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Mohammad Saadatfar

Researcher at Australian National University

Publications -  101
Citations -  3178

Mohammad Saadatfar is an academic researcher from Australian National University. The author has contributed to research in topics: Atomic packing factor & Deformation (meteorology). The author has an hindex of 29, co-authored 96 publications receiving 2591 citations. Previous affiliations of Mohammad Saadatfar include Trinity College, Dublin & University of Sydney.

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Geometrical structure of disordered sphere packings.

TL;DR: This study is the largest and the most accurate empirical analysis of disordered packings at the grain-scale to date, mapping over 380,000 sphere coordinates with precision within 0.1% of the sphere diameters.
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Porosity and permeability characterization of coal: A micro-computed tomography study

TL;DR: In this article, a unique contrast agent technique using X-ray micro-computed tomography (micro-CT) was developed for studying micrometer-sized features in coal, which allows for the visualization of coal fractures not visible with conventional imaging methods.
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Onset of mechanical stability in random packings of frictional spheres.

TL;DR: It is found that the volume fraction phiRLP of the loosest mechanically stable packing is in an operational sense well defined by a limit process, and this random loose packing volume fraction decreases with decreasing pressure p and increasing interparticle friction coefficient mu.
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Investigating the geometrical structure of disordered sphere packings

TL;DR: In this article, the authors investigate the geometrical structure of disordered packings, looking for signatures of disorder, and discuss ways to characterize and classify these systems and the implications that local geometry can have on densification dynamics.
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Techniques in helical scanning, dynamic imaging and image segmentation for improved quantitative analysis with X-ray micro-CT

TL;DR: The technical hurdles that needed to be overcome to allow imaging with cone angles in excess of 60° are discussed and dynamic tomography algorithms that enable the changes between one moment and the next to be reconstructed from a sparse set of projections are presented, allowing higher speed imaging of time-varying samples.