PhD Studentship – Efficient Multi-scale Methods for Test Reduction in Aerospace Composites

University of Bath

Supervisor Name: Andrew Rhead, Richard Butler, Robert Scheichl (Department of Mathematical Sciences)

Research Centre: Materials and Structures (MAST)

Overview of the research
Major penalties arise from the lack of predictive modelling and the uncertainty associated with current design, test and manufacturing methods in composites, impacting on production cost and environmental footprint. This leads to over-conservativism in design, resulting in unnecessarily high operational costs and greenhouse-gas emissions; it also necessitates over-stringent manufacturing quality requirements associated with ’defect-free’ part production policies.

This project will use an efficient and scalable Finite Element software to reduce reliance on expensive physical testing on all scales by combining mathematically rigorous multi-scale models with experimental data using state-of-the-art stochastic methods. Very rare events such as coincidental impact damage and manufacturing defects will be simulated and uncertainty quantification techniques will be used to predict probabilities of occurrence. Physically-based failure criteria will be applied within the finite element method to predict initiation of damage growth.

We are looking for a mathematically-able student with good skills in applied mechanics and an interest in the modelling and testing of composites, to develop computational models and analytical methods. Our partners in the project, GKN Aerospace (UK) and Fokker (Holland), will provide industrial context and realistic problems for analysis and testing, and you will be expected to liaise with practicing engineers and industrial researchers.  To work on this project you will need to have, or be able to develop, good programming and experimental skills; you can expect to be working in a multidisciplinary team within a fast-developing field in a high-technology industrial sector, where new ideas are valued and implemented.

Funding
A Home/EU award will cover tuition fees, a training support fee of £2,000/annum, and a tax-free maintenance payment of £17,000 per year (2017-8 rate) over 3.5 years.

Funding is provided by GKN Aerospace (UK)

Preferred start date: 2nd October 2017

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source: jobs.ac.uk

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