Abstract Particle-resolved CFD simulations are widely used for the predictions of particle-scale flow, transport processes and chemical reactions. However, the effect of turbulence models used in such simulations is not… Click to show full abstract
Abstract Particle-resolved CFD simulations are widely used for the predictions of particle-scale flow, transport processes and chemical reactions. However, the effect of turbulence models used in such simulations is not quantitatively established. In the present work, particle-resolved simulations are performed to understand the influence of different turbulence models (standard k − e (KE), SST k − ω (SST), SSG Reynolds-stress (SSG), BSL Reynolds-stress (BSL) and LES WALE (LES)) on the particle-scale predictions of the flow, heat transfer and reactions. A comparison of the PIV measurements and particle-resolved simulations of a square column containing 72 spherical particles with a tube-to-particle diameter ratio (TPDR) of ∼3.6 and Rebed of 9765 was performed. The e−based models (KE and SSG) under-predicted the velocity (Vx and Vy) profiles due to the over-estimation of the turbulence parameters (k and e) and an opposite behaviour was observed for the ω−based models (SST and BSL). The LES model showed a better agreement with the measurements. In addition, numerical investigations of methane steam reforming reactions were performed in a packed bed containing 30 randomly packed 7-hole cylindrical particles with a TPDR of 5, and Ret of 50,000. The e−based models predicted lower values of velocities, temperature and species concentration in the bulk region (and higher values on the particle surfaces) resulting into higher CH4 conversion compared to the ω−based models, whereas the LES model showed the highest ΔP and CH4 conversion. Overall, the predictions of the LES model were found to be closer to the experiments. The predictions of the RSM (SSG and BSL) models did not show any improvements over the eddy-viscosity (KE and SST) models.
               
Click one of the above tabs to view related content.