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Vehicle restraint system design is a difficult optimization problem to solve because (1) the nature of the problem is highly nonlinear, non-convex, noisy, and discontinuous; (2) there are large numbers of discrete and continuous design variables; (3) a design has to meet safety performance requirements for multiple crash modes simultaneously, hence there are a large number of design constraints. Based on the above knowledge of the problem, it is understandable why design of experiment (DOE) does not produce a high-percentage of feasible solutions, and it is difficult for response surface methods (RSM) to capture the true landscape of the problem. Furthermore, in order to keep the restraint system more robust, the complexity of restraint system content needs to be minimized in addition to minimizing the relative risk score to achieve New Car Assessment Program (NCAP) 5-star rating.

Transfer or response equations are important as they provide relationships between the responses of different surrogates under matched, or nearly identical loading conditions. In the present study, transfer equations for different body regions were developed via mathematical modeling. Specifically, validated finite element models of the age-dependent Ford human body models (FHBM) and the mid-sized male Hybrid III (HIII50) were used to generate a set of matched cases (i.e., 192 frontal sled impact cases involving different restraints, impact speeds, severities, and FHBM age). For each impact, two restraint systems were evaluated: a standard three-point belt with and without a single-stage inflator airbag. Regression analyses were subsequently performed on the resulting FHBM- and HIII50-based responses. This approach was used to develop transfer equations for seven body regions: the head, neck, chest, pelvis, femur, tibia, and foot.

The primary function of an airbag control module is to detect crashes, discriminate and predict if a deployment is necessary, then deploy the restraint systems including airbags and where applicable, pretensioners. At General Motors (GM), the internal term for airbag control module is Sensing and Diagnostic Module (SDM). In the 1994 model year, GM introduced its SDM on some of its North American airbag-equipped vehicles. A secondary function of that SDM and all subsequent SDMs is to record crash related data. This data can include data regarding impact severity from internal accelerometers and pre-crash vehicle data from various chassis and powertrain modules. Previous researchers have addressed the accuracy of both the velocity change data, recorded by the SDM, and the pre-crash data, but the assessment of the timing of the pre-crash data has been limited to a single family of modules (Delphi SDM-G).

Design optimization techniques are widely used to drive designs toward a global or a near global optimal solution. However, the achieved optimal solution often appears to be the only choice that an engineer/designer can select as the final design. This is caused by either problem topology or by the nature of optimization algorithms to converge quickly in local/global optimal or both. Problem topology can be unimodal or multimodal with many local and/or global optimal solutions. For multimodal problems, most global algorithms tend to exploit the global optimal solution quickly but at the same time leaving the engineer with only one choice of design. The paper explores the application of genetic algorithms (GA), simulated annealing (SA), and mixed integer problem sequential quadratic programming (MIPSQP) to find multiple local and global solutions using single objective optimization formulation.

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.

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.

The development of combined finite element and spring / rigid mass crash simulation dummy models for automotive applications is described. In order to better understand the crash phenomena and occupant kinematics during vehicle crashes, recent developments have been focused on the use of finite element techniques in the simulation of both structure and structure / dummy interactions. The combination of spring /rigid mass modeling and finite element technique is used to develop models of fiftieth percentile Hybrid III and Side impact Dummies in a finite element program (RADIOSS). In general, the dummies are modeled with rigid masses and joints with techniques similar to those used in Crash Victim Simulation programs like MADYMO and CAL3D. Only selected components, like the Hybrid III dummy thorax and the SID pelvis and jacket, are modeled with finite element shell and brick elements to improve dummy / restraint system and dummy / structure interaction responses.

Side impact accounts for a significant source of societal harm, injury and death. To address this issue, Europe and US have introduced legislation to be met for the new vehicle certification. In an effort to meet these regulations and the market demand for safety, Automotive manufacturers have significantly improved vehicle side structure integrity and introduced side impact airbags are for added protection. Today, passenger vans, light truck and sport-utility type vehicles are all popular consumer choices in the US. These vehicles differ significantly from passenger cars in many respects and as such need special design considerations for side airbags. Here, MADYMO-3D model of a generic passenger van / Sport-Utility type vehicle is created and correlated to FMVSS-214 side impact crash test. This model is used to evaluate both door and seat mounted side airbag designs in different orientations at standard test impact condition and at a higher speed.

Vehicle sound quality has become lately one of the main topics of study in the automotive industry which is associated with the quality of the product. Into the automotive development the static operational sound quality is one of the attributes that is considered. The sounds produced through the manipulation of items like the doors and interior components (windows, seats, safety belts, windshield wipers, and others) generated for safety and warning purposes are items related to the vehicle quality for customers. Those sounds based on relative level of intensity, duration, harmony and degree of contribution are elements that the customer will retain in mind, an overall quality impression. The sound produced during gear lever manipulation is important to the customer in order that the event should transmit low intensity and robust and soft impression.

Seat belts are the primary occupant-protection devices for vehicle crashes. Field statistics show that proper usage of seat belts substantially contributes to decreases in the fatality rate and injury level. To collect first-hand information regarding seat belt comfort and usability, a questionnaire survey was conducted. The most significant problems were found as belt trapping in the door, awkward negotiating with clothes, belt twisting, belt locking up, and difficulty to locate the buckle. The survey results indicated that drivers who are over 40 years old have more complaints than younger drivers. When the driver's age increases to 55 and above, belt pulling force and inappropriate and loose fitting of the belt on the body become major issues. Female drivers have more complaints than male drivers. Short statured drivers need both hands to pull and guide the retracting of the belt.

