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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.

An eight-group taxonomy was created to classify real-world frontal crashes from the Crashworthiness Data System (CDS) component of the National Automotive Sampling System (NASS). Three steps were taken to develop the taxonomy: (1) frontal-impact towaway crashes were identified by examining 1985-2005 model year light passenger vehicles with Collision Deformation Classification (CDC) data from the 1995-2005 calendar years of NASS; (2) case reviews, engineering judgments, and categorization assessments were conducted on these data to produce the eight-group taxonomy; and (3) two subsets of the NASS dataset were analyzed to assess the consistency of the resulting taxonomic-group frequencies. “Full-engagement” and “Offset” crashes were the most frequent crash types, each contributing approximately 33% to the total. The group identified as “D, Y, Z No-Rail” was the most over-represented crash type for vehicles with at least one seriously-injured occupant.

This study demonstrates the use of pressure sensing technology to predict the crash severity of frontal impacts. It presents an investigation of the pressure change in the front structural elements (bumper, crush cans, rails) during crash events. A series of subsystem tests were conducted in the laboratory that represent a typical frontal crash development series and provided empirical data to support the analysis of the concept. The pressure signal energy at different sensor mounting locations was studied and design concepts were developed for amplifying the pressure signal. In addition, a pressure signal processing methodology was developed that relies on the analysis of the air flow behavior by normalizing and integrating the pressure changes. The processed signal from the pressure sensor is combined with the restraint control module (RCM) signals to define the crash severity, discriminate between the frontal crash modes and deploy the required restraint devices.

Described in this paper is a component test methodology to evaluate the door trim armrest performance in an Insurance Institute for Highway Safety (IIHS) side impact test and to predict the SID-IIs abdomen injury metrics (rib deflection, deflection rate and V*C). The test methodology consisted of a sub-assembly of two SID-IIs abdomen ribs with spine box, mounted on a linear bearing and allowed to translate in the direction of impact. The spine box with the assembly of two abdominal ribs was rigidly attached to the sliding test fixture, and is stationary at the start of the test. The door trim armrest was mounted on the impactor, which was prescribed the door velocity profile obtained from full-vehicle test. The location and orientation of the armrest relative to the dummy abdomen ribs was maintained the same as in the full-vehicle test.

This paper describes the development of a three-dimensional lumped-mass structure and dummy model to study barrier-to-car side impacts. The test procedures utilized to develop model input data are also described. The model results are compared to crash test results from a series of six barrier-to-car crash tests. Sensitivity analysis using the validated model show the necessity to account for dynamic structural rate effects when using quasi-statically measured vehicle crush data.

This study deals with passenger side restraint system design for frontal impact and four impact modes are considered in optimization. The objective is to minimize the Relative Risk Score (RRS), defined by the National Highway Traffic Safety Administration (NTHSA)'s New Car Assessment Program (NCAP). At the same time, the design should satisfy various injury criteria including HIC, chest deflection/acceleration, neck tension/compression, etc., which ensures the vehicle meeting or exceeding all Federal Motor Vehicle Safety Standard (FMVSS) No. 208 requirements. The design variables include airbag firing time, airbag vent size, inflator power level, retractor force level. Some of the restraint feature options (e.g., some specific features on/off) are also considered as discrete design variables. Considering the local variability of input variables such as manufacturing tolerances, the robustness and reliability of nominal designs were also taken into account in optimization process.

Optimization of heavy materials like steel, in order to create a lighter vehicle, it is a major goal among most automakers, since heavy vehicles simply cannot compete with a lightweight model's fuel economy. Thinking this way, this paper shows a case study where the Size Optimization technique is applied to a front floor reinforcement. The reinforcement is used by two different vehicles, a subcompact and a crossover Sport Utility Vehicle (SUV), increasing the problem complexity. The Size Optimization technique is supported by Finite Element Method (FEM) tools. FEM in Computer Aided Engineering (CAE) is a numerical method for solving engineering problems, and its use can help to optimize prototype utilization and physical testing.

In this study, the occupant containment characteristics of automotive laminated safety glass in side window applications was evaluated through two full-scale, full-vehicle dolly rollover crash tests. The dolly rollover crash tests were performed on sport utility vehicles equipped with heat-strengthened laminated safety glass in the side windows in order to: (1) evaluate the capacity of laminated side window safety glass to contain unrestrained occupants during rollover, (2) analyze the kinematics associated with unrestrained occupants during glazing interaction and ejection, and (3) to identify laminated side window safety glass failure modes. Dolly rollovers were performed on a 1998 Ford Expedition and a 2004 Volvo XC90 at a nominal speed of 43 mph, with unbelted Hybrid II Anthropomorphic Test Devices (ATDs) positioned in the outboard seating positions.

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 attempts to integrate a derivative-free probability analysis method to Reliability-Based Robust Design Optimization (RBRDO). The Eigenvector Dimension Reduction (EDR) method is used for the probability analysis method. It has been demonstrated that the EDR method is more accurate and efficient than the Second-Order Reliability Method (SORM) for reliability and quality assessment. Moreover, it can simultaneously evaluate both reliability and quality without any extra expense. Two practical engineering problems (vehicle side impact and layered bonding plates) are used to demonstrate the effectiveness of the EDR method.

