Automated Design Optimization of Side View Mirror Geometries for Improved Autonomous Sensor and Vehicle Soiling Performance

The use of sensors in advanced driver-assistance systems (ADAS) and autonomous vehicles has been accelerating over the past few years largely driven by regulatory and consumer interest in safety applications. These sensors help to prevent accidents and protect drivers by assisting with the monitoring, warning, braking, and steering tasks. As several unfortunate examples have highlighted these valuable systems can reduce safety if the sensors are not operating un-impaired. Planning for harsh weather environments is critical to the success of these systems. This study presents a fully automated workflow for an industrial side mirror geometry optimization for improved sensor performance under soiling conditions. The methodology includes CAD parametrization, multiphase simulation setup, intelligent design optimization and a detailed result analysis. All relevant aspects like external flow, geometrical fidelity and multiphase interaction are considered. A source term is applied to a fluid film model that approximates the effect of raindrops accumulating on the side view mirror. The average fluid film thickness on the camera lens is monitored and a numerical integration of the mean thickness vs time is used to compare designs. The parametric mirror geometry modifies the depth, location, and shape of trench surrounding the camera mound. This design concept is intended to capture and redirect the fluid film to avoid contact with the camera lens. The parametrized, lower part of the side mirror is modified by the optimization algorithm resulting in a 26% decrease in the time integral of the film thickness on the camera lens.