In the 1999 Supplemental Notice for Proposed Rulemaking (SNPRM) for Advanced Airbags, the National Highway Traffic Safety Administration (NHTSA) sought comments on the maximum speed at which the high-speed, unbelted occupant test suite will be conducted, i.e., 48 kph vs. 40 kph. To help address this question, an analysis of constraints was performed via extensive mathematical modeling of a theoretical restraint system. First, math models (correlated with several existing physical tests) were used to predict the occupant responses associated with 336 different theoretical dual-stage driver airbag designs subjected to six specific Regulated and non-Regulated tests.

Accurate prediction of the responses from the anthropomorphic test devices (ATDs) in vehicle crash tests is critical to achieving better vehicle occupant performances. In recent years, automakers have used finite element (FE) models of the ATDs in computer simulations to obtain early assessments of occupant safety, and to aid in the development of occupant restraint systems. However, vehicle crash test results have variation, sometimes significant. This presents a challenge to assessing the accuracy of the ATD FE models, let alone improving them. To resolve this issue, it is important to understand the test variation and carefully select the target data for model improvement. This paper presents the work carried out by General Motors and Humanetics Innovative Solutions (formerly FTSS) in a joint project, aimed at improving the FE model of the Hybrid III-50 ATD (HIII-50) v5.1.

There is a growing interest in using pressure sensors to sense side impacts, where the pressure change inside the door cavity is monitored and used to discriminate trigger and non-trigger incidents. In this paper, a pressure sensor simulation capability for side impact sensing calibration is presented. The ability to use simulations for side impact sensing calibration early in the vehicle program development process could reduce vehicle development cost and time. It could also help in evaluating sensor locations by studying the effects of targeted impact points and contents in the door cavity. There are two modeling methods available in LS-DYNA for predicting pressure change inside a cavity, namely airbag method and fluid structure interaction method. A suite of side impact calibration events of a study vehicle were simulated using these two methods. The simulated door cavity pressure time histories were then extracted to calibrate the side sensing system of the study vehicle.

Headliner material plays an important role in occupant protection in situations involving head impact into the interior vehicle roof area. Accurate characterization of its mechanical properties is therefore extremely important for prediction of its behavior during interior impact assessment of a vehicle. Headliner material typically consists of two main layers: the substrate layer which provides structural integrity and impact protection, and the fabric-foam layer which provides proper interior fit and appearance. Both layers vary significantly in thickness and composition between different manufacturers. This paper investigates effects of the layer thickness on compressive strength and deformation of several different headliner materials.

General Motors (GM), OnStar and the University of Michigan International Center for Automotive Medicine (ICAM) have formed a partnership to investigate and analyze real world rollover crashes involving GM vehicles equipped with rollover sensing technology and rollover-capable roof rail airbag systems. Candidates for the study are initially identified by OnStar, who receive notification of a rollover crash through the vehicle's Automatic Crash Response system. If the customer agrees to participate in the study, medical, vehicle and crash scene information are quickly gathered. This information is then reviewed by the medical and GM engineering communities to provide field relevant learning on injury mechanisms and vehicle system performance in rollover events. This paper provides a detailed review of the field case studies collected to date.

This paper reports on a study that examines the effect of shoulder belt load limiters and pretensioners as well as crash and occupant factors that influence upper torso harm in real-world frontal crashes. Cases from the University of Michigan International Center for Automotive Medicine (ICAM) database were analyzed. Additional information was used from other databases including the National Highway Traffic Safety Administration (NHTSA) New Car Assessment Program (NCAP), the Insurance Institute for Highway Safety (IIHS), the National Automotive Sampling System - Crashworthiness Data System (NASS-CDS), and patient data available from the University of Michigan Trauma Center. The ICAM database is comprised of information from real-world crashes in which occupants were seriously injured and required treatment at a Level 1 Trauma Center.

The sensor mounted on the door impact beam plays a major role in side impact events. The accelerations of side impact sensors are processed by sensing algorithms to make a decision on the air bag deployment. The sensing signal criterion for the deployable condition is a well understood process. However, the non-deployment sensing signal for the immunity to abuse conditions is a function of sensor attachment stiffness to the base structure. The base structure can be a door inner panel or door impact beam. In one of the production program, the acceleration based sensor attached to the impact beam showed immunity issues in the abusive door slams/opening to objects. Hence, the computer Aided Engineering (CAE) analysis was used to develop the sensor attachment criterion.

The potential effects of passenger air bag deployment on an out-of-position three-year-old child dummy and on a three-year-old child dummy in a child restraint system are two of the items considered in the development of an automotive passenger air bag restraint system. In order to increase our understanding of the passenger air bag and three-year-old dummy interaction, we have developed a three dimensional computer model of a three-year-old child dummy. The dummy model has a compressible sternum and a multi-segment representation of the neck which helps improve the predictive capabilities of the neck criteria. The dummy properties needed as inputs to the occupant model were all experimentally determined. An energy technique was used for separating the elastic, damping and friction components for each joint. Three HYGE sled test cases were simulated to validate the model.