David James Bauch

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.

Side Crash Pressure Sensor Prediction for Body-on-Frame Vehicles: An ALE Approach

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.

Side Crash Pressure Sensor Prediction for Unitized Vehicles: An ALE Approach

With a goal to help develop pressure sensor calibration and deployment algorithms using computer simulations, an Arbitrary Lagrangian Eulerian (ALE) approach was adopted in this research to predict the responses of side crash pressure sensors for unitized vehicles. For occupant protection, acceleration-based crash sensors have been used in the automotive industry to deploy restraint devices when vehicle crashes occur. 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. Instead of using acceleration (or deceleration) in the acceleration-based crash sensors, the pressure sensors utilize pressure change in a door structure to determine the deployment of restraint devices. The crash pulses recorded by the acceleration-based crash sensors usually exhibit high frequency and noisy responses. Different from those of the acceleration-based crash sensors, 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. The lower frequency and less noisy characteristics are also more suitable for non-linear finite element codes to predict.Fifteen different benchmark problems were designed and tested in the first stage of this research to investigate the responses of pressure sensors in different impact conditions and the capabilities of the ALE method in the predictions of different pressure sensor responses. The fifteen benchmark problems were divided into three groups to examine the capabilities of the ALE method in detail. Different structures, gases, hole locations, sensor locations, hole sizes, impact speeds, and impactors, were chosen in the fifteen benchmark problems so that the sensitivity of the pressure responses to different factors could be obtained and understood. Computer simulations conducted by employing the ALE method for all fifteen benchmark problems were compared to their corresponding theoretical solutions or test data. The correlations between the tests and the computer simulations were found to be reasonable as reported in a paper published previously.The research was advanced into its final stage, full vehicle tests, after the positive results obtained from the benchmark study. The full vehicle study included two major vehicle architectures, body-on-frame and unitized, that are commonly used to design vehicles in the automotive industry. This paper focuses on the unitized vehicles. A total of thirteen tests, including different body styles, powertrains, drivetrains, test modes, and impact speeds, were investigated.A simulation methodology was developed in this study to correlate the structural responses and to predict the pressure sensor responses for unitized vehicles. The results obtained from the developed methodology using the ALE simulations are compared to those obtained from the corresponding tests. In the full vehicle study, low speed impact conditions were found to be more challenging to predict compared to those of the high speed impact conditions. This is because the pressure responses for the low speed impacts are usually much weaker than those of the high speed impacts. The numerical errors obtained from the simulations become more significant when the magnitudes of the pressure responses are low. The numerical errors induced by the coupling of Lagrangian and Eulerian calculation need to be distinguished and ignored (or filtered) when processing the pressure information. The slopes, peak values, and overall shapes of the predicted pressure responses correlate reasonably with most of the full vehicle tests selected. The correlations of the pre-peak responses are better than those of the post-peak responses which involve air leakage. The oblique pole, IIHS MDB, and FMVSS 214 MDB test modes create distinct door deformations and pressure responses which can be predicted by the computer simulations reasonably. Sensor engineers analyzed the results obtained from a FMVSS 214 simulation and confirmed that replacing the test data with the predicted results would result in the same deployment algorithm. Language: en