Development of a Modeling Approach to Numerically Predict Filtration Efficiencies of Brake Dust Particle Filters

According to the European Environment Agency, air pollution is the biggest environmental health risk in Europe. Since traffic is one of the main contributors of fine dust, technical solutions are necessary to reduce the particulate emission footprint of vehicles. Also, the Health Effects Institute hosted recently an international workshop on non-tailpipe emissions. Brake dust filtration concepts have proven to be a promising solution to significantly reduce fine dust emissions from brakes directly at the source. While CFD simulations for inner-ventilated brakes have become state-of-the-art, a holistic model from particle generation and emission to particle dynamics in the vicinity of the brake is not yet available. However, a good modeling approach of particle tracks is essential to predict filtration efficiencies of brake dust particle filters. Based on current literature data and models, and independent of the turbulence model, filtration efficiencies cannot be predicted with required accuracy of <10%. Therefore, a new, reliable and quantifiable simulation model is developed. The simulation model has been implemented in ANSYS Fluent using the Discrete Particle Model. The rotation of the inner-ventilated brake disc is modeled via a Multiple Reference Frame combined with moving wall boundary conditions. Several emission locations are defined, and a subsequent parameter optimization was used to determine the parameters not accessible so far by experimental means. For validation, experiments were conducted based on an enclosure-in-chamber setup on an inertia brake dynamometer (LINK 3900) with low background concentration (<10 #/cm^3 measured with a TSI CPC 3756). Based on the WLTC Class 3 driving cycle, four floating caliper brakes in combination with 10 different filter designs are tested to validate the model. A maximum deviation between simulation and experiments of <10% in terms of filtration efficiency was achieved.