This work presents the further development and the validation of the Discrete Ordinates Model for thermal radiation which is implemented in OpenFOAM® for application to packed beds of biomass particles. This radiation model is an important part of a more comprehensive model which simulates the thermal conversion of discrete phase (here for instance wet biomass particles) which flows continuously inside an indirectly heated rotary kiln. The comprehensive Eulerian-Lagrangian model integrates three-dimensional, time-resolved simulation of the essential chemical and physical processes occurring within and in-between the moving bed of particles. This is realized by combining the particle collision models for non-reactive dense flows with models for heat transport, phase change and chemical reaction for multiphase reacting flow in the framework of OpenFOAM®.
For the thermal treatment of solid particles, convection and radiation heat transfer methods couple the energy exchange between the reactor wall, gas- and disperse phase. The original implementation of the finite volume Discrete Ordinate Model (fvDOM) valid for a dilute particulate phase neglects the effect of local opacity due to the existence of individual particles. ... mehrHowever, in the present application, a dense-packed bed of the particulate phase exists in the reactor. Therefore, in this study, this direction-based radiation model is adjusted for a computational cell with arbitrary particle volume fractions.
To validate the results with the present thermal radiation model, first a simple test case with heating the bed of particles from the top of the domain is carried out. A second test relates to a laboratory-scale reactor. The results of the improved fvDOM are compared to the original implementation of OpenFOAM® and the more simple and computationally cheap P-1 radiation model. In general, the P-1 model largely overpredicts the radiative heat transfer while the original fvDOM underpredicts the heat flux by about 15% for the first test case. The improved model delivers results within 1% deviation at the expense of maximum 10% of the increase in the computational time.