PhD Research Studentships
Computational design of self-healing functional materials for renewable energy and sustainability
| Supervisor: | Chinnapat PANWISAWAS |
| Apply by: | 29 January 2025 |
| Start in: | September (Semester 1) |
Description
Background:
Artificial or synthetically ‘self-healing’ materials which are capable of autonomous/spontaneous or stimulated repair of their damage under external stimuli, such as heat, light, solvents, find several engineering applications. The state-of-the-art autonomous healing in polymers and composites for repair and healing of delamination with integrated sensing capabilities, and metals for fatigue cracks via advanced high-precision metallurgical processes such as fusion welding and additive manufacturing has proven possible and paved its way to accelerate innovative materials technology up-take. Manufacturing process of the self-healing materials is challenging to control and retain the self-healing properties of the smart materials.
The aim of this project is to develop computational fluid and/or solid mechanics and data-driven modelling framework to enable self-healing engineering application. Multi-physics multi-scale modelling and data-driven framework for rationalising physical effect of self-healing mechanisms will be developed and used to search for new materials with self-healing functionality.
PhD candidate specification:
The successful candidate will develop integrate computational materials engineering tools to understand the physical mechanism of self-healing materials and perform materials design materials for specific conditions to obtain microstructure-informed property and designable performance. Useful skills include, but not limited to:
- Computational fluid dynamics using volume-of-fluid (VOF) method;
- Phase-field and/or cellular automata finite element calculation;
- Crystal plasticity finite element calculation;
- Reduced-order or data-driven modelling; and
- Proficiency in programming languages, e.g. Python, C/C++, and/or MATLAB.
Utilising both computational and experimental data, the PhD student will be trained to have skills in computational mechanics, data analytics and materials design.
The successful PhD candidate will have full access to the SEMS’ advanced microscopy centre as well as mechanical testing facilities. The developed computational AM model will be validated experimentally and then used to predict processing, structure, and property relationship to discover new AM composition for specific applications.
The candidate should have relevant experience in the following subject areas: computational fluid/solid mechanics, materials process simulations, microstructure modelling, data-driven modelling, data analytics. This project will collaborate closely with world-leading academic institutions as well as UK and international industrial partners.
Research group:
Dr Chinnapat Panwisawas's research is concentrated, over the last 15 years, on advanced process science and engineering of investment casting laser fusion welding and powder-bed fusion additive manufacturing particularly for establishing a multi-scale multi-physics approach to understand the process-structure-property-performance relationship of new materials for engineering applications. The research group is committed to facilitating an inclusive and collaborative research environment focused on the personal development of PhD students. Should you want to discuss potential applications informally, please contact me directly (see contact details below).
Funding
Funded by: China Scholarship CouncilCandidate will need to secure a CSC scholarship.
Under the scheme, Queen Mary will provide scholarships to cover all tuition fees, whilst the CSC will provide living expenses and one return flight ticket to successful applicants.
Eligibility
- The minimum requirement for this studentship opportunity is a good honours degree (minimum 2(i) honours or equivalent) or MSc/MRes in a relevant discipline.
- If English is not your first language, you will require a valid English certificate equivalent to IELTS 6.5+ overall with a minimum score of minimum score of 6.0 in each of Writing, Listening, Reading and Speaking).
- Candidates are expected to start in September (Semester 1).
Contact
For informal enquiries about this opportunity, please contact Chinnapat PANWISAWAS.
Apply
Start an application for this studentship and for entry onto the PhD Materials Science full-time programme (Semester 1 / September start):
Please be sure to quote the reference "SEMS-PHD-614" to associate your application with this studentship opportunity.
| Related website: | https://www.sems.qmul.ac.uk/staff/c.panwisawas | |
| SEMS Research Centre: | ||
| Keywords: | Artificial Intelligence, Data Science, Machine Learning, Fluid Mechanics, Manufacturing, Mechanics, Materials Science - Other, Metallurgy, Data Analysis, Mathematical Modelling |