Project Reference: CMEES-FSI-431
Computational methods for predicting complex unsteady flows are computationally demanding, while full-field experimental methods are very expensive. This project will merge the fields of machine learning, experimental methods, and physics-based modelling to greatly increase the speed of computational predictions.
In particular, this thesis will develop models of the forcing and motion experienced by objects turbulent flows by integrating data from high-resolution Particle Image Velocimetry (PIV) and force measurements into Computational Fluid Dynamics (CFD). The data-analysis methodology generated by this work will vastly reduce the computational cost of CFD while maintaining or even increasing simulation accuracy.
If you wish to discuss any details of the project informally, please contact Dr Gabriel Weymouth, Fluid Structure Interaction Research Group, Email: G.D.Weymouth@soton.ac.uk.
To apply, please complete an application for a PhD in Engineering and the Environment using the following link: https://www.southampton.ac.uk/engineering/postgraduate/research_degrees/apply.page? Within the application form, please enter the title of the PhD Studentship you are applying for.
Closing Date: 13 Apr 2018
Post Type: PhD Studentship (Funded)
