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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. Supplementing the use of endurance testing data, 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 driving state spaces is condensed into a substitute load case.

In the digital prototype development process of a new Mercedes-Benz, thermal protection is an important task that has to be fulfilled. In the early stages of development, numerical methods are used to detect thermal hotspots in order to protect temperature sensitive parts. These methods involve transient full Vehicle Thermal Management (VTM) simulations to predict dynamic vehicle heat-up during critical load cases. In order to simulate thermal control mechanisms, a coupled 1D to 3D thermal vehicle model is built in which the coolant and oil circuit of the engine, as well as the exhaust flow are captured in detail. When performing a transient 3D VTM analysis, the conduction and radiation phenomena are simulated using a transient structure model while the convective phenomena are co-simulated in a steady state fluid model. Both models are brought to interaction at predetermined points by an automatized coupling method.