About the project
Composite materials are at the forefront of airframe technologies successfully providing reliable and high performance aerospace structures. A key barrier to greater deployment of next generation composites for new and novel vehicle concepts is the time and cost associated with certification of new designs.
The current approach, driven by regulatory requirements, necessarily relies on physical test supported by simulation, is slow and expensive. This approach is heavily biased towards physical testing because it is currently the only reliable way to assess how inherent variabilities influence the relationship between the materials, manufacturing process and structural performance of the airframe design.
This project aims to develop and validate an emerging solution using a new statistical framework to design, model and test representative structural elements/components in order to safely account for known and unknown inherent uncertainties. The novel approach deploys multi-scale FE modelling emulators and deep learning techniques in conjunction with full-field data rich test regimes. The resulting data fusion and Bayesian analysis is able to predict probability of achieving strengths above design limit loads. This approach promises to develop virtual test techniques that can predict the real-life distribution of failure strengths and thus assign a stochastic risk factor to the simulated strength.
This project exploits expertise at University of Southampton and Bristol and will employ the use of the world leading experimental test facilities in the National Infrastructure Laboratory on the Boldrewood Campus (Southampton). Experimental full field Digital Image Correlation and Thermographic imaging will be used to establish deviations between physical and simulated tests of a typical structural assembly.
You will be primarily based and supervised in the School of Engineering at the University of Southampton and co-supervised at the University of Bristol where they will have visiting student status for access to supplementary facilities and expertise.
Mandatory training will be provided in Research Ethics, Research integrity and Research Data Management, in addition there are opportunities to audit taught module to enhance knowledge and understanding and also detailed indiction and training on the use of experimental facilities.
