Simulation strategies for improved contamination modeling of liquid dynamics on automotive surfaces

Abstract: A significant level of research is currently being carried out in the development of driver support systems as they are expected to play a key role in minimizing road vehicle accidents and creating a safe driving environment under harsh weather conditions. However, the performance of some components used by existing driver support systems like LIDAR and visual cameras are affected by extreme weather conditions such as heavy rain fall and snow. Therefore, it is paramount to identify key locations in an automotive vehicle where such systems are least affect by external weather conditions, thereby, improving their overall performance. The field of research that deals with such questions from a simulation perspective is called contamination modeling. At the moment, one of the biggest knowledge gaps in this field is how to consider the effect of different materials on the movement of liquids such as water on different automotive surfaces like glass, plastic, rubber and painted metal. The work presented in this research study has been carried out to investigate and establish the most suitable simulation strategies to match numerical predictions with experimental data for flow of water over different automotive surfaces. Following a comprehensive parametric study of simulation parameters, it was found that the most suitable model that can be tweaked to achieve different flow properties with different surfaces is a dynamic contact angle model. The Blended Kistler model available in STAR-CCM+ required specific values for static, advancing and receding contact angles to optimize a surface for a given material. Therefore, droplet experiments of two droplet sizes were initially carried out for all tested materials at different inclinations and necessary flow parameters were recorded. All experiments were carried out using an approach known as light induced fluorescence imaging where the captured images provided a very convenient method for post processing in computational software. Results from droplet experiments showed that water moved quickest on plastic and slowest on glass. Static contact angle measurements were carried out first on horizontal surfaces. Afterwards, the surface was inclined at 15, 30, 45, 60, and 75 degrees to measure changes in contact angle and velocities. The surfaces for glass and painted metal were directly taken from the door of a Volvo S60 while a separate surface was used for plastic and rubber. These results were then used to create simulation setups for rivulets in STAR-CCM+ with the multiphase modeling approach known as volume of fluid. Rivulet simulations were carried out for all four materials at five different inclinations and the results were compared and validated with experimental data. The results show good correlation between numerical predictions for rivulet movement and experimental data emphasising on the possibility of fine-tuning the surfaces of a simulation setup to represent different material properties.