Todd N. Clark

Side crash pressure sensor prediction: an improved corpuscular particle method

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.

Side Crash Pressure Sensor Prediction: An ALE Approach

An Arbitrary Lagrangian Eulerian (ALE) approach was adopted in this study to predict the responses of side crash pressure sensors in an attempt to assist pressure sensor algorithm development by using computer simulations. Acceleration-based crash sensors have traditionally been used to deploy restraint devises (e.g., airbags, air curtains, and seat belts) in vehicle crashes. The crash pulses recorded by acceleration-based crash sensors usually exhibit high frequency and noisy responses depending on the vehicles structural design. As a result, it is very challenging to predict the responses of acceleration-based crash sensors by using computer simulations, especially those installed in crush zones. Therefore, the sensor algorithm developments for acceleration-based sensors are mostly based on physical testing.With the advancement in the crash sensor technology, pressure sensors that detect pressure change in door cavities have been developed recently and production vehicle applications are increasing. The pressure sensors detect pressure change when there is a change in the door volume. Due to the nature of pressure change, the data obtained from side crash pressure sensors exhibits lower frequency and less noisy responses which are quite different from those of the acceleration-based crash sensors. The technology is most promising for side crash applications due to its ability to discriminate crash severities and deploy airbags earlier. The lower frequency and less noisy responses are also more suitable for non-linear finite element codes to predict.To help understand the responses of pressure sensors and obtain reliable test data for model developments, fourteen different benchmark tests were designed and performed in this research. The first set of benchmark tests included a rectangular steel container with one side being compressed while all other sides were fixed to simulate a piston compression condition. The second set of benchmark tests, a series of eight, involved 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 component tests to obtain the corresponding responses. The third set of benchmark tests, a series of five, involved a rigid impactor or a deformable barrier hitting a vehicle side door with different openings. Similar to the second set of the benchmark tests; different speeds were chosen to create different crash severities. Computer simulations for all fourteen benchmark tests were conducted by employing the ALE method as one of the studies in this research. The results obtained from the benchmark tests and the computer simulations are presented and discussed in this paper.