|Funding for:||UK Students|
|Funding amount:||£18,622 per annum|
|Placed On:||17th July 2023|
|Closes:||15th October 2023|
Hydrogen as an energy carrier has the potential to be the leader of industrial decarbonization. However, efficient application of hydrogen depends on ensuring the safety of storage, carrying and filling of highly pressurised hydrogen, and it is highly affected by the type of materials considered for future hydrogen infrastructure. Polymer composites can play a vital role in high-pressure hydrogen gas applications, offering light weight and high corrosion resistance. However, high-pressure hydrogen gas environments can cause microdamage in polymer-based systems due to gas penetration when the system is depressurised after hydrogen exposure. The precise nature of the phenomenon remains unclear yet and it is considered to be an important factor that can affect structural integrity of high-pressure hydrogen infrastructure made of polymer composites. At present no general acceptance criteria for the level of microdamage have been defined in the industry.
This project aims to understand better the controlling mechanisms behind microscopic damage and develop a predictive model for polymer composites subject to high-pressure hydrogen exposure, by combining advanced experiments with theory. Particularly, a suitable Machine Learning approach will be utilized to analyse experimental data obtained from advanced microscopy such as X-ray computed tomography (CT), to develop a combined mechanistic and data-driven modelling methodology describing the material microdamage process. This will form the basis for an improved theory and predictive models of composite behaviour in high-pressure hydrogen environments.
This study also aims to provide non-academic impact by proposing useful guidelines to our industrial collaborator about understanding critical states of microdamage in composite materials subject to high-pressure gaseous environments. Consequently, this will allow development of less conservative and more sustainable design protocols that will assist development of future light-weight hydrogen infrastructure with polymer composite materials.
Essential: At least a 2:1 degree in Engineering, Computer Science; Physics or Applied Maths
Desirable: strong interest/background in (a) Materials modelling; (b) Machine Learning for applications in Materials Engineering; (c) Computer Programming
Funding and Eligibility
The studentship is open to home students with full awards for 3.5 years. Stipend at the UKRI rate (currently £18,622 per annum) and tuition fees will be paid at the UK rate.
Informal enquiries are encouraged and should be addressed to Dr Lukasz Figiel (firstname.lastname@example.org)
Type / Role: