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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.

Motivated by a combination of increasing consumer demand for fuel efficient vehicles, more stringent greenhouse gas, and anticipated future Corporate Average Fuel Economy (CAFE) standards, automotive manufacturers are working to innovate in all areas of vehicle design to improve fuel efficiency. In addition to improving aerodynamics, enhancing internal combustion engines and transmission technologies, and developing alternative fuel vehicles, reducing vehicle weight by using lighter materials and/or higher strength materials has been identified as one of the strategies in future vehicle development. Weight reduction in vehicle components, subsystems and systems not only reduces the energy needed to overcome inertia forces but also triggers additional mass reduction elsewhere and enables mass reduction in full vehicle levels.

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.

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.

Advanced High Strength Steels (AHSS) have been implemented in the automotive industry to balance the requirements for vehicle crash safety, emissions, and fuel economy. With lower ductility compared to conventional steels, the fracture behavior of AHSS components has to be considered in vehicle crash simulations to achieve a reliable crashworthiness prediction. Without considering the fracture behavior, component fracture cannot be predicted and subsequently the crash energy absorbed by the fractured component can be over-estimated. In full vehicle simulations, failure to predict component fracture sometimes leads to less predicted intrusion. In this paper, the feasibility of using computer simulations in predicting fracture during crash deformation is studied.

The forming effects along with strain rate, actual material properties and weld effects have been found to be very critical for accurate prediction of crash responses especially the prediction of local deformation. As a result, crash safety engineers started to consider these factors in crash models to improve the accuracy of CAE prediction and reduce prototype testing. The techniques needed to incorporate forming simulation results, including thickness change, residual stresses and strains, in crash models have been studied extensively and are well known in automotive CAE community. However, a challenge constantly faced by crash safety engineers is the availability of forming simulation results, which are usually supplied by groups conducting forming simulations. The forming simulation results can be obtained by either using incremental codes with actual stamping processes or one-step codes with final product information as a simplified approach.

Hydroformed components are replacing stamped parts in automotive frames and front end and roof structures to improve the crash performance of vehicles. Due to the increasing application of hydroformed components, a better understanding of the crash behavior of these parts is necessary to improve the correlation between full-vehicle crash tests and FEM analysis. Accurately predicting the performance of hydroformed components will reduce the amount of physical crash testing necessary to develop the new components and new vehicles as well as reduce cycle time. Virgin material properties are commonly used in FEM analysis of hydroformed components, which leads to erroneous prediction of the full-vehicle crash response. Changes in gauge and material properties during the hydroforming process are intuitive and can be reasonably predicted by using forming simulations. The effects of the forming process have been investigated in the FEA models that are created for crash analyses.

There are a wide variety of approaches to model the automotive seat and occupant interaction. This paper traces the studies conducted for simulating the occupant to seat interaction in frontal and/or rear crash events. Starting with an initial MADYMO model, a MADYMO-LS/DYNA coupled model was developed. Subsequently, a full Finite Element Analysis model using LS/DYNA was studied. The main objective of the studies was to improve the accuracy and efficiency of CAE models for predicting the dummy kinematics and structural deformations at the restraint attachment locations in laboratory tests. The occupant and seat interaction was identified as one of the important factors that needed to be accurately simulated. Quasi-static and dynamic component tests were conducted to obtain the foam properties that were input into the model. Foam specimens and the test setup are discussed. Different material models in LS/DYNA were evaluated for simulating automotive seat foam.

Macroscopic constitutive behaviors of aluminum 5052-H38 honeycombs under dynamic inclined loads with respect to the out-of-plane direction are investigated by experiments. The results of the dynamic crush tests indicate that as the impact velocity increases, the normal crush strength increases and the shear strength remains nearly the same for a fixed ratio of the normal to shear displacement rate. The experimental results suggest that the macroscopic yield surface of the honeycomb specimens as a function of the impact velocity under the given dynamic inclined loads is not governed by the isotropic hardening rule of the classical plasticity theory. As the impact velocity increases, the shape of the macroscopic yield surface changes, or more specifically, the curvature of the yield surface increases near the pure compression state.

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 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.

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.

The conversion between cast aluminum lower control arms (LCAs) and stamped steel LCAs has prompted the need for new LCA designs to achieve parallel levels of performance. Component tests procedures and CAE modeling methodologies need to be utilized to assess future LCA designs across a variety of vehicle lines to meet or exceed performance criteria. Therefore the overall goal of this study was to develop a standardized test procedure to test the stiffness, deformation and strength of LCAs. In addition, CAE modeling methodologies to better model LCAs will be developed. The test procedures and CAE modeling methodologies would then be used to set performance targets for future LCA designs. To standardize the LCA test procedure, component test fixtures were developed in this work. The objective of the fixtures is to test LCAs with similar boundary conditions they would experience in vehicle crash. Three different test modes are examined in this project.

Finite element models of cast aluminum and stamped steel lower control arms (LCAs) were created to simulate subsystem tests of LCA with bushings and brackets. Several modeling methods were used to simulate the dynamic responses of cast aluminum LCAs, and the advantages and disadvantages of each method are discussed. Factors that are essential for modeling stamped steel components found in previous studies [1, 2] including strain rate, forming, and welding effects are incorporated in the stamped steel LCA models. Difficulties in modeling LCAs subsystem, possible remedies, and further improvements are also discussed in this paper.

Advanced High Strength Steels (AHSS) along with innovative design and manufacturing processes are effective ways to improve crash energy management. Crash trigger hole is another technology which can been used on front rails for controlling crash buckling mode, avoiding crash mode instability and minimizing variations in crash mode due to imperfections in materials, part geometry, manufacturing, and assembly processes etc. In this study, prototyped crash columns with different trigger hole shapes, sizes and locations were physically tested in frontal crash impact tests. A corresponding crash computer simulation model was then created to perform the correlation study. The testing data, such as crash force-displacement curves and dynamic crash modes, were used to verify the FEA crash model and to study the trigger sensitivity and effects on front rail crash performance.

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.

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.