular flow reactors, and additive manufacturing. Accurate modeling of the thermal conductivity of particle beds and understanding of their heat transfer mechanisms are important. However, previous models were based on simple cubic packing of particles, which could not accurately represent the actual heat transfer processes under certain conditions. Here, we examine the effect of the packing structure on the thermal conductivity of particle beds. We use monodispersed silica microspheres with average particle sizes ranging from 23 to 330 mu m as a model material. We employ a transient hot-wire technique to measure the thermal conductivity of the particle beds with packing density of 43%-57% within a temperature range of room temperature to 500 degrees C and under N-2 gaseous pressures of 20-760 Torr. We then use a discrete element method (DEM) to obtain the realistic packing structure of the particles, which is then fed into a finite-element model (FEM) to calculate the thermal conductivity, with the consideration of solid conduction, gas conduction, and radiation heat transfer. Our results show that the thermal conductivity model based on the more realistic random packing structure derived from the DEM shows better agreement with the experimental data compared to that based on the simple cubic-packing structure. The combined DEM and FEM methodology can serve as a useful tool to predict the effective thermal conductivity of particle beds and to quantify different heat transfer mechanisms under various conditions.

Automated summary

Particle beds are widely used in systems like heat exchangers and reactors. This study investigated the effect of packing structure on thermal conductivity using silica microspheres as model material. Results showed that a model based on a more realistic packing structure had better agreement with experimental data, indicating the potential of the combined DEM and FEM methodology in predicting thermal conductivity.