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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.

The ability to extract and evaluate the time history of structural deformations or crush during vehicle crashes represents a significant challenge to automotive safety researchers. Current methods are limited by the use of electro-mechanical devices such as string pots and/or linear variable displacement transducers (LVDT). Typically, one end of the transducer must be mounted to a point on the structure that will remain un-deformed during the event; the other end is then attached to the point on the structure where the deformation is to be measured. This approach measures the change in distance between these two points and is unable to resolve any movement into its respective X, Y, or Z directions. Also, the accuracy of electro-mechanical transducers is limited by their dynamic response to crash conditions. The photogrammetry technique has been used successfully in a wide variety of applications including aerial surveying, civil engineering and documentation of traffic accidents.

In order to provide an improved countermeasure for occupant protection, a new type of active reversible preload seatbelt (ARPS) is presented in this paper. The ARPS is capable of protecting occupants by reducing injuries during frontal collisions. ARPS retracts seatbelt webbing by activating an electric motor attached to the seatbelt retractor. FCW (Forward Collision Warning) and LDW (Lane Departure Warning) provide signals as a trigger to activate the electric motor to retract the seatbelt webbing, thus making the occupant restraint system work more effectively in a crash. It also helps reduce occupant’s forward movement during impact process via braking. Four important factors such as preload force, preload velocity and the length and timing of webbing retraction play influential roles in performance of the ARPS. This paper focuses on studying preload performance of ARPS under various test conditions to investigate effects of the aforementioned factors.

Many foams exhibit significant strain rate dependency in their mechanical responses. To characterize these foams, a strain rate dependent constitutive model is formulated and implemented in an explicit dynamic finite element code developed at FORD. The constitutive model is developed in conjunction with a Lagrangian eight node solid element with twenty four degrees of freedom. The constitutive model has been used to model foams in a number crash analysis problems. Results obtained from the analyses are compared to the experimental data. Evidently, numerical results show excellent agreement with the experimental data.

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.

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.

Multidisciplinary Design Optimization (MDO) is of great significance in the lean design of vehicles. The present work is concerned with the objective of cross-functional optimization (i.e. MDO) of automotive body. For simplicity, the main goal adopted here is minimizing the weight of the body meeting NVH and crash safety targets. The stated goal can be achieved following either of two different ways: classic response surface method (RSM) and practical MDO methodology espoused recently. Even though RSM seems to be able to find a design point which satisfies the constraints, the problem is with the time associated with running such CAE algorithms that can provide a single optimal solution for multi-disciplinary areas such as NVH and crash safety.

This paper presents a method to obtain improved foam/pillar structural designs to help enhance occupant interior impact protection. Energy absorbing foams are used in this study with their thickness and crush strength being selected as primary design variables for optimization. The response surface techniques in the design of experiment are used in the optimization process. Head impact analyses are conducted by a CAE model with explicit, nonlinear, dynamic finite element code LS-DYNA3D. A baseline model is developed and verified by comparing the simulation results with the experimental data. Based on this model, the anticipated effects of stiffness of the pillar structure and the trim on the Head Injury Criterion (HIC) results are also assessed. The optimization approach in this study provides a comprehensive consideration of the factors which affect the HIC value.

This paper represents the development of a new design methodology based on data mining theory for decision making in vehicle crashworthy components (or parts) development. The new methodology allows exploring the big crash simulation dataset to discover the underlying complicated relationships between vehicle crash responses and design variables at multi-levels, and deriving design rules based on the whole vehicle safety requirements to make decisions towards the component and sub-component level design. The method to be developed will resolve the issue of existing design approaches for vehicle crashworthiness, i.e. limited information exploring capability from big datasets, which may hamper the decision making and lead to a nonoptimal design. A preliminary design case study is presented to demonstrate the performance of the new method. This method will have direct impacts on improving vehicle safety design and can readily be applied to other complex systems.

A mathematical model of an airbag restraint system for automobile drivers, including the simulation of the simultaneous collapse of the steering column, has been developed. The model is designed to work in conjunction with a three-dimensional occupant model. It is capable of assessing the relative effects of airbag size, pressure, deployment rate, venting area, contact force, steering column collapse force, and column collapse distance. The results of the model are compared with experimental runs in which anthropometric dummies were used as test subjects. Good correlation was obtained for torso kinematics. The model can be conveniently used for a parametric study to aid the design of airbag restraint systems.

The present work is concerned with the objective of design optimization of an automotive front end structure meeting both occupant and pedestrian safety requirements. The main goal adopted here is minimizing the mass of the front end structure meeting the safety requirements without sacrificing the performance targets. The front end structure should be sufficiently stiff to protect the occupant by absorbing the impact energy generated during a high speed frontal collision and at the same time it should not induce unduly high impact loads during a low speed pedestrian collision. These two requirements are potentially in conflict with each other; however, there may exist an optimum design solution, in terms of mass of front end structure, that meets both the requirements.

