In the automotive industry, the need to meet the durability requirements in a very early stage of the development of a new vehicle model is becoming more and more crucial. This is a key factor that can reduce the time to market and avoids modifying substantially the design if a component fails earlier than expected. This is also a challenging task for several reasons; in the early phase the primary design suffers from a lack of knowledge about the loads that the new vehicle will experience in its life. In literature ([1][2][5][6][7]) several methods have been proposed; for instance the so-called digital test track approach ([1]) is a CAE-based tool in which the vehicle and the road are modeled in a multibody environment together with a detailed representation of the tire and the driver in order to perform a replication of a test drive. This predictive method is very valuable but still requires a lot of information about the vehicle’s components that is usually not available at this stage of the vehicle development. On the other hand a pure test-based procedure suffers from other problems such as the need of a mule vehicle and long and costly test campaigns that need to be repeated at each component’s modification. A hybrid approach has then been proposed and implemented successfully by LMS on industrial size cases. This approach known as Time Waveform Replication (TWR) ([2]) relies on a set of test data and multibody model available from test drives carried out on a predecessor or a vehicle similar to the one that is being currently designed. The data collected on a road test is used to back-calculate the equivalent spindle displacements that will cause the same forces on the multibody model that are experienced in the test sessions. This approach has several beneficial aspects with respect to the two mentioned before. The tire model does not need to be accurate since the displacement are applied directly to the spindles (but the application can be easily extended to “road profile identification” if a detailed tire model is available). Moreover it is well known that if the forces measured at the spindles are applied directly to the unconstrained multibody model, it will result in an undesired drift of the model due to a mismatch in the mass and inertial properties between the real vehicle and its model. This is even more important when measured forces are applied to a new vehicle model that is only similar to the tested one. The TWR approach relies on a linearized model of the vehicle that is derived directly from its multibody representation. Then the spindle displacements are back-calculated by pseudo-inversion of the Frequency Response Function of the system and the application of the desired target signals. This method gives a direct result only if the system is linear; this is typically not the case in the field of vehicle dynamics where the geometry of the suspension, the non-linear properties of the dampers and bushings together with the intrinsic non-linear nature of the constrained equation of motion implies that the linearized model used by TWR is valid only for small changes to the configuration at the instant of linearization. To cope with this problem, the TWR sets up an iterative process that uses the output error to update the input. In case of high non-linearities or large changes in the configuration the linearized model can be also updated. In this paper the integration of the TWR process in a multibody code such as LMS Virtual.Lab Motion is described. In particular a new tool named LMS Motion-TWR has been developed. The application guides the engineer in setting up the models inputs and outputs, allows to drive the multibody code to compute the linearized model and the association between the test data and the numerical responses of the model. The computation of the driving signals is performed by TWR core solver as a background process allowing the user to focus on the analysis of the results rather than spending time in dealing with file conversion and transfer from one software to another as was done in the past. Moreover several post-processing tools are available such as time and frequency domain plots, RMS error and X-Y plots. Finally this paper describes the application of the tool in an industrial case scenario using a model of a quad. A quad was equipped with several sensors and driven on a test track. The collected data is then used in the Motion TWR software to compute the equivalent spindle displacements. Since some of the front suspension parts are modeled as flexible bodies the reverse load identification analysis is completed by a durability calculation.

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