Research Supervisor Connect
Hierarchical structures exhibiting self-affinity are abundant in nature. We seek to characterise how the evolution of such self-affine topographies is governed by microstructural parameters in different materials and determine how hierarchical structures of such surfaces govern friction and the manner in which frictional behaviour changes through surface deformation.
PHD
The micromechanical aspects of deformation in granular materials require further characterisation, as most current models rely on bulk scale descriptors. This research project is aimed at understanding the micromechanics of asperity deformation and its consequences on frictional forces and deformation in granular materials. Objectives of the research will include an analysis of the load and time dependent evolution of fractal surface topologies in different materials and a microstructural interpretation of observed phenomena. Experimental work will form the basis for the construction of computational models to simulate the evolution of surface profile and frictional forces in different materials. The project is likely to involve microscopic techniques, micromechanical testing and mathematical descriptions of surface phenomena.
The Ph.D. candidate is expected to have a strong background in materials characterisation, mechanical testing and data analysis with a demonstrated ability to work independently. Furthermore the candidate should be willing in developing creative cross-disciplinary experimental procedures and be skilled in the formulation of numerical models. The Ph.D. Candidate will engage in this research project under the supervision of Dr. Dorian Hanaor and Dr. Yixiang Gan as a postgraduate member of the Particles and Grains Laboratory (PGL) in the School of Civil Engineering. Scholarship programs are available for suitable candidates. Applicants must hold an MSc degree or equivalent. Applications should include a Cover Letter, an Expression of Interest in this project, Curriculum Vitae, and contact details of referees.
The opportunity ID for this research opportunity is 1602