Abstract
Based upon the two fluid model (TFM) theory, a CFD model was implemented to investigate a cold multiphase-fluidized bubbling bed reactor. The key variable used to characterize the fluid dynamic of the experimental system, and compare it to model predictions, was the time-pressure drop induced by the bubble motion across the bed. This time signal was then processed to obtain the power spectral density (PSD) distribution of pressure fluctuations. As an important aspect of this work, the effect of the sampling time scale on the empirical power spectral density (PSD) was investigated. A time scale of 40 s was found to be a good compromise ensuring both simulation performance and numerical validation consistency. The CFD model was first numerically verified by mesh refinement process, after what it was used to investigate the sensitivity with regards to minimum fluidization velocity (as a calibration point for drag law), restitution coefficient, and solid pressure term while assessing his accuracy in matching the empirical PSD. The 2D model provided a fair match with the empirical time-averaged pressure drop, the relating fluctuations amplitude, and the signal’s energy computed as integral of the PSD. A 3D version of the TFM was also used and it improved the match with the empirical PSD in the very first part of the frequency spectrum.
Highlights
Fluidized bubbling reactors are extensively employed in the industry, both for chemical and biochemical processes as well as for power generation, and one of the main reasons is due to their optimal level of heat and mass transfer induced by the bubbling turbulence (Singh et al, 2013)
In this case, all the 2D model simulations showed some deficiency in reproducing the experimental power spectral density (PSD) distribution with an unrealistic presence of peaks in the low frequencies zone
The results presented in this paper concern the application of a CFD two fluid model (TFM) to a gas–solid fluidized bubbling bed reactor
Summary
Fluidized bubbling reactors are extensively employed in the industry, both for chemical and biochemical processes as well as for power generation, and one of the main reasons is due to their optimal level of heat and mass transfer induced by the bubbling turbulence (Singh et al, 2013) Under this regime, bubbles are responsible for the overall mixing among phases, and it is essential to understand their fluid dynamics to optimize the whole process. In multiphase applications, where the solid phase involves a very high number of particles, the Eulerian–Eulerian two fluid model (TFM) has been proven to be the most convenient investigation approach (Singh et al, 2013) In addition to this method, two possible
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