|Funding for:||UK Students, EU Students|
|Funding amount:||£14,777 The funding covers UK/EU fees and stipend at the standard EPSRC rate|
|Placed On:||9th November 2018|
|Closes:||4th January 2019|
Computational methods for predicting complex unsteady flows are computationally demanding, while full-field experimental methods are very expensive. This project is at the forefront of the emerging field of machine learning and data-driven computational modelling. The student will develop unique new models to predict fluid mechanic characteristics by integrating experimental data from high-resolution Particle Image Velocimetry (PIV) and force measurements into Computational Fluid Dynamics (CFD) simulations. The resulting learning-based models will vastly reduce the computational cost of running CFD simulations. This reduction in cost will expand the potential applications of CFD into new areas such as generative design, digital twins, and life-cycle forecasting for engineering structures.
This work is in collaboration with partner Universities in the U.S.A. and the student could spend one or more semesters at the University of Rhode Island.
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.
Funding and Eligibility
This 3 year studentship covers UK/EU tuition fees and provides an annual tax-free stipend at the standard EPSRC rate, which is £14,777 for 2018/19.
How to Apply
Click here to apply and select the programme - PhD in Engineering and the Environment. Please enter the title of the PhD Studentship in the application form.
Type / Role: