In an attempt to assist pressure sensor algorithm and calibration development using computer simulations, an Arbitrary Lagrangian Eulerian (ALE) approach was adopted in this study to predict the responses of side crash pressure sensors for body-on-frame vehicles. Acceleration based, also called G-based, crash sensors have been used extensively to deploy restraint devices, such as airbags, curtain airbags, seatbelt pre-tensioners, and inflatable seatbelts, in vehicle crashes. With advancements in crash sensor technologies, pressure sensors that measure pressure changes in vehicle side doors have been developed recently and their applications in vehicle crash safety are increasing. The pressure sensors are able to detect and record the dynamic pressure change when the volume of a vehicle door changes as a result of a crash. Due to the nature of pressure change, data obtained from the pressure sensors exhibits lower frequency and less noise in the responses which are significantly different from those of the acceleration-based crash sensors. This technology is very suitable for side crash applications due to its ability to discriminate crash severities and deploy restraint devices earlier in the event. The lower frequency and less noise in the responses are also more suitable for non-linear finite element codes to simulate.
To help understand the responses of pressure sensors and the capabilities of the ALE method in the prediction of pressure sensor responses, fifteen different benchmark tests were designed and performed in previous research. The fifteen benchmark tests were divided into three groups so that the capabilities of the ALE method could be examined in detail. The first group 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. Two different gases were tested in the first group of benchmark tests. Solutions for the first group of benchmark tests can be derived theoretically. The second group 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. In addition, different speeds were chosen in the second group of component tests to obtain their corresponding responses. The third group 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 group of benchmark tests, different speeds were chosen to create different crash severities. Computer simulations conducted employing the ALE method for all fifteen benchmark tests were compared to their corresponding theoretical solutions or test data. Reasonable correlations had been found between the benchmark tests and the computer simulations as presented and discussed in a previous paper.
The success of the benchmark study allowed the advancement of the research into its final stage, full vehicle tests. The full vehicle tests contained both body-on-frame and unitized vehicles which are the two main vehicle architectures used in the automotive industry. This paper focused on the body-on-frame vehicles with fifteen tests, including a combination of different body styles, powertrains, drive-trains, wheel bases, test modes, and impact speeds, being investigated. In this study, an approach was developed to correlate the structural responses and to predict the pressure sensor responses for body-on-frame vehicles. The results obtained from the developed method are compared to those obtained from tests. Contrary to common thoughts, it was found that the pressure responses of the low speed test conditions are more challenging to predict than those of the high speed test conditions. This is because the pressure responses for the low speed test conditions are usually very weak. The errors obtained from the numerical simulations become predominant when the magnitudes of the pressure responses are small. The numerical fluctuations induced by the coupling of Lagrangian and Eulerian calculation need to be distinguished and ignored (or filtered) when processing the pressure information. Overall, the slopes, peak values, and shapes of the predicted pressure responses correlate reasonably well with those of the fifteen full vehicle tests selected. The pre-peak responses seem to correlate better to those of the tests than the post-peak responses which involve air leakage. The door pressure changes due to the impacts of oblique pole, IIHS MDB, and FMVSS 214 MDB, can be captured reasonably by the computer simulations.