Head Impact Criterion (HIC) numbers obtained from interior head impact testing with the NHTSA-designed Free Motion Headform (FMH) are influenced by many variables. The high level of variability experienced in the NHTSA-proposed Interior Head Impact Test presents a challenge to today's automotive engineers. Primary contributors to HIC variability include (1) impact speed, (2) headform calibration performance, (3) design/build part variation, and (4) target point impact accuracy. This study shows that controlling these variables during testing can improve test data repeatability and reproducibility, as well as reduce design and testing time.

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.

Upper interior head impact safety is an important consideration in vehicle design and is covered under FMVSS 201. This standard generally requires that HIC(d) should not exceed 1000 when a legitimate target in the upper interior of a vehicle is impacted with a featureless Hybrid III headform at a velocity of 15 mph (6.7 m/s). As HIC and therefore HIC(d) is based on translational deceleration experienced at the CG of a test headform, its applicability is often doubted in protection against injury that can be caused due to rotational acceleration of head during impact. A study is carried out here using an improved lumped parameter model (LPM) representing headform impact for cases in which moderate to significant headform rotation may be present primarily due to the geometric configuration of targets.

The seatbelt system, originally designed for protecting occupants in frontal crashes, has been reported to be inadequate for preventing occupant head-to-roof contact during rollover crashes. To improve the effectiveness of seatbelt systems in rollovers, in this study, we reviewed previous literature and proposed vertical head excursion corridors during static inversion and dynamic rolling tests for human and Hybrid III dummy. Finite element models of a human and a dummy were integrated with restraint system models and validated against the proposed test corridors. Simulations were then conducted to investigate the effects of varying design factors for a three-point seatbelt on vertical head excursions of the occupant during rollovers. It was found that there were two contributing parts of vertical head excursions during dynamic rolling conditions.

Characterization of materials used in the automotive industry is often done via component testing. A strict regimen of tests is conducted on a component to determine material parameters for numerical simulations of more complicated loading conditions. Separation of material constants and geometrically- or experimentallyinduced effects is difficult with this method of characterization. Well-controlled experiments that determine the material response in basic deformations allow material properties to be determined. In this paper low strain rate and high strain rate experimental responses of dummy skin material (i.e. plasticized polyvinyl chloride) are presented. Details of the experimental procedures used to acquire the data are also included. In addition, a rate-dependent constitutive model for the plasticized material is developed, and its simulated results are compared with low strain rate results.

Numerical models of Hybrid III had been widely used to study the effect of underbody blast loading on lower extremities. These models had been primarily validated for automotive loading conditions of shorter magnitude in longer time span which are different than typical blast loading conditions of higher magnitude of shorter duration. Therefore, additional strain rate dependent material models were used to validate lower extremity of LSTC Hybrid III model for such loading conditions. Current study focuses on analyzing the mitigating effect of combat boots in injury responses with the help of validated LSTC Hybrid III model. Numerical simulations were run for various impactor speeds using validated LSTC Hybrid III model without any boot (bare foot) and with combat boot.

A number of studies, using data from NASS-CDS, have shown a large percentage of rollover crashes can be classified as tripped events. In many cases, the requirements for a tripped rollover detection algorithm are driven by the timely activation of an occupant containment device. To meet these requirements rollover detection algorithms have been developed by utilizing vehicle roll rate, lateral and vertical accelerations data collected primarily from laboratory tests. This study identifies and examines several challenges associated with developing a rollover detection algorithm with enhanced capabilities. Enhancement of the detection algorithm is explored by considering additional vehicle responses: forward velocity and sideslip angle. With the additional signals, discrimination of rollover crashes from other crash modes is discussed. Potential field/laboratory test modes are proposed to generate the additional vehicle signals.

As early as the 1950's, Gurdjian and colleagues (Gurdjian et al., 1955) observed that brain injuries could occur by direct pressure loading without any global head accelerations. This pressure-induced injury mechanism was "forgotten" for some time and is being rekindled due to the many mild traumatic brain injuries attributed to blast overpressure. The aim of the current study was to develop a finite element (FE) model to predict the biomechanical response of rat brain under a shock tube environment. The rat head model, including more than 530,000 hexahedral elements with a typical element size of 100 to 300 microns was developed based on a previous rat brain model for simulating a blunt controlled cortical impact. An FE model, which represents gas flow in a 0.305-m diameter shock tube, was formulated to provide input (incident) blast overpressures to the rat model. It used an Eulerian approach and the predicted pressures were verified with experimental data.

Numerical analyses frequently accompany experimental investigations that study injury biomechanics and improvements in automotive safety. Limited by computational speed, earlier mathematical models tended to simplify the system under study so that a set of differential equations could be written and solved. Advances in computing technology and analysis software have enabled the development of many sophisticated models that have the potential to provide a more comprehensive understanding of human impact response, injury mechanisms, and tolerance. In this article, 50 years of publications on numerical modeling published in the Stapp Car Crash Conference Proceedings and Journal were reviewed. These models were based on: (a) author-developed equations and software, (b) public and commercially available programs to solve rigid body dynamic models (such as MVMA2D, CAL3D or ATB, and MADYMO), and (c) finite element models.