PhD Studentship - An integrated predictive tool for City-scale CB Hazard dispersion and uncertainty quantification
University of Southampton
Southampton, Hampshire, UK
2h ago

1323320DA

Supervisory Team : Zhengtong Xie, Steven Herring

Project description

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 (

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