Experimental and numerical investigations of a fixed-bed distributor for obtaining the outlet fluid velocity profile

In this study, computational fluid dynamics (CFD) was used to simulate a fixed-bed distributor, investigating spherical particles in body-centered cubic (BCC) and hexagonally close-packed (HCP) structures. The bed-to-particle diameter ratio (D/d(p)) varied in the range of 4.158-16.65, while the range for the ratio of bed height to particle diameter (h/d(p)) was 5-13. The simulations were carried out for Reynolds number (Re-p) in the range of 4-589, including laminar and turbulent flow regimes. To conduct validation, the numerical results were compared with our experimental data as well as seven empirical equations, where perfect match was found for both laminar and turbulent flows. Then simulations were conducted to generate the required data for an artificial neural network (ANN) to predict the velocity profile at the distributor outlet in order to save the computational CPU time. The R-2, MAE and RMSE values of the neural network for predicting the fluid outlet velocity were 0.972, 0.0274 and 0.0512, respectively. The function obtained from the neural network is an efficient tool for the optimum design of fixed-bed distributors. This function could be directly used in three-dimensional fixed-bed distributor models.

Automated summary

In this study, CFD was used to simulate a fixed-bed distributor, investigating the structure of spherical particles in both laminar and turbulent flow regimes. The numerical results were validated and a neural network was developed to predict velocity profiles at the distributor outlet, saving computational time.