Abstract
RESEARCH AND/OR ENGINEERING QUESTIONS/OBJECTIVE Meeting global pedestrian protection requirements for regulatory compliance and consumer metrics with passive hood designs can be challenging for some vehicle designs due to factors such as vehicle hood strength, regulatory requirements and engine packaging constraints. Use of a deployable hood to provide the necessary deformation space enables meeting pedestrian protection requirements. The objective of this study is to show how math modeling methods can be used to lead the design of parts that surround the active hood sensor for effective pedestrian detection sensor calibrations. METHODOLOGY The use of a pressure tube sensor in the front bumper energy absorber to sense a pedestrian impact is an effective method found on production automobiles. Calibrating the pressure tube sensors for discriminating between pedestrian impactors and immunity impactors can be a big challenge. In this study, different approaches were captured using CAE to influence the pressure sensors’ signal. Design variables considered in our study involve bumper foam geometry, density, proximity to surrounding parts, and other parameters which influence foam’s compression and subsequent pressure signal to the sensors. RESULTS In this paper, we show the pressure signal across the width of the vehicle for different Pedestrian impactors and immunity impactors. This study demonstrates how pedestrian detection pressure tube predictions will help the designer early in a program’s timing to balance the foam profile for the pedestrian detection sensing signal, passive pedestrian protection, and aesthetics. LIMITATIONS OF THIS STUDY CAE model is correlated to physical test data. Limitations including plastic material models and fascia/grille clip model assumptions are outlined. Comparison of the pressure signal between the PDI2 and the Human Body Model is limited. WHAT DOES THE PAPER OFFER THAT IS NEW IN THE FIELD INCLUDING IN COMPARISON TO OTHER WORK BY THE AUTHORS? The paper outlines a technique of using CAE predictions to calibrate pedestrian protection pressure tube sensors before physical hardware is available. Since the CAE based evaluations are completed early in a program’s timing, CAE driven designs can result in successful pedestrian detection impacts and immunity impact calibrations with less physical tests.