Modelling of radar performance losses during extreme snow weather conditions

Important results from the project

Within the project the aim was to develop a CAE prediction method to determine snow exposure and its effects on sensor systems. The developed complete vehicle CFD model can predict three different driving scenarios; Vehicle driving alone on snowy road, driving in falling snow, driving in snow dust of another car. The CFD model is open for a wide variety of contamination types; Today it works robust for dry snow dust, which was the focus of an extensive experimental campaign. The exposure of assisted driving sensors to snow and other contaminants can be predicted with that model.

Expected long term effects

We gained lot of knowledge about snow properties. In a first phase, the characterization of snow different snow types and conditions was conducted. Fresh snow, compressed snow on road surfaces and airborne snow in the wake of a vehicle were investigated. Useful knowledge was gained concerning snow particle shape, particle size and their structure when agglomerating on a vehicle surface. Thus, a complete vehicle fluid dynamic model, which models the snow cloud around a driving vehicle, was developed. The CFD model predicts the snow particle trajectory for different driving scenarios.

Approach and implementation

The project was split into smaller work packages, with clear targets and measurable deliveries. Volvo took effort towards the CFD modelling of the complete vehicle, while Chalmers focussed on detailed physical properties of snow. Deliverables of each work package were implemented after each successful execution. The expected extend of the project had to be adjusted, when first research findings have shown larger complexity then initially anticipated. Knowledge was shared through the continuous involvement of IAESTE students, Master theses and scientific publications.