Side Impact Pressure Sensor Predictions with Computational Gas and Fluid Dynamic Methods

Paper #:
  • 2017-01-0379

  • 2017-03-28
  • 10.4271/2017-01-0379
Tyan, T., Shaner, L., Niesluchowski, M., Kochhar, N. et al., "Side Impact Pressure Sensor Predictions with Computational Gas and Fluid Dynamic Methods," SAE Int. J. Engines 10(2):420-456, 2017, doi:10.4271/2017-01-0379.
Three computational gas and fluid dynamic methods, CV/UP (Control Volume/Uniform Pressure), CPM (Corpuscular Particle Method), and ALE (Arbitrary Lagrangian and Eulerian), were investigated in this research in an attempt to predict the responses of side crash pressure sensors. Acceleration-based crash sensors have been used extensively in the automotive industry to determine the restraint system firing time in the event of a vehicle crash. The prediction of acceleration-based crash pulses by using computer simulations has been very challenging due to the high frequency and noisy responses obtained from the sensors, especially those installed in crush 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 crash applications. Unlike the acceleration-based crash sensors, the data recorded by the side crash pressure sensors exhibits lower frequency and less noisy responses. The lower frequency and less noisy response characteristics are more suitable for CAE (Computer Aided Engineering) predictions.Fifteen benchmark tests, in three groups, were designed and conducted to better understand the pressure sensor responses under different impact conditions and to provide data for the evaluation of the three computational gas and fluid dynamic methods. The first group of benchmark tests included a piston compression test with two different gases being compressed in a rectangular container. The second group of benchmark tests consisted of a rigid impactor or a deformable barrier hitting a rectangular steel box with and without a hole as well as at different impact speeds. The third group of benchmark tests involved a rigid impactor or a deformable barrier hitting a vehicle side door with different openings and at different impact speeds. To ensure the robustness of CAE predictions for different test conditions, variables such as, structural design, hole size, hole location, sensor location, impactor type, and impact speed, were considered when designing the fifteen benchmark tests.To choose appropriate approaches for side crash pressure sensor predictions, three computational gas and fluid dynamic methods available for SFI (Structure-Fluid Interaction) applications were evaluated in this research. The three methods, including two computational gas dynamic methods, the CV/UP and CPM methods, and one computational fluid dynamic method, the ALE method, were employed to simulate the fifteen benchmark tests and to understand their corresponding numerical performances. The predictions of the benchmark tests including the structure deformation mode and the pressure response are compared to those of the tests. The advantages and limitations of each method for the different variables are discussed in details based on the results obtained from the numerical simulations. In addition, computation efficiency and user-friendliness for the three methods are also compared. In addition, four full vehicle tests were selected to assure that the pressure sensor prediction capability can be used in the full vehicle test environments. The main objective of this research is to identify the most appropriate methods to predict pressure sensor responses and to enable computer simulations for the development of restraint deployment algorithms associated with the side crash pressure sensors. It is also hoped that the enhancements and developments made throughout this research would allow the three methods to be applied to a broader range of SFI problems.
SAE MOBILUS Subscriber? You may already have access.
Members save up to 40% off list price.
HTML for Linking to Page
Page URL

Related Items

Technical Paper / Journal Article
Training / Education
Technical Paper / Journal Article
Technical Paper / Journal Article