Browse Publications Technical Papers 2012-01-0043

Side Crash Pressure Sensor Prediction: An Improved Corpuscular Particle Method 2012-01-0043

In an attempt to predict the responses of side crash pressure sensors, the Corpuscular Particle Method (CPM) was adopted and enhanced in this research. Acceleration-based crash sensors have traditionally been used extensively in automotive industry to determine the air bag firing time in the event of a vehicle accident. The prediction of crash pulses obtained from the acceleration-based crash sensors by using computer simulations has been very challenging due to the high frequency and noisy responses obtained from the sensors, especially those installed in crash zones. As a result, the sensor algorithm developments for acceleration-based sensors are largely based on prototype testing. With the latest advancement in the crash sensor technology, side crash pressure sensors have emerged recently and are gradually replacing acceleration-based sensor for side impact applications. Unlike the acceleration-based crash sensors, the data recorded by the side crash pressure sensors exhibits lower frequency and less noisy responses which is more conductive for CAE prediction.
In the attempt to predict the side crash pressure sensor responses, fourteen different benchmark tests were designed and conducted to provide data for model validations. The fourteen benchmark tests can be divided into three sets based on the structure designs. The first set of benchmark tests included a rectangular rigid container with one side being compressed while all other sides were fixed to simulate a piston compression condition. The second set of benchmark tests contained a rigid impactor or a deformable barrier hitting a rectangular steel box with and without a hole. Different speeds were chosen in the second set of benchmark tests to obtain the corresponding pressure responses. The third set of benchmark tests involved a rigid impactor or a deformable barrier hitting a real vehicle side door with different openings. In the baseline door test, the window weather strip and speaker were kept and all holes in door inner were closed to represent a production door. To ensure the robustness of CAE predictions for different door designs, the window weather strip was removed and some holes in the door inner were opened in some of the door benchmark tests. Computer models were created according to the corresponding test conditions.
The CPM method originally developed in LS-DYNA to simulate the deployments of side air bags and side air curtains was adopted and improved in this research to predict the responses of the side crash pressure sensors. One of the main purposes of adopting such method in this project is trying to expand the application of the CPM method to problems that do not involve inflators. With major improvements in the CPM method through this research in the past two years, not only the responses of side crash pressure sensor can be predicted but also the computation time required to complete such simulations has been shortened. The development of the modeling methodology to predict the responses of the side crash pressure sensors will also make it possible to use computer simulations as part of side crash sensor development and results in more robust sensor firing algorithm.


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