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A Coupled Approach Using CFD and FEA Solution for Solving the Cooling of Automotive Disk Brake
ISSN: 0148-7191, e-ISSN: 2688-3627
Published January 13, 2020 by SAE International in United States
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The cooling efficiency of automotive disk brakes comprises an important area of development, since the system performance is directly related to the overheating level that is being achieved during the braking period. The heat generation occurs during the conversion of kinetic energy into heat. After this process, it is crucial an effective thermal dissipation in order to assure the decay of temperature levels. Such thermal dissipation results mainly from the interaction of the brake components with the external airflow in the wheel arch, where there are the occurrence of heat transfer mechanisms including conduction, convection and radiation. In this sense, through the application of simulation models, it is possible to predict the thermal- structural behavior by combining solutions in the fluid dynamics and structural areas. In this work, a one-way fluid-structure approach is proposed by combining conventional CFD (Computational Fluid Dynamics) and FEA (Finite Element Analysis) models. The CFD modeling aim to solve the disk brake cooling during the airflow passage in the wheel arch and its interaction with the brake components. The FEA modeling aim to solve the thermal structural behavior that is originated during the braking action due to the thermal loads and certain constraints. When coupling both models in a one-way coupled solution, it is noticed a faithful characterization of the thermal-structural behavior, making possible the investigation of key parameters that leverage better performances. A design optimization of the disk brake is performed to obtain an improved design based on thermal exchange criteria between the disk and the external airflow.
CitationJunior, J. and Dutra, D., "A Coupled Approach Using CFD and FEA Solution for Solving the Cooling of Automotive Disk Brake," SAE Technical Paper 2019-36-0012, 2020, https://doi.org/10.4271/2019-36-0012.
Data Sets - Support Documents
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