PhD Studentship: Accelerated Urban Air Quality Modelling for the Strategic Deployment of Pollutant-Mitigating Catalytic Surfaces

Urban air quality is influenced by multiple factors including traffic emissions, human behaviour, meteorology, and the built environment. This project aims to develop high-performance, GPU accelerated numerical models capable of simulating air high resolution air pollution dynamics across London. The project will incorporate a detailed emissions model tightly coupled to a high-performance fluid dynamics solver enabling fully integrated simulations of airflow, dispersion, and reactive transport of pollutants such as NO₂, O₃, CO₂, and volatile organic compounds (VOCs). The accelerated solver will build on an existing Lattice Boltzmann solver, and could involve developing surrogate models (e.g., Physics-Informed Neural Networks) for coupled physics such as reactive transport.

The resulting coupled solver will be used to support urban planning decisions that affect air quality, from microscale street canyon flows to mesoscale atmospheric dispersion. This project will focus on the “Environmental Catalytic City" concept, investigating how the deployment of photocatalytic coatings on urban infrastructure can reduce the concentration of harmful ambient pollutants via chemical degradation under natural sunlight. This project will look to use the solver to identify potential deployment sites and strategies that reduce their exposure to moisture, maximise pollutant contact time, and increase real-world efficiency of catalytic surfaces.

The ideal candidate will have a strong interest in the built environment, computational modelling, physics, and/or engineering, with proficiency in programming languages such as C++ and Python. Prior experience with GPU programming (e.g., CUDA, OpenCL, or SYCL) or Machine Learning is highly desirable but not essential

This studentship is fully funded by the £9 million Research England-funded M34Impact expansion programme. This project represents a cornerstone of the Computational Science and Engineering Group’s (CSEG) research goals, allowing the successful applicant to join a team with extensive, world-leading expertise in numerical modelling, computational fluid dynamics (CFD), and GIS.

You will be fully embedded within the M34Impact doctoral cohort and play a key role in our dynamic, growing research group. Driven by this Expanding Excellence in England (E3) grant, you will benefit directly from specialized training, collaborative initiatives, and the cutting-edge resources dedicated to advancing our core research.

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