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This paper reports a study on the use of response inverse filtering (RIF) methodology for crash pulse prediction. RIF is based on the finite impulse response (FIR) and inverse filtering (IF) methods. The FIR coefficients obtained by the digital convolution theory and the least squared error approach serve to transfer response from the input (impacting or excitation) side to the output (non-impacting or receiving) side. The FIR method, a process of low pass filtering (e.g. truck body mount), is commonly used in predicting the non-impacting side (e.g. truck body or cab) response with the input excitation in the impacting side (e.g. truck frame). The accuracy in the validation and prediction via FIR transfer function depends on the frequency contents of the input and output accelerometer data from which the transfer function is developed. The prediction accuracy is low if the output data contain higher frequency components than the input.

This paper reports a study of the dynamic characteristics of body mounts in body on frame vehicles and their effects on structural and occupant CAE results. The body mount stiffness and damping are computed from spring-damper models and component test results. The model parameters are converted to those used in the full vehicle structural model to simulate the vehicle crash performance. An effective body mount in a CAE crash model requires a set of coordinated damping and stiffness to transfer the frame pulse to the body. The ability of the pulse transfer, defined as transient transmissibility[1]1, is crucial in the early part of the crash pulse prediction using a structural model such as Radioss[2]. Traditionally, CAE users input into the model the force-deflection data of the body mount obtained from the component and/or full vehicle tests. In this practice, the body mount in the CAE model is essentially represented by a spring with the prescribed force-deflection data.