Application of CFD to Predict Brake Disc Contamination in Wet Conditions

Event
SAE 2016 World Congress and Exhibition
Authors Abstract
Content
Brake disc materials are being utilised that have low noise/dust properties, but are sensitive to contamination by surface water. This drives large dust shields, making brake cooling increasingly difficult. However, brake cooling must be delivered without compromising aerodynamic drag and hence CO2 emissions targets.
Given that front brake discs sit in a region of geometric, packaging and flow complexity optimization of their performance requires the analysis of thermal, aerodynamic and multi-phase flows. Some of the difficulties inherent in this task would be alleviated if the complete analysis could be performed in the same CAE environment: utilizing common models and the same solver technology.
Hence the project described in this paper has sought to develop a CFD method that predicts the amount of contamination (water) that reaches the front brake discs, using a standard commercial code already exploited for both brake disc thermal and aerodynamics analysis.
This project builds on the capabilities of the Lattice Boltzman CFD code, Exa PowerFLOW, using its inherently unsteady simulation capability to capture the flow around the front brakes of an SUV (Range Rover Sport), sliding mesh to represent wheel rotation; along with Lagrangian Particle Tracking and Thin Film modelling to account for both airborne spray and surface water, respectively. Validation data is provided from experiments carried out in the FKFS Thermal Tunnel.
The numerical method is seen to reproduce the trends from the wind tunnel experiment.
Meta TagsDetails
DOI
https://doi.org/10.4271/2016-01-1619
Pages
8
Citation
Schembri Puglisevich, L., Gaylard, A., Osborne, M., Jilesen, J. et al., "Application of CFD to Predict Brake Disc Contamination in Wet Conditions," SAE Int. J. Passeng. Cars - Mech. Syst. 9(2):800-807, 2016, https://doi.org/10.4271/2016-01-1619.
Additional Details
Publisher
Published
Apr 5, 2016
Product Code
2016-01-1619
Content Type
Journal Article
Language
English