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Fea Simulations For Comparison To Thermal Data Obtained from Vehicle Testing on The Grossglockner High Alpine Road
EuroBrake2016/EB2016-SVM-065

Authors

Wren Stewart, Suarez Fernando, Sims, Richard*
EURAC Poole Ltd, UK [A member of the MAT Foundry Group Ltd]

Abstract

KEYWORDS – thermal analysis, ventilated brake rotor, finite element analysis, thermal loading, alpine testing

ABSTRACT

With an increasing stringency upon carbon emissions throughout the automotive sector, mass reduction strategies for components and assemblies are being brought evermore closely into focus. For the safety-critical braking sub-assembly, mass reductions are competitively requisite and cannot negate any safety or performance characteristics. This is where the use of increasingly comprehensive analytical methods, such as Finite Element Analysis (FEA) has yielded merits. Presented is a methodology for determining a suitable simulation model for the prediction of thermal effects for brake rotors, in particular those induced under the operational conditions during a descent of Grossglockner Alpine Pass (GAP). Under these driving conditions, the sustained braking subjects the rotor to considerably higher levels of heat flux, and hence the notable use of the GAP in automotive testing.

Thermal FEA simulations were conducted alongside experimental procedures, closely matching the vehicle dynamics in order to compare the thermal behaviour. Yielding a high degree of correlation, these simulations provided the opportunity to realise a design procedure that eliminated the need for native experimental data. This offered demonstrative validation of untested rotor geometries at an early stage in the design. Presented is a simplified thermal study of a complex transient system where a single-body approach was used. Necessary simplifications were utilised and this included the omission of some complex dynamic phenomena e.g. turbulent cooling. These reductions in complexity provided a means to create a procedural basis from which to develop. Such models will be subjected to sustained refinement and validation against additional data sets to improve the prediction efficacy

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