The general topic of this project is the development of improved mathematical models for thin film flow modelling. The study of such thin fluid layers is an integral part of many flow problems, ranging from gear lubrication and cleaning simulations, through automotive water management and spray coating. While specialized thin film solvers have been developed to model these thin fluid layers, there exists no method to detect their formation, or to couple surface and bulk flow.
This project developed a new computational framework to model thin film flow. Unlike existing methods, the proposed method is not just able to model not just the evolution of thin fluid films, but it can also predict the formation and break up of thin fluid sheets. A further advantage of the new framework, referred to as the Discrete Droplet Method (DDM), is that it can predict the evolution of thin fluid films on moving surfaces of any shape. Proof-of-concept applications have shown that this method can significantly benefit rain-on-car applications in the automotive industry to track how rainwater moves over a car, and where it collects.
We further developed a model adaptive framework to couple surface and bulk flow. The newly developed DDM for modelling surface flow was coupled with an existing and popular Navier-Stokes solver for bulk fluid flow. Through various examples, we showed that the newly developed model adaptive framework can significantly speed up simulations of coupled bulk surface flow, with one application showing an almost 20x speed-up.
