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Multi-scale machine learning approaches to composite materials modelling and design


   Department of Mathematics

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  Prof W J Parnell, Dr P Carbone  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

Accurate modelling of the mechanical behaviour of elastomeric composite materials including microscopic or nano-sized filler particles still represents a major challenge in terms of the range of length scales involved. This difficulty arises because the macroscopic continuum-scale properties are strongly influenced by chemical and physical interactions at the atom-scale and mesoscale. The approaches to the modelling of these materials is affected by many factors including the choice of filler particles, the particle size distribution, the percentage filler content and bulk material manufacturing processes.

This PhD project will employ molecular dynamics and micromechanics in order to predict the effective macroscopic behaviour of the filled composite. The aim is to characterise the local mechanical properties that result from interaction between the microscale filler particle surfaces and the surrounding polymer. An example elastomer of interest is silicone, whereas the filler particles may be glass, a thermoplastic, or an inorganic species. These properties will inform a description of the interface regions around filler particles, the properties of which vary according to distance from the filler surfaces and the nature of the surrounding polymer. The localised mechanical parameters and the nature of the interface region will then be used to develop constitutive relations within micro-mechanical models of the composite and homogenization from the mesoscale to the continuum scale, allowing the prediction of the linear elastic (low strain deformation) region of stress / strain curves.

These models have the potential to assist in the design and down-selection of new materials with optimised or novel properties. In order to achieve this optimisation, machine learning techniques will be employed, with experimental data being combined with surrogate learning from the models developed, in order to reach novel areas of the design parameter space.

The project will involve a combination of computational and modelling work. A suitable candidate should have (or be expected to soon obtain) a good degree in a STEM subject, and be willing to engage with existing computational physics / chemistry codes and techniques to tackle the molecular dynamics aspects. The scale-up process and micromechanical modelling will also require a student with strong analytical skills and a solid understanding of the physical and engineering aspects involved.

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder). 

The project will involve a combination of computational and modelling work. A suitable candidate should have (or be expected to soon obtain) a good degree in a STEM subject, and be willing to engage with existing computational physics / chemistry codes and techniques to tackle the molecular dynamics aspects. The scale-up process and micromechanical modelling will also require a student with strong analytical skills and a solid understanding of the physical and engineering aspects involved. 

If you have any questions about the project, please contact Prof P. Carbone (Chem. Eng. & Analytical Science) [Email Address Removed] or Prof. W. Parnell (Mathematics) [Email Address Removed].


Funding Notes

This is a 3.5 year PhD Studentship which will cover fees and stipend of £16,062 in academic year 2022/23, plus industrial enhancement.
Applicants are expected to hold, or be about to obtain, a first-class undergraduate degree (or Masters degree) in a STEM discipline. Computational skills are a significant advantage.
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