A New Approach to Predicting Component Temperature Collectives for Vehicle Thermal Management

Paper #:
  • 2017-01-0134

Published:
  • 2017-03-28
Abstract:
There is a growing need for life-cycle data – so-called collectives – when developing components like elastomer engine mounts. Current standardized extreme load cases are not sufficient for establishing such collectives. Instead of using endurance testing data – which necessitates full vehicle on-road tests – a prediction methodology for component temperature collectives utilizing existing 3D CFD simulation models is presented. The method uses support points to approximate the full collective. Each support point is defined by a component temperature and a position on the time axis of the collective. Since it is the only currently available source for component temperature data, endurance testing data is used to develop the new method. The component temperature range in this data set is divided in temperature bands. Groups of driving states are determined which are each representative of an individual band. Each of the resulting four groups of driving states is condensed into a substitute load case. To determine component temperatures for these substitute load cases, combined numerical CFD and heat transfer simulations are performed. The simulations follow the standard Mercedes-Benz Vehicle Thermal Management (VTM) methods and their results show good agreement with measurements at a climatic wind tunnel. A support point’s position on the time axis is determined from the frequency of occurrence of the corresponding group of driving states in the endurance testing data. Using these two pieces of information, four support points are defined for the component temperature collective. Their dimensions, component temperature and position on the time axis, are independent of each other. Following this approach, a new geometry or vehicle type affects only the component temperature through changes in the simulation model. The position of the support point on the time axis depends solely on the assumed driving profile. Thus, this method enables the prediction of component temperature collectives in an early development stage.
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