Effect of particle shape in grinding mills using a GPU based DEM code

Govender, N.; Rajamani, Raj K.; Wilke, Daniel N.; Wu, Chuan‐Yu; Khinast, Johannes G.; Glasser, Benjamin J.

doi:10.1016/j.mineng.2018.09.019

Cited by 43 publications

(16 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We are furnishing here three mill simulations, which were done with the Millsoft [9] and Blaze-DEM [13][14][15] codes. The first one is the Hudbay Mines Constancia SAG mill of size 36 × 26.5 ft.…”

Section: Millsoft and Blaze-dem Mill Simulation Resultsmentioning

confidence: 99%

The Evolution of Grinding Mill Power Models

Rajamani

Kumar²,

Govender

2019

Mining, Metallurgy & Exploration

Self Cite

View full text Add to dashboard Cite

Your article is protected by copyright and all rights are held exclusively by Society for Mining, Metallurgy & Exploration Inc.. This eoffprint is for personal use only and shall not be self-archived in electronic repositories. If you wish to self-archive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com".

show abstract

Section: Millsoft and Blaze-dem Mill Simulation Resultsmentioning

confidence: 99%

The Evolution of Grinding Mill Power Models

Rajamani

Kumar²,

Govender

2019

Mining, Metallurgy & Exploration

Self Cite

View full text Add to dashboard Cite

show abstract

“…Computational tools to find an intersected region have been developed before for pairs of convex polygons [22,23] and polyhedrons [22,24,25]. Yet to the best of our knowledge this is the first attempt to use these tools to study overlap in 3D-structured neurons.…”

Section: Discussionmentioning

confidence: 99%

Geometrical factors determining dendritic domain intersection between neurons: a modeling study

Gatica

Montero

Farassat

et al. 2022

Preprint

View full text Add to dashboard Cite

Overlap between dendritic trees of neighboring neurons is a defining feature of central nervous systems. Dendritic domains sharing a given locus are arguably more likely to share afferent inputs, and proximity is a requirement for dendro-dendritic communication. Dendritic overlap also differentiates neuronal circuits characterized by redundancy and integration from those characterized by a more specific and selective connectivity. We examined the geometrical factors that affect intersection between pairs of neurons by, specifically, modelling the influence of cell-to-cell distance, and dendritic domain size and shape. We developed Matlab scripts to determine intersected volume between pairs of three-dimensionally reconstructed neurons using their convex hull (CH) polyhedrons as proxies for dendritic domain. The influence of neuron-to-neuron distance on intersection was not straightforward and reflected the diverse and anisotropic geometry of neurons, and the ostensible displacement of cell bodies from the CH centroid. The influence of size on pair intersection was tested by giving each neuronal CH pair the same (average) volume without modifying their shape. Conversely, the influence of neuronal shape on pair intersection was proved by giving each neuronal pair an average shape without modifying volumes. While both normalization increased intersection, shape averaging was remarkably more effective than size averaging in increasing pair intersection. We conclude that intersection does not only depend on neuron-to-neuron proximity, but critically on neuron-to-neuron differences in size and shape. The results predict that circuits characterized by selective connectivity exhibit greater heterogeneity in size, but especially shape, of their constituent neuronal elements.

show abstract

“…This was followed by the introduction of irregular shaped particles such as scaled hexagonal prisms, truncated tetrahedra, cubes, and other shapes in GPU-based DEM computing. Govender et al (2018a) presented the grinding mill simulation of regular polyhedral particles such as cube, tetrahedra, hexagonal prism, Biluna, and mixed polyhedral particles. This study investigated the role of particle angularity and aspect ratio on power draw.…”

Section: Dem Simulation Of Grinding Millsmentioning

confidence: 99%

“…Computing the volume of a convex hull, with a computationally efficient algorithm is not a trivial task. The details of volume computing are given in Govender et al (2018a;2018b;2018c). First, the bounding surfaces and their respective normal are determined.…”

Section: Contact Mechanicsmentioning

confidence: 99%

Verification of Polyhedral DEM with Laboratory Grinding Mill Experiments

Puga

Govender

Rajamani

2022

KONA

Self Cite

View full text Add to dashboard Cite

The simulation of grinding mills with the discrete element method (DEM) has been advancing. First, it emerged as a method for studying charge motion with spherical balls and predicting the power draw of the mill. Subsequently, studies on liner wear, charge motion with ellipsoidal and polyhedral shaped particles simulated with three dimensional DEM followed. Further, the impact energy spectra computed in the DEM algorithm is leading to the development of models for the breakage of brittle particles in mills. The core elements in such simulations are the shape of particles in the mill charge and the power draw of the mill due to operating variables. To advance the field, we present a set of experimental data and the corresponding DEM validation results for a 90 × 13 cm mill. The DEM algorithm uses the volume-overlap method which is more realistic for multifaceted irregular particle collisions. Further, we use the scanned shape of the rock media and multifaceted spherical shape for the grinding media to represent as close as possible the actual charge in the mill. First, we present DEM validation for spherical grinding media-only experiments, rock-only experiments, and a mixture of spherical grinding media and rocks, as well as aluminum cubes only to represent the theme of particle shape. Finally, a discussion of the contact mechanics parameters in the four modes of experiments is given. Since the feed ore to plant scale mills can vary in shape, mill simulations with scanned shape of typical particles are the future for more accurate results.

show abstract

Effect of particle shape in grinding mills using a GPU based DEM code

Cited by 43 publications

References 34 publications

The Evolution of Grinding Mill Power Models

The Evolution of Grinding Mill Power Models

Geometrical factors determining dendritic domain intersection between neurons: a modeling study

Verification of Polyhedral DEM with Laboratory Grinding Mill Experiments

Contact Info

Product

Resources

About