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This paper describes (1) the findings from the implementation of a component test methodology for body, engine and transmission mounts [1, 2 and 3], and (2) the associated CAE model development and mount design robustness enhancement. A series of component tests on light truck body, engine and transmission mounts have been conducted to not only obtain their characteristics as inputs for crashworthiness analysis, but also drive mount design direction for frontal impacts.

The optimization method and CAE analysis have been widely used in structure design for crash safety. Combining the CAE analysis and optimization approach, vehicle structure design for crash can be implemented more efficiently. One of the recent safety desirables in structure design is to reduce vehicle pitch and drop. At frontal impact tests with unbelted occupants, the interaction between occupant's head and interior header/sun visor, which is caused by excessive vehicle pitch and drop, is not desired in vehicle crash development. In order to comply with the federal frontal crash requirements for unbelted occupant, it is necessary to manage the vehicle pitch and drop by improving structure design. In this paper, a systematic process of CAE analysis with optimization approach is applied for discovering the major structural components affecting vehicle pitch and drop.

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.

Spot weld is the primary joining method to assemble the automotive body structure. In any crash events some separation of spot-welds can be expected. However, if this happens in critical areas of the vehicle it can potentially affect the integrity of the structure. It will be beneficial to identify such issues through CAE simulation before prototypes are built and tested. This paper reports a spot weld modeling methodology to characterize spot weld separation and its application in full vehicle crash simulation. A generalized two-node spring element with 6 DOF at each node is used to model the spot weld. Separation of spot welds is modeled using three alternative rupture criteria defined in terms of peak force, displacement and energy. Component level crash tests are conducted using VIA sled at various impact speeds to determine mean crush load and identify possible separation of welds.

Vehicle pitch and drop play an important role for occupant neck and head injury at frontal impact. The excessive vehicle header drop, due to vehicle pitch and drop during crash, induces aggressive interaction between occupant head and sun visor/header that causes serious head and neck injuries. For most of body-on-frame vehicles, vehicle pitch and drop have commonly been observed at frontal impact tests. It is because the vehicle body is pulled downward by frame rails, which bend down during crash. Hence, the challenges of frame design are not only to absorb crash energy but also to manage frame deformation for minimizing vehicle pitch and drop. In this paper, the finite element method is used to analyze frame deformation at full frontal impact. To ensure the quality of CAE model, a full vehicle FEA model is correlated to barrier tests. In addition, a study of CAE modeling affecting vehicle header drop is performed.

This paper reports the latest development of methodologies for testing and CAE modeling of the automobile mounts. The objective of this study is to provide dynamic mount properties for product evaluation and CAE modeling guideline for crashworthiness simulations. The methodology is divided into component, subsystem and full system levels. The study at the component level is to extract the dynamic parameters of mounts, such as stiffness and damping coefficient, based on the component tests. Furthermore, such parameters are employed to investigate the interaction between mount and connecting structures at the subsystem level. A robust connection mechanism from mount to surrounding structures is also developed during this process. Finally, the results from full vehicle system tests are compared with the CAE simulations to verify the methodology at the component and subsystem levels. A robust component test methodology is the first key element of this study.

Vehicle pitch and drop has become an important subject to crash analysis due to the recent FMVSS208 requirements for unbelted occupant. During frontal impact, the excessive header drop due to significant vehicle pitch and drop can induce the contact between occupant's head and sun visor. To avoid this issue, structure design for reducing vehicle pitch and drop is essential to crash safety. Historically, CAE simulation has been used in structure design during vehicle development process. Therefore, the quality of CAE modeling for replicating vehicle pitch and drop at physical test is crucial for assisting the structure design. In this paper, the most effective components in CAE model to vehicle pitch and drop have been identified and ranked by using the results of the sensitivity study. Hence the model quality can be emphasized on those major components including front horn, kick-down of front frame, body structure at upper load path, and body mounts.

This study focuses on the modeling of a frame in a body-on-frame (BOF) vehicle to improve the prediction of vehicle response in crashes. The study is divided into three phases - component (frame material modeling), subsystem (frame sled test) and full system (full vehicle test). In the component level, we investigate the available strain rate data, the performance of various material models in crash codes and the effect of the strain rate in crash simulation. In the subsystem phase, we incorporate the strain rate modeling and expand the scope to include both the forming and the welding effects in the subsystem CAE model to improve the correlation between CAE and test. Finally the improved frame modeling methodology with strain rate, forming and welding effects is adopted in full vehicle model. It is found that the proposed frame modeling methodology is crucial to improve the pulse prediction of a full vehicle in crashes.

For a body-on-frame (BOF) vehicle, the frame is the major structural subsystem to absorb the impact energy in a frontal vehicle impact. It is also a major contributor to energy absorption in rear impact events as well. Thus, the accuracy of the finite element frame model has significant influence on the quality of the BOF vehicle impact predictability. This study presents the latest development of the frame modeling methodology on the simulation of BOF vehicle impact performance. The development is divided into subsystem (frame sled test) and full system (full vehicle test). This paper presents the first phase, subsystem testing and modeling, of the frame modeling development. Based on the major deformation modes in frontal impact, the frame is cut into several sections and put on the sled to conduct various tests. The success of the sled test highly depends on whether the sled results can replicate the deformation modes in the full vehicle.

The frontal rail is one of the most important components of a vehicle in determining crash performance, especially for a body on frame vehicle. Prior research [1] has shown that the frontal rail absorbs a significant amount of impact energy in a crash condition. In order to optimize crash performance, a component level sensitivity study was conducted to determine the effect different types of triggers would have on the performance of the frontal rail. The objective of this study is to determine the sensitivity of trigger size, trigger shape, and trigger orientation as well as to improve corresponding trigger modeling methodology by comparing crushed components to crushed CAE models. In this sensitivity study, the location of the triggers was held fixed, while the size, shape, and orientation were varied. The metric that will be used to compare the performance of these different trigger shapes is the overall stiffness of the frontal rail.

Strain rate effects have been identified as one of the most critical factors for the modeling of vehicle components in many previous investigations. The strain rate data available to the authors have been processed to obtain the input decks of a required material law in prior investigations. With the application of strain rate modeling, the strain rate database needs to be expanded. In order to continuously improve the safety CAE quality and efficiency, especially the prediction of a vehicle's pulse in a crash event, the effort has been made to include more strain rate data and extend the material database for safety CAE applications. In this study, strain rate data provided by Ispat Inland Inc. for AISI/DOE Technology Roadmap Program are processed. The material processed in this study include HSS590-CR, 440W-GA, BH300-GI, HSLA350-GI, DP600-HR, TRIP590-EG, TRIP600-CR, TRIP780-CR.