Side Crash Pressure Sensor response Prediction: CPM approach

Side Impact crashes are generally hazardous because there is no room for large deformation to protect an occupant from the crash forces. A crucial point in side impacts is the rapid intrusion of the side structure into the passenger compartment which need sufficient space between occupants and door trim to enable a proper unfolding of the side airbag. This problem can be alleviated by using the rising air pressure inside the door as an additional input for crash sensing. With improvements in the crash sensor technology, pressure sensors that detect pressure changes in door cavities have been developed recently for vehicle crash safety applications. The crash pulses recorded by the acceleration-based crash sensors usually exhibit high frequency and noisy responses. The data obtained from the pressure sensors exhibit lower frequency and less noisy responses. Due to its ability to discriminate crash severities and allow the restraint devices to deploy earlier, the pressure sensor technology has gained its popularity for side crash applications. CAE based calibration approach reduces cost of multiple physical tests required for side airbag algorithm development to deploy the airbags. With a goal to achieve CAE based calibration such that side airbag deployment algorithms can be enhanced with the help of pressure sensors, Corpuscular Particle Method (CPM) was adopted to predict the pressure responses of side crash pressure sensors. The major challenge was to capture the change in pressure accurately in side door cavity during an event of side crashes in digital environment. Also the challenge was to develop robust CAE methodology which can predict sensible pressure responses during event of high speed as well as slow speed side crashes. This paper describes the innovative CPM airbag based methodology developed to predict the pressure response and its correlation with side impact physical tests.