Application of Model Order Reduction to nonlinear Finite Element tire models for NVH design 2019-01-1507
In order to meet customer NVH performance requirements, tire manufacturers continuously strive to improve their tire designs. Building and testing new designs has been the standard procedure for many years now. However, as the need to simultaneously optimize multiple performance criteria increases and development cycles become shorter, predictive numerical simulation techniques could be used rather than time consuming physical experiments. Especially since many tire performance areas are coupled, the experimental approach often lacks detailed insights which numerical simulations can provide. Currently, no industrially applicable fully predictive high fidelity numerical approach that incorporates the use of nonlinear Finite Element (FE) tire models for NVH design is available in literature. Therefore, a fully predictive numerical simulation approach that predicts the rolling of a tire over a coarse road surface is described in this work. The proposed approach allows to predict the dynamic contact- and hub forces that arise during rolling without the need for experimental data. These results are useful to assess and optimize the NVH performance of a specific tire design. One of the main drawbacks of using nonlinear FE tire models for NVH design in the proposed approach, is the large computational cost associated with running the numerical simulations. Therefore, application of a nonlinear Model Order Reduction (MOR) technique to the nonlinear FE tire models is described in this work as well. It is shown that application of the MOR technique to the proposed simulation approach greatly reduces the total computational time and costs. In addition, the simulation results show good correspondence with experimental data. This confirms the potential of this efficient predictive numerical simulation approach as a viable numerical alternative to the experimental based approach in tire NVH design.
Daniel De Gregoriis, Frank Naets, Peter Kindt, Wim Desmet
Goodyear SA/KU Leuven, KU Leuven/DMMS Lab, Flanders Make
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