Search Results

To evaluate vehicle crash performance in the early design stages, a reliable fracture model is needed in crash simulations to predict material fracture initiation and propagation. In this paper, a generalized incremental stress state dependent damage model (GISSMO) in LS-DYNA® was calibrated and validated for a 780-MPa third generation advanced high strength steels (AHSS), namely 780 XG3TM steel that combines high strength and ductility. The fracture locus of the 780 XG3TM steel was experimentally characterized under various stress states including uniaxial tension, shear, plane strain and equi-biaxial stretch conditions. A process to calibrate the parameters in the GISSMO model was developed and successfully applied to the 780 XG3TM steel using the fracture test data for these stress states.

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.

The objective of this paper focused on the modeling of an adaptive energy absorbing steering column which is the first phase of a study to develop a modeling methodology for an advanced steering wheel and column assembly. Early steering column designs often consisted of a simple long steel rod connecting the steering wheel to the steering gear box. In frontal collisions, a single-piece design steering column would often be displaced toward the driver as a result of front-end crush. Over time, engineers recognized the need to reduce the chance that a steering column would be displaced toward the driver in a frontal crash. As a result, collapsible, detachable, and other energy absorbing steering columns emerged as safer steering column designs. The safety-enhanced construction of the steering columns, whether collapsible, detachable, or other types, absorb rather than transfer frontal impact energy.

This paper presents the final phase of a study to develop the modeling methodology for an advanced steering assembly with a safety-enhanced steering wheel and an adaptive energy absorbing steering column. For passenger cars built before the 1960s, the steering column was designed to control vehicle direction with a simple rigid rod. In severe frontal crashes, this type of design would often be displaced rearward toward the driver due to front-end crush of the vehicle. Consequently, collapsible, detachable, and other energy absorbing steering columns emerged to address this type of kinematics. These safety-enhanced steering columns allow frontal impact energy to be absorbed by collapsing or breaking the steering columns, thus reducing the potential for rearward column movement in severe crashes. Recently, more advanced steering column designs have been developed that can adapt to different crash conditions including crash severity, occupant mass/size, seat position, and seatbelt usage.

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.

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.

Advanced high strength steels (AHSS) have been widely accepted as a material of choice in the automotive industry to balance overall vehicle weight and stringent vehicle crash test performance targets. Combined with efficient use of geometry and load paths through shape and topology optimization, AHSS has enabled vehicle manufacturers to obtain the highest possible ratings in safety evaluations by the Insurance Institute for Highway Safety (IIHS) and the National Highway Traffic Safety Administration (NHTSA). In this study, vehicle CAE side impact models were used to evaluate three side impact crash test conditions (IIHS side impact, NHTSA LINCAP and FMVSS 214 side pole) and the IIHS roof strength test condition and to identify several key components affecting the side impact test performance. HyperStudy® optimization software and LS-DYNA® nonlinear finite element software were utilized for shape and gauge optimization.

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.

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 vehicle's 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.

Explicit method is commonly used in crashworthiness analysis due to its capability to solve highly non-linear problems without numerous iterations and convergence problems. However, the time step for explicit methods is limited by the time that the physical wave crosses the element. Therefore, to avoid large amount of CPU time, the explicit method is usually used for non-linear dynamic problems with a short period of simulation duration. For problems under quasi-static loading conditions at pre-crash and post-crash, implicit method could be more efficient than explicit methods because the required computation time is much shorter. Due to the recent advance of crash codes, which allows both implicit and explicit computations to be performed in the same code, crash engineers are able to use explicit computation for crash simulation as well as implicit computation for some of the pre-crash quasi-static loading or post-crash spring back simulations.

Because of their excellent crash energy absorption capacity, dual phase (DP) steels are gradually replacing conventional High Strength Low Alloy (HSLA) steels for critical crash components in order to meet the more stringent vehicle crash safety regulations. To achieve optimal axial and bending crush performance using DP steels for crash components designed for crash energy absorption and/or intrusion resistance applications, the cross sections need to be optimized. Correlated crush simulation models were employed for the cross-section study. The models were developed using non-linear finite element code LS-DYNA and correlated to dynamic and quasi-static axial and bending crush tests on hexagonal and octagonal cross-sections made of DP590 steel. Several design concepts were proposed, the axial and bending crush performance in DP780 and DP980 were compared, and the potential mass savings were discussed.

The frame rail has an impact on the crash performance of body-on-frame (BOF) and uni-body vehicles. Recent developments in materials and forming technology have prompted research into improving the energy absorption and deformation mode of the frame rail design. It is worthwhile from a timing and cost standpoint to predict the behavior of the front rail in a crash situation through finite element techniques. This study focuses on improving the correlation of the frame component Finite Element model to physical test data through sensitivity analysis. The first part of the study concentrated on predicting and improving the performance of the front rail in a frontal crash [1]. However, frame rails in an offset crash or side crash undergo a large amount of bending. This paper discusses appropriate modeling and testing procedures for front rails in a bending situation.

The dynamic response of a front lower control arm (LCA) is very important in crash safety. In the event of a crash, the deformation of the LCA affects the frame rail's ability to crush and absorb energy on impact. Therefore, the deformation and rupture of the LCA during a crash may indirectly influence the deceleration pulse which is needed for safety sensor calibration of airbag deployment [1]. Depending on compliance, bushings have a significant effect on the deformation and rupture of the LCA. During a high speed impact test, the bushings allow the LCA to rotate at the joints or points where the LCA connects to the frame. The development of new LCA and bushing designs, constructed of different materials and geometries, require a standard test to measure their performance. The overall goal of this study was to develop a standardized procedure to test the stiffness, deformation, and strength of LCA bushings.

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.

The front rail plays a very important role in vehicle crash. Trigger holes are commonly used to control frame crush mode due to their simple manufacturing process and flexibility for late changes in the product development phase. Therefore, a study, including CAE and testing, was conducted on a production front rail to understand the effects of trigger hole shape, size and orientation. The trigger hole location in the front rail also affects crash performance. Therefore, the effect of trigger hole location on front rail crash behavior was studied, and understanding these effects is the main objective of this study. A tapered front rail produced from 1.7 mm thick DP600 steel was used for the trigger hole location investigation. Front rails with different trigger spacing and sizes were tested using VIA sled test facility and the crash progress was simulated using a commercial code RADIOSS. The strain rate, welding and forming effects were incorporated in the front rail modeling.

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 front rail plays an important role in the performance of body-on-frame (BOF) vehicles in frontal crashes. New developments in materials and forming technology have led to the exploration of different configurations to improve crash performance. This paper presents the initial stages of an ongoing study to investigate the effects of the cross section of steel columns on crash performance in automotive applications. Because accurate prediction of the performance of these rails can help reduce the amount of physical crash testing necessary, the focus of this paper is on appropriate testing and modeling procedures for different rail configurations. In the first part of this paper, the Finite Element Analysis (FEA) methodology is presented with respect to correlation with real world tests. The effects of various parameters are described, along with the optimum configuration for model correlation.

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.

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.

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.