This study applies a detailed finite element model of the human body to simulate occupant knee impacts experienced in vehicular frontal crashes. The human body model includes detailed anatomical features of the head, neck, chest, thoracic and lumbar spine, abdomen, and lower and upper extremities. The material properties used in the model for each anatomic part of the human body were obtained from test data reported in the literature. The total human body model used in the current study has been previously validated in frontal and side impacts. Several cadaver knee impact tests representing occupants in a frontal impact condition were simulated using the previously validated human body model. Model impact responses in terms of force-time and acceleration-time histories were compared with test results. In addition, stress distributions of the patella, femur, and pelvis were reported for the simulated test conditions.

Various frontal impact risk curves were assessed for the next-generation USA New Car Assessment Program (NCAP). Specifically, the “NCAP risk curves” — those chosen by the government for the 2011 model year NCAP — as well as other published risk curves were used to estimate theoretically the injury rates of belted drivers in real-world frontal crashes. Two perspectives were considered: (1) a “point” estimate of NCAP-type events from NCAP fleet tests, and (2) an “aggregate” estimate of 0 ≤ ΔV ≤ 56 km/h crashes from a modeled theoretical vehicle whose NCAP performance approximated the average of the studied fleet. Four body regions were considered: head, neck, chest, and knee-thigh-hip complex (KTH). The curve-based injury rates for each body region were compared with those of real-world frontal crashes involving properly-belted adult drivers in airbag-equipped light passenger vehicles. The assessment yielded mixed results.

Structure enhancement based on data monitored in a traditional side impact evaluation is primarily a trial and error exercise resulting in a large number of computer runs. This is because how the structure gets loaded and the degree of contribution of local structural components to resist the impact while absorbing energy during a side collision is not completely known. Developing real time complete load profiles on a body side during the time span of an impact is not an easy task and these loads cannot be calculated from that calculated at the barrier mounting plate. This paper highlights the load distribution, calculated by a procedure using computer aided engineering (CAE) tools, on a typical 2-door vehicle body side when struck by moving deformable barriers used in the insurance institute for highway safety (IIHS), EuroNCAP and LINCAP side impact evaluations.

The front end module technology is a system developed to make the interface with vehicle body in accordance with costumer requirements. This modular system also has characteristics to reinforce the structure (chassis, main rails, shotguns), respecting its robustness (tolerances of the body) in accordance with NVH performance. The decision of having a FEM design made by steel and plastic was taken due to NVH specification, impact and safety requirements. Other items were either considered such as: fixation on the body of the vehicle, constraints between bumper beam and engine cooling module. Simulation tools including: durability test (static and dynamic) and modal F.E.A analysis, CAE system, crash test performance, aerodynamics required to insure results desired results.

The engine subframe (cradle) is an important contributor to crash energy management in frontal impact for automotive vehicles. Subframe design can enhance vehicle crash performance through energy management. In addition to energy management targets, the subframe must meet stiffness, durability and other vehicle engineering requirements. Various subframe concepts are reviewed. Their design intents and vehicle performance are discussed. A development process of an aluminum subframe is then presented which details the subframe design as an energy absorbing component for frontal impacts. The architecture of the subframe is developed based on overall functionality requirements and package constraints. The geometry of the subframe is first designed to accommodate engine mounts and suspension support locations. The subframe member's shape, orientation, and location are then refined to accommodate the subframe-to-body connection requirements.

This paper reports a study on the use of response inverse filtering (RIF) methodology for crash pulse prediction. RIF is based on the finite impulse response (FIR) and inverse filtering (IF) methods. The FIR coefficients obtained by the digital convolution theory and the least squared error approach serve to transfer response from the input (impacting or excitation) side to the output (non-impacting or receiving) side. The FIR method, a process of low pass filtering (e.g. truck body mount), is commonly used in predicting the non-impacting side (e.g. truck body or cab) response with the input excitation in the impacting side (e.g. truck frame). The accuracy in the validation and prediction via FIR transfer function depends on the frequency contents of the input and output accelerometer data from which the transfer function is developed. The prediction accuracy is low if the output data contain higher frequency components than the input.

Ford designed and built a midsize family sedan for the PNGV (Partnership for a New Generation of Vehicle). The side impact performance of the aluminum vehicle and the current CAE capability was studied. The vehicle was tested according to the specifications of FMVSS 214. The results show the vehicle meet the federal safety requirements. The impact performances of the front and rear dummies were comparable to those of the steel counterpart. CAE analysis was conducted to develop the body component design and to predict the structural and dummy responses. The results show that without modeling of the joint (rivet and weld) separation, the accuracy of the CAE crash analysis for this aluminum vehicle was inadequate. When empirical separation criteria were incorporated to model the joint, analysis results correlated with the test. Further development of robust modeling methods for joint separation is needed to improve the prediction of aluminum structure crash responses.

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.

A theoretical, mathematical, risk assessment procedure was developed to estimate the fraction of drivers that incurred head and thoracic AIS3+ injuries in full-engagement frontal crashes. The estimates were based on numerical simulations of various real-world events, including variations of crash severity, crash speed, level of restraint, and occupant size. The procedure consisted of four steps: (1) conduct the simulations of the numerous events, (2) use biomechanical equations to transform the occupant responses into AIS3+ risks for each event, (3) weight the maximum risk for each event by its real-world event frequency, and (4) sum the weighted risks. To validate the risk assessment procedure, numerous steps were taken. First, a passenger car was identified to represent average field performance.