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Turbulent mixing in urban environments – towards trusted simulations under strongly stable and unstable conditions

Summary

Using high resolution numerical simulations this project will advance our understanding of the effects of buoyant convection on turbulent mixing in urban environments (i.e. within city streets) and will develop new modelling approaches for large eddy simulations of these flows.

Supervisor

Associate Professor Nicholas Williamson.

Research location

Aerospace, Mechanical and Mechatronic Engineering

Synopsis

The modelling and simulation of urban environments is undergoing an extraordinary revolution. The increase in computational power currently available means that computational fluid dynamics simulations of an urban city block can be performed at ∼ 5m resolution in near real time using Large Eddy Simulation (LES). This advancement has the capacity to revolutionise local weather prediction, decision making in urban planning and responses to natural and anthropogenic disasters, as well as address critical challenges such as urbanisation, urban heat islands and climate change. There are key deficiencies in current urban flow models that must be addressed however.

This project focuses on understanding the effect of buoyancy on street level mixing process. These process include convective boundary layers formed on streets and buildings and thermal plumes that discharge into the urban roughness layer. These processes are poorly represented in course resolution models. This project will use high resolution direct numerical simulation and high resolution large eddy simulations to understand the flow physics and develop new modelling strategies for them. 

Additional information

Successful candidates must:

  • Have a strong interest in fundamental fluid mechanics and numerical methods
  • Have a background in Engineering or Science/Mathematics is preferred

How to Apply:

To apply, please email nicholas.williamson@sydney.edu.au the following:

  • CV
  • Transcripts

Want to find out more?

Opportunity ID

The opportunity ID for this research opportunity is 3427

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