Impact dynamics of complex fluid droplets on heterogeneously wetting surfaces
- Academic lead
- Dr Sepideh Khodaparast, School of Mechanical Engineering, s.khodaparast@leeds.ac.uk
- Co-supervisor(s)
- Dr Mark Wilson, School of Mechanical Engineering, M.Wilson@leeds.ac.uk, Dr Arash Rabbani, School of Computing, A.Rabbani@leeds.ac.uk, Dr David Harbottle, School of Chemical and Process Engineering, D.Harbottle@leeds.ac.uk
- Project themes
- Underpinning Methods for Fluid Dynamics
Knowledge of the wetting and impact of liquid droplets on solid substrates is key to reliable and efficient operation in diverse industrial processes, such as spray cooling, application of pesticides and inkjet printing. The impact dynamics and droplet-surface interactions greatly influence the heat and mass transfer, thus controlling the outcome of the process. To date, scientific investigations have been mainly focused on analysing the impact of droplets of (1) pure liquids such as water on smooth or patterned surfaces with homogeneous and heterogeneous wetting properties or (2) complex fluids on smooth surfaces. Natural and engineered surfaces, however, often exhibit non-uniform surface properties that significantly influence droplet-surface interactions in static and dynamic conditions, yielding undesirable outcomes or advantageous functionalities.
This PhD project aims to build a prediction model linking the wetting heterogeneity of the surface with the dynamics of impacting complex fluids, using a combination of experimental, computational and machine learning approaches. Three-dimensional (3D) spatio-temporal numerical simulations will be performed using the Lattice-Boltzmann method and validated against the two-dimensional (2D) experimental data. Time-resolved 3D numerical data will be used to train a generative machine learning algorithm. This model will learn the relationship between (i) the 2D and 3D droplet topologies and (ii) the surface properties and the dynamics of the droplet impact. Finally, the prediction model will be used to construct the 3D topology of droplets of complex fluids from the 2D experimental data and estimate the surface wettability distribution based on impact dynamics.
Benefiting from the ongoing and previous successful developments on surface patterning techniques and experimental/numerical droplet dynamic analysis, this PhD project provides extensive interdisciplinary technical and scientific training. 
