Dr Mohammad Saadatfar

Department Department of Applied Mathematics
Qualifications PhD
Office phone (02) 612 56361
Email
Skype mohammad.saadatfar
Office Cockcroft 4 56

Patterns on closed curved surfaces

In this project, we will study the formation of regular patterns, as well as defects, on closed curved surfaces such as boundaries of granular packings.

Dr Mohammad Saadatfar, Dr Nicolas Francois, Professor Stephen Hyde, Prof Timothy Senden

Force networks in granular materials: imaging, pattern recognition and data mining

This project employs an integrated experimental and analytical approach to interrogate granular materials (e.g., soil, sand and sedimentary rocks, powder, colloidal systems, coal, snow etc.).  The experimental part, undertaken at ANU, involves the accurate experimental measurement and 3D visualisation of contact forces at the contacts between particles. The analytical part, undertaken at UoM, focuses on “mining” hidden patterns in the experimental data, using new tools from mathematics and statistics of complex systems.

Dr Mohammad Saadatfar

4D structural characterization of carbon-sequestering cements

This project will use high resolution 3D X-ray computed tomography to characterise the evolving structure of reactive magnesium cement materials over months-long time frames, in order to learn how to optimise cement composition and initial structure to enhance CO2 uptake and cement strength, while at the same time minimizing clogging.

Dr Anna Herring, Dr Mohammad Saadatfar, Prof Adrian Sheppard

Granular materials: understanding their geometry and physics

What is a granular material from geometry and physics perspective? We'll try to understand the fundementals of granular materials in this project.

Dr Mohammad Saadatfar, Dr Nicolas Francois, Dr Vanessa Robins, Prof Timothy Senden

Why does the English willow make the best cricket bat?

In this project, we will investigate the microstructure of wood using 3D microscopes and a host of interesting analytical tools.

Dr Mohammad Saadatfar, Prof Phil Evans

3D image segmentation using machine learning techniques

We aim to use  machine learning techniques to identify minerals and components of three dimensional images obtained from X-ray micro Computed Tomography (XCT).

Dr Mohammad Saadatfar

Updated:  15 January 2019/ Responsible Officer:  Director, RSPE/ Page Contact:  Physics Webmaster