Everybody has experienced in daily life that complex fluids such as ketchup, mud or hair gel, behave differently and more erratically than simple fluids like water. The reason is in properties such as yield stress and elasticity, which makes the flow more complex to predict and understand. Yield-stress fluids are very common in nature (mud flows, volcanic events, biology) and industries (food industry, pharmaceutics, process industry, chemical industry, building industry). Processes designed for Newtonian fluids do not work for complex fluids, and there is an urgent need for improved theories and models.
The MUCUS project aimed to bring forward the state-of-the-art understanding of complex fluid flow over surfaces, by computer simulations which only became possible due to recent development of high-performance computing algorithms. Very little was known so far about time-varying yield-stress fluid flow over surface hills, grooves and how complex fluid droplets wet or are repelled by different surfaces.
The objectives of MUCUS project were to:
i) reveal new insight of the time-varying and unsteady (inertial) transport, mixing of different fluids and particles/droplets, spreading and impact of complex fluids on surfaces,
ii) create the first database of yield-stress fluid simulations, experiments and their cross-validation, and
iii) develop novel analysis tools, and couplings between micro- and macrostructure, to enable controlled design of complex fluids processes in the future
The conclusions of the project were:
i) Complex fluid droplets impact, and spread, or bounce from surfaces in a qualitatively different way from Newtonian droplets such as water. The type of behaviour is governed not only by surface wettability (hydrophobic/hydrophilic), but the microscale surface structure and complex fluids interact to create new phenomena. This has to be considered in design of surfaces in contact with complex fluids, such as, self-cleaning surfaces, or microfluidics.
ii) Particle and droplet suspensions in a yield-stress carrier fluid were studied for the first time, and their dynamics and distribution (near the channel centre or near the corners) were sensitive functions of the carrier fluid rheology.
Behaviours not present in Newtonian fluids were identified.
iii) Computational methods were developed that enabled us to simulate real three-dimensional flows of yield stress fluids and surface interactions of 3D viscoelastic droplets for the first time. We pioneered machine learning tools for analysis and prediction of yield-stress fluid flows.
