Inferring causality from atmospheric turbulence and dispersion experimental data

In spite of the great research efforts addressing environmental sustainability, several challenges and gaps exist in understanding physical mechanisms driving air quality [1]. Challenges include the modelling of atmospheric processes underlying such physical mechanisms, as well as the role of anthropogenic sources of pollution and, from a holistic viewpoint, the impact of all these aspects on the environment and living organisms.  

​This project will provide insights into the dispersion of substances (e.g., pollutants) in the atmosphere through the lens of causal inference [2], seeking to identify and quantify statistically-significant cause-effect patterns from measurement data. Wind tunnel experiments will be performed under various geometrical and flow conditions, and measurements will be exploited for the analysis as well as in comparison with numerical simulations. Results originating from this project will enable the development of more reliable and interpretable data-driven models of dispersion processes, specifically for urban dispersion in atmospheric flows. 

​References: 

[1] Hertwig, D., Soulhac, L., Fuka, V., Auerswald, T., Carpentieri, M., Hayden, P., et al. (2018). Evaluation of fast atmospheric dispersion models in a regular street network. Environmental Fluid Mechanics, 18, 1007-1044. 

[2] Pearl, J., & Mackenzie, D. (2018). The book of why: the new science of cause and effect. Basic Books editor​.

Open to both UK and international candidates.

Up to 30% of our UKRI-funded studentships can be awarded to candidates paying international rate fees. Find out more about eligibility.

You will need to meet the minimum entry requirements for our Aerodynamic and Environmental Flow PhD programme.

​We are seeking a highly motivated candidate with degrees in a relevant engineering field (e.g., aeronautics, environmental, civil, mechanical), as well as physical sciences or applied mathematics. Candidates should have excellent communication skills and a strong inclination towards multidisciplinary research. Students holding a first-class degree (or a good 2:1) in the aforementioned disciplines and a background in fluid mechanics are encouraged to apply. Previous experience with Matlab/Python and with experimental work would be beneficial.