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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.

In vehicle crash tests, an unbelted occupant's kinetic energy is absorbed by the restraints such as an air bag and/or knee bolster and by the vehicle structure during occupant ride-down with the deforming structure. Both the restraint energy absorbed by the restraints and the ride-down energy absorbed by the structure through restraint coupling were studied in time and displacement domains using crash test data and a simple vehicle-occupant model. Using the vehicle and occupant accelerometers and/or load cell data from the 31 mph barrier crash tests, the restraint and ride-down energy components were computed for the lower extremity, such as the femur, for the light truck and passenger car respectively.