Mixing and segregation assessment of bi-disperse solid particles in a double paddle mixer

A double paddle blender's flow patterns and mixing mechanisms were analyzed using discrete element method(DEM)and experiments.The mixing performance of this type of the blender containing bi-disperse particles has been rarely studied in the literature.Plackett-Burman design of experiments(DoE)methodology was used to calibrate the DEM input parameters.Subsequently,the impact of the particle number ratio,vessel fill level,and paddle rotational speed on mixing performance was investigated using the calibrated DEM model.The mixing performance was assessed using relative standard deviation and segregation intensity.Mixing performance was significantly affected by the paddle rotational speed and particle number ratio.Moreover,the Peclet number and diffusivity coefficient were used to evaluate the mixing mechanism in the blender.Results revealed that the diffusion was the predominant mixing mechanism,and the best mixing performance was observed when the diffusivity coefficients of 3 mm and 5 mm particles were almost equal.

CFD-DEM simulation of turbulence modulation in horizontal pneumatic conveying

A study is presented to evaluate the capabilities of the standard k–ε turbulence model and the k–ε turbulence model with added source terms in predicting the experimentally measured turbulence modulation due to the presence of particles in horizontal pneumatic conveying, in the context of a CFD–DEM Eulerian–Lagrangian simulation. Experiments were performed using a 6.5-m long, 0.075-m diameter horizontal pipe in conjunction with a laser Doppler anemometry （LDA） system. Spherical glass beads with two sizes, 1.5 and 2 mm, were used. Simulations were performed using the commercial discrete element method software EDEM, coupled with the computational fluid dynamics package FLUENT. Hybrid source terms were added to the conventional k–ε turbulence model to take into account the influence of the dispersed phase on the carrier phase turbulence intensity. The simulation results showed that the turbulence modulation depends strongly on the model parameter Cε3. Both the standard k–ε turbulence model and the k–ε turbulence model with the hybrid source terms could predict the gas phase turbulence intensity trend only generally. A noticeable discrepancy in all cases between simulation and experimental results was observed, particularly for the regions close to the pipe wall. It was also observed that in some cases the addition of the source terms to the k–ε turbulence model did not improve the simulation results when compared with those of the standard k–ε turbulence model. Nonetheless, in the lower part of the pipe where particle loading was greater due to gravitational effects, the model with added source terms performed somewhat better.

Analysis of cohesive particles mixing behavior in a twin-paddle blender:DEM and machine learning applications

This research paper presents a comprehensive discrete element method(DEM)examination of the mixing behaviors exhibited by cohesive particles within a twin-paddle blender.A comparative analysis between the simulation and experimental results revealed a relative error of 3.47%,demonstrating a strong agreement between the results from the experimental tests and the DEM simulation.The main focus centers on systematically exploring how operational parameters,such as impeller rotational speed,blender's fill level,and particle mass ratio,influence the process.The investigation also illustrates the significant influence of the mixing time on the mixing quality.To gain a deeper understanding of the DEM simulation findings,an analytical tool called multivariate polynomial regression in machine learning is employed.This method uncovers significant connections between the DEM results and the operational parameters,providing a more comprehensive insight into their interrelationships.The multivariate polynomial regression model exhibited robust predictive performance,with a mean absolute percentage error of less than 3%for both the training and validation sets,indicating a slight deviation from actual values.The model's precision was confirmed by low mean absolute error values of 0.0144(80%of the dataset in the training set)and 0.0183(20%of the dataset in the validation set).The study offers valuable insights into granular mixing behaviors,with implications for enhancing the efficiency and predictability of the mixing processes in various industrial applications.