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| Funder | European Commission |
|---|---|
| Recipient Organization | Queen Mary University of London |
| Country | United Kingdom |
| Start Date | Mar 01, 2022 |
| End Date | Feb 29, 2024 |
| Duration | 730 days |
| Number of Grantees | 1 |
| Roles | Coordinator |
| Data Source | European Commission |
| Grant ID | 101022470 |
A moving contact line (MCL) is a moving line of intersection between a fluid/fluid interface and a solid wall.
MCLs are central to a wide range of flows in nature and industry, ranging from surface coating, spray cooling, displacement of oil by CO2, to the recent development of 3D printing, microreactors, and nanotechnology. However, the modelling of MCLs has been a classical challenge.
In particular, when the solid phase can dissolve into the wetting fluids, e.g., in soldering, formation of alloys and manufacturing of composite materials, there are still a few fundamental challenges which have handicapped the development of predictive computational models.
This fellowship project will address those challenges and it has three objectives: (1) to develop a new computational model for 3D direct numerical simulation (DNS) of MCLs on dissolvable solid surfaces, covering both inertial- and diffusion-dominant dissolutive wetting regimes, and resolving nanometer length scales; (2) to develop a novel experimental method using tapping mode atomic force microscopy to directly measure, for the first time of the world, the dynamic contact angle and interface profiles within tens to hundreds of nanometers near the dynamic dissolutive wetting line, and to formulate a theory relating the dynamic contact angle and interface profiles to system parameters; (3) to formulate a reduced-order macroscale computational model which can save computational effort by at least nine orders of magnitude compared with DNS models in numerical simulation of dissolutive wetting.
The project will lead to a step change in our modelling and understanding of dissolutive wetting.
It will enable accurate and affordable simulations which will greatly benefit design and optimization in a vast range of industrial applications.
Queen Mary University of London
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