PhD Studentship, School of Engineering
PhD Studentship, An integrated predictive tool for City-scale CB Hazard dispersion and uncertainty quantification
School of Engineering
Location: Boldrewood Campus
Closing Date: Tuesday 31 August 2021
Supervisory Team: Zhengtong Xie, Steven Herring
We have now run into a fast evolving but more uncertain world. This includes fast developing urban environments where most of the population lives. It is crucial that we are able to predict in time street airflows, concentration of pollutants, chemicals and pathogens, to respond promptly and to be more resilient.
It is well-known that accurately predicting the dispersion of materials within 1 km of the source is challenging and beyond the capability of the models typically used for operational response. Emergency response predictions typically have high uncertainties because details of the source are limited and because that the meteorological conditions are determined from a single nearby observation station or forecast grid point.
This project will exploit recent advances in computational methods and facilities to enable statistical analyses to be conducted which address two of the most challenging issues in hazard prediction:
- How is uncertainty in hazard prediction affected by changes in the meteorological conditions and simplified building geometries?
- How should meteorological data be processed to define the inputs for dispersion simulations?
Answers to these are required so that an assessment can be made as to whether current emergency response tools are ‘fit-for-purpose’, and to define the information and data preparation requirements necessary for accurate real-time high-fidelity dispersion simulations.
You will join a large and flourishing aerodynamics group (http://www.southampton.ac.uk/engineering/research/groups/afm.page) engaged in a wide range of experimental and numerical studies of turbulent flows. The project will benefit from close collaboration with other researchers and with colleagues in the collaborative project. You will be able to access the local and national supercomputers, and work closely with Dstl colleagues through placements and regular meetings.
If you wish to discuss any details of the project informally, please contact Dr Zheng-Tong Xie, AFM Research Group, Email: firstname.lastname@example.org, Tel: +44 (0) 2380 59 4493.
A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).
Closing date: applications should be received no later than 31 August 2021 for standard admissions, but later applications may be considered depending on the funds remaining in place.
Funding: For UK students, Tuition Fees and a stipend of £15,285 tax-free per annum for up to 3.5 years.
How To Apply
Applications should be made online. Select programme type (Research), 2021/22, Faculty of Physical Sciences and Engineering, next page select “PhD Engineering & Environment (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Zhengtong Xie
Applications should include:
Two reference letters
Degree Transcripts to date
For further information please contact: email@example.com