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Bio-inspired joints for metal-composite structures

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  • Full or part time
    Prof M Kashtalyan
    Prof I Guz
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Combining dissimilar materials such metals and fibre-reinforced composites in structural components has potential to reduce weight and improve structural performance in many applications. However, reliable joining of dissimilar materials still remains a challenge. Studying the design principles of joints found in nature can provide a valuable insight into their mechanical behaviour and support future development of new, more efficient joint designs. The aim of the project is to develop new bio-inspired joining concepts for metal-composites structures with the view to optimise joint performance for energy applications. The project will involve theoretical modelling using a combination of analytical and computational means.

The successful candidate should have (or expect to achieve) a minimum of a UK Honours degree at 2.1 or above (or equivalent) in applied mathematics, solid mechanics, or mechanical engineering.

Good knowledge of composite materials and solid mechanics, and strong modelling skills using Matlab and ABAQUS would be a significant advantage.

APPLICATION PROCEDURE:

Formal applications can be completed online: http://www.abdn.ac.uk/postgraduate/apply. You should apply for Degree of Doctor of Philosophy in Engineering, to ensure that your application is passed to the correct person for processing.

NOTE CLEARLY THE NAME OF THE SUPERVISOR AND EXACT PROJECT TITLE YOU WISH TO BE CONSIDERED FOR ON THE APPLICATION FORM.

Informal inquiries can be made to Dr M Kashtalyan ([Email Address Removed]) with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Postgraduate Research School ([Email Address Removed]).

How good is research at Aberdeen University in General Engineering?

FTE Category A staff submitted: 38.60

Research output data provided by the Research Excellence Framework (REF)

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