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An engine cooling system in an automotive vehicle comprises of heat exchangers such as a radiator, charge air cooler and oil coolers along with engine cooling fan. Typical automotive engine-cooling fan assembly includes an electric motor mounted on a shroud that encloses the radiator core. One of main drivers of fan shroud design is Noise, Vibration, and Harshness (NVH) requirements without compromising the main function of airflow for cooling requirements. In addition, there is also a minimum stiffness requirement of fan shroud which is often overlooked in arriving at optimal design of it. Low Speed Damageability (LSD) assessment of an automotive vehicle is about minimizing the cost of repair of vehicle damages in low speed crashes. In low speed accidents, these fan motors are subjected to sudden decelerations which cause fan motors to swing forward thereby damaging the radiator core. So designing fan shroud for low speed damageability is of importance today.

There are a wide variety of approaches to model the automotive seat and occupant interaction. This paper traces the studies conducted for simulating the occupant to seat interaction in frontal and/or rear crash events. Starting with an initial MADYMO model, a MADYMO-LS/DYNA coupled model was developed. Subsequently, a full Finite Element Analysis model using LS/DYNA was studied. The main objective of the studies was to improve the accuracy and efficiency of CAE models for predicting the dummy kinematics and structural deformations at the restraint attachment locations in laboratory tests. The occupant and seat interaction was identified as one of the important factors that needed to be accurately simulated. Quasi-static and dynamic component tests were conducted to obtain the foam properties that were input into the model. Foam specimens and the test setup are discussed. Different material models in LS/DYNA were evaluated for simulating automotive seat foam.

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.