Data-informed modelling of aerosol resuspension under aerodynamic loads

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Experimental data for test particles and surfaces will be built in a structured and systematic manner, eventually extending to complex surfaces and particles relevant to real suspension problems. A broad range of sizes will be considered up to environmental particles, the effects of deposit surface topology and time-varying flow effects will be explored. Key parameters influencing the resuspension process and their correlation to other variables will be identified from the carefully controlled experiments and the data will be used to cross-validate with a selected set of numerical simulations. Subsequently, the numerical studies can be used to elucidate the complete resuspension process and moreover be extended to scenarios not easily replicable by the experiments. Machine learning techniques will be employed to learn from the data and develop simplified models, based on the experimental and computational data obtained.