Robert Gordon University, through the School of Engineering is offering a PhD studentship, within the Advanced Materials Research Group.
FRP has gained momentum as the best alternative to steel pipelines used in hydrogen transport, FRP pipelines offer a durable, corrosion free and light weight option for hydrogen transport. However, these FRP pipelines are subject to cracks, fibre breakage and delamination.
The objective of the study is to develop a model for predicting defects in FRP composite pipes for hydrogen transport using a quantitative approach and will be delivered within the Advanced Materials Research Group.
Experimental and simulation methods will be utilised for this project; data gathered from the experiments and numerical simulations will be used to build a dataset and Machine Learning will be implemented to investigate and predict possible occurrences of failure under different conditions.
Desirable skills and knowledge: Knowledge or experience of composites, numerical simulations and Machine Learning is advantageous.
Applications should be emailed to Dr Judith Abolle at [Email Address Removed] The applications should consist of a covering letter or personal statement of interest, academic transcripts and a CV.
It is expected that candidates are available to register and commence study on 01 Feb 2023. In addition, the successful candidate will be expected to submit publications to refereed journals and to present their findings at international conferences.
Questions should initially be addressed to:
Dr Judith Abolle-Okoyeagu
School of Engineering, Robert Gordon University, Sir Ian Wood Building, Garthdee Campus, Aberdeen, AB10 7GJ United Kingdom
T: +44 (0)1224 26 2868
E: [Email Address Removed]
Applicants should have a First- or Second-Class UK honours degree, or equivalent CGPA from non-UK universities in a relevant discipline such Mechanical Engineering, Electrical Engineering, Polymer Technology, or Materials Science and Engineering. An MSc in a relevant subject is highly desirable. Knowledge or experience of machine learning, polymer composites and modelling & simulations is advantageous.
Keywords: FEA, Composite Materials, Pipe or Pipelines, Solid Mechanics, Machine Learning