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Postgraduate research project

An integrated predictive tool for City-scale CB Hazard dispersion and uncertainty quantification

Funding
Competition funded View fees and funding
Type of degree
Doctor of Philosophy
Entry requirements
2:1 honours degree View full entry requirements
Faculty graduate school
Faculty of Engineering and Physical Sciences
Closing date

About the project

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 1km 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 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 2 of the most challenging issues in hazard prediction:

  1. How is uncertainty in hazard prediction affected by changes in the meteorological conditions and simplified building geometries?
  2. How should meteorological data be processed to define the information and data preparation requirements necessary for accurate real-time high-fidelity dispersion simulations?

You’ll join a large aerodynamics group engaged in a wide range of experimental and numerical studies of turbulent flows. The project will benefit from close collaboration with other researchers and colleagues in the collaborative project. You’ll be able to access local and national supercomputers, and work closely with Defence Science and Technology Lab colleagues through placements and regular meetings.

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