The geometric analysis and parameterisation of array of obstacles undergoing high Reynolds number flows

Air pollution in urban environments has huge economical and human costs, leading to 40k deaths and £20bn loss in UK annually (RCP and RCPCH, 2016). The problem is being worsen by climate change and population growth. The scientific challenge is that pollutant dispersion is governed by multi-scale turbulent flows in a complex urban environment. It calls for innovative mathematical models to better inform policy makers & improve the well-being of our society. The aim of the project is to employ clusters of fractal structures to model urban landscape and investigate the unique properties of urban flows using novel data-driven methods. The outcome can have have potential impacts on policy making and urban planning.

The aim is to employ clusters of fractal structures to model urban landscape and investigate the unique properties of urban flows using novel data-driven methods.
Objective 1: Understand the effects of fractal geometries on pollutant dispersion
Objective 2: Develop physics-based data-driven methods for efficient prediction of pollutant dispersion

Research methodology:
1. Employ the Lattice Boltzmann Method (LBM) to simulate pollutant dispersion between fractals, elucidate its unique features
2. Use machine learning techniques to identify key physical processes and parametrize pollutant dispersion with the fractal geometry and field parameters.
3. Use optimisation to find geometries that best mitigate pollution effects.
4. Develop LBM-based data assimilation (DA) scheme to predict pollutant dispersion
5. Cross validate the methods with experimental data from researchers in Chile and France.

The project is aligned with, e.g., the living with environmental change theme, and various research areas under the theme, such as infrastructure and urban systems, artificial intelligence technologies, fluid dynamics, statistics and applied probability.