Discrete Element Modeling of Martian Regolith Simulants Accounting for Realistic Particle Shapes and Particle Size Distributions

Author(s):  
Z. Lai ◽  
Q. Chen
Author(s):  
Meire Pereira de Souza Braun ◽  
Alice Jordam Caserta ◽  
Helio Aparecido Navarro

The focus of this paper is to study the behavior of systems with continuous particle size distributions over a gas-solid flow in a bubbling fluidized bed. A lognormal distribution with particle-size range between 800 micrometers and 900 micrometers was used to perform numerical simulations to investigate gas bubbles formation for a polydispersed system. Different drag models were used to predict the bubbles. Species segregation for a binary mixture and a monodispersed system were also studied. Discrete Element Method (DEM) simulations were performed using the source code MFIX (“Multiphase Flow with Interphase eXchanges”) [1] developed at NETL (“National Energy Technology Laboratory”). The bubble size of a single injected bubble depended strongly on gas-particle drag model used. The influence of the gas bubbles in the mixture and segregation was analyzed and discussed. The results were compared with experimental results from the literature and a good agreement were obtained.


2021 ◽  
Author(s):  
javad Manashti ◽  
Francois Duhaime ◽  
Matthew Toews ◽  
Pouyan Pirnia

The two objectives of this paper were to demonstrate use the of the discrete element method for generating synthetic images of spherical particle configurations, and to compare the performance of 9 classic feature extraction methods for predicting the particle size distributions (PSD) from these images. The discrete element code YADE was used to generate synthetic images of granular materials to build the dataset. Nine feature extraction methods were compared: Haralick features, Histograms of Oriented Gradients, Entropy, Local Binary Patterns, Local Configuration Pattern, Complete Local Binary Patterns, the Fast Fourier transform, Gabor filters, and Discrete Haar Wavelets. The feature extraction methods were used to generate the inputs of neural networks to predict the PSD. The results show that feature extraction methods can predict the percentage passing with a root-mean-square error (RMSE) on the percentage passing as low as 1.7%. CLBP showed the best result for all particle sizes with a RMSE of 3.8 %. Better RMSE were obtained for the finest sieve (2.1%) compared to coarsest sieve (5.2%).


2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Zhaoyang Xu ◽  
Ning Xu ◽  
Haibo Wang

The minimum void ratio is an important parameter for evaluating soil properties. It is closely related to the compressive properties, permeability, and shear strength of soil, and it is affected by particle size distributions and particle shapes. However, existing research generally focuses on modeling the minimum void ratio with the effect of particle size distributions, ignoring the influences of particle shapes on the minimum void ratio. This paper analyzes the influences of particle size distributions and particle shapes on the minimum void ratio using four types of sand and alternative materials. The experiments showed that the minimum void ratio first decreased and then increased with the increase of the fines content. The minimum void ratio reached a minimum value when the proportion of fines content was approximately 40%. The more irregular the particle shapes, the more complicated the contact between particles, the more the void existed between the particles, and the larger the minimum void ratio. Based on the experimental data, a relational model between the minimum value of the minimum void ratio and the particle sizes ratio was derived with binary mixtures of different particle sizes and shapes. This proposed model required only one parameter T, which was closely related to the sphericity of the particles, to predict the minimum value of the minimum void ratio with various fines contents. The experiment results showed that the predicted value was very close to the actual measured value.


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