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A computational model of a mid-size sport utility vehicle was developed using MADYMO. The model includes a detailed description of the suspension system and tire characteristics that incorporated the Delft-Tyre magic formula description. The model was correlated by simulating a vehicle suspension kinematics and compliance test. The correlated model was then used to simulate a J-turn vehicle dynamics test maneuver, a roll and non-roll ditch test, corkscrew ramp and a lateral trip test, the results of which are presented in this paper. The results indicate that MADYMO is able to reasonably predict the vehicle and occupant responses in these types of applications and is potentially suited as a tool to help setup a suite of vehicle configurations and test conditions for rollover sensor testing. A suspension system sensitivity study is presented for the laterally tripped non-roll event.

An assessment tool was developed for rollover CAE signals evaluation to assess primarily the qualities of CAE generated sensor waveforms. This is a key tool to be used to assess CAE results as to whether they can be used for algorithm calibration and identify areas for further improvement of sensor. Currently, the method is developed using error estimates on mean, peak and standard deviation. More metrics, if necessary, can be added to the assessment tool in the future. This method has been applied to various simulated signals for laboratory-based rollover test modes with rigid-body-based MADYDO models.

This paper presents an image analysis of a laboratory-based rollover crash test using camera-matching photogrammetry. The procedures pertaining to setup, analysis and data process used in this method are outlined. Vehicle roll angle and rate calculated using the method are presented and compared to the measured values obtained using a vehicle mounted angular rate sensor. Areas for improvement, accuracy determination, and vehicle kinematics analysis are discussed. This paper concludes that the photogrammetric method presented is a useful tool to extract vehicle roll angle data from test video. However, development of a robust post-processing tool for general application to crash safety analysis requires further exploration.

Full vehicle dynamic crash tests are commonly used in the development of rollover detection sensors, algorithms and occupant protection systems. However, many published studies have utilized component level rollover test fixtures for rollover related occupant kinematics studies and restraint system evaluation and development. A majority of these fixtures attempted to replicate only the rotational motion that occurs during the free flight phase of a typical full vehicle rollover crash test. In this paper, a description of the methods used to design a new dynamic component rollover test device is presented. A brief summary of several existing rollover component test methods is included. The new system described in this paper is capable of replicating the transfer of lateral energy into rotational vehicle motion that is present in many tripped laboratory based rollover crash tests.

State of the art electronic restraint systems rely on the acceleration measured during a vehicle crash for deployment decisions. The acceleration signal is analyzed with different criteria, among which the velocity change is a dominant criterion in almost any existing crash detection algorithm. Sensors in the front crush zone have recently been added to help develop restraint systems that comply with the new FMVSS208 and EuroNCAP regulations. Front crash sensors are usually evaluated for their velocity change during a crash and typically play a key role in the deployment decision. CAE based FEA analysis has recently been used to generate signals at the sensor module locations in crash simulations to provide supplemental information for crash sensing algorithm development and calibration. This paper presents an initial effort in developing a velocity-based crash detection algorithm, that allows broad use of CAE generated velocity time histories for system calibration.

This paper describes (1) the development of a component test methodology for testing a cooling module including radiator, condenser and trans. cooler, and (2) the associated CAE model development. A series of light truck/SUV cooling module component tests were conducted to obtain their characteristics as inputs for frontal impact and sensor modeling development. First, the cooling module component CAE sub-model was developed using soft springs along with fine-mesh sheet metal shell elements. Second, simulated sub-model results were correlated fairly well with the test data. Third, this component CAE sub-model was then incorporated into a full vehicle CAE model that was used for frontal impact (NCAP) and sensor development. Results indicated that the proposed test method for cooling module components provided consistent data and the results from cooling module sub-model can be incorporated into the full vehicle CAE model for improving the quality and accuracy of CAE models.

CAE has played a key role in development of the rollover safety technology by reducing the required number of prototypes. CAE-led Design Of Experiments (DOE) studies have helped in developing the process to minimize the number of CAE runs and to optimize use of the prototypes. This paper demonstrates the use of CAE/DOE for the design and optimization of rollover vehicle prototypes and also investigates effects of various factors in the selection of vehicle configuration for rollover sensor calibration testing. The process described herein has been successfully applied to vehicle programs. Modeling and analysis guidelines are also presented for CAE engineers to help in optimizing vehicle prototypes at program level.