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Rapid Multi-scale Modelling of Polymer Composite Strength

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  • Full or part time
    Dr J Foreman
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

The strength of a polymer composite system is difficult to predict with accuracy due to the huge number of variables associated with a multiphase system. These include complexity associated with linear elastic reinforcements embedded in a thermosetting polymer matrix which displays strain rate dependent viscoelastic properties. Recently, we have developed a multi-scale model which predicts the strength of a polymer composite as a function of temperature and strain rate based on Group Interaction Modelling of polymer properties. The material properties are used to populate a FE/statistical model which predicts composite strength via the transfer of strain between adjacent fibres. This project will involve developing the multi-scale model and comparing it to a range of experimental validation measurements. The overall aim of the project will be to use the model to develop stronger composites by fine-tuning the properties of the constituent materials.

Funding Notes

Candidates should be self-funded, sponsored, or applying for Scholarships. The University and the Faculty of Engineering award PhD scholarships for Home, EU, and International students on a competitive basis every year. The deadline for Faculty/University Scholarships is February each year - for more information see: www.shef.ac.uk/postgraduate/research/scholarships
Candidates should have or expect to gain a good Honours degree with 2i or above in Materials Science and Engineering or related discipline.

If English is not your first language then you must have International English Language Testing Service (IELTS) average of 6.5 or above with at least 6.0 in each component.

Related Subjects

How good is research at University of Sheffield in Electrical and Electronic Engineering, Metallurgy and Materials?
Materials Science and Engineering

FTE Category A staff submitted: 34.80

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

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