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This paper will discuss the various tools used to measure the steering and suspension properties of a vehicle. Measuring the kinematic and compliance properties of the steering and suspension systems is an important part of the vehicle development process. Some of the ways these measurements are used include confirmation of vehicle design and build, to create and correlate CAE models, and for diagnosis of steering and handling concerns. Understanding exactly how the steering and suspension systems are performing is an important step in the development process. We have found that by employing the proper tools and methods, plus having a defined vehicle dynamics fingerprint process, that most issues and concerns can be successfully resolved.

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.

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.

Identification of the noise generating mechanisms of gravity action and vibrator stimulated sliding chutes has resulted in the development of practical and effective noise abatement treatments for both. In the case of gravity action chutes the application of foam-backed thin and narrow spring steel plates on the chute surface achieves the desired effect with noise reduction of 14 to 25 dB(A). With vibrator stimulated chutes progressive steps were taken to attenuate source noise, chute radiation noise and the non-productive component of the force vector from the vibrator, resulting in noise reduction of 25 to 30 dB(A).

Noise generating mechanisms connected with steel-blanking operation has been identified and their engineering treatments developed and tested. Use of rubber-metal laminates proved to be successful for cushioning impacts in kinematic pairs and joints. Use of plastic for the stripper plate construction was recommended. The “die stiffener” concept was developed to reduce main noise peak associated with punch breakthrough. Screening of the die cavity by a transparent curtain of overlapping PVC strips was shown to be effective. A pulse load simulator with adjustable load rate and amplitude has been developed to facilitate testing of presses.

Since statistics indicate that front impact is the major accident type, Ford has been studying energy-absorbing structures for some time. Early designs such as the “ball and tube” and “rail splitter” were discarded in favor of the “S” frame. Details of the design approach and testing are given in this paper. Design objectives were increased effective collapse distance, compatibility with production practices, and maintenance of satisfactory noise, vibration, and harshness levels. Safety objectives are improved passenger compartment integrity and reduction of seat belt loads. Barrier crash tests at 30 mph (equivalent to collision into standing vehicle at 50 mph) were used to evaluate the design of the “S” frame. Results of testing indicate that occupant restraint with seat belts, combined with front end structural improvements, offer the most promise for injury reduction during service front impact accidents.

Since their inception, the design of airbag sensing systems has continued to evolve. The evolution of air bag sensing system design has been rapid. Electromechanical sensors used in earlier front air bag applications have been replaced by multi-point electronic sensors used to discriminate collision mechanics for potential air bag deployment in front, side and rollover accidents. In addition to multipoint electronic sensors, advanced air bag systems incorporate a variety of state sensors such as seat belt use status, seat track location, and occupant size classification that are taken into consideration by air bag system algorithms and occupant protection deployment strategies. Electronic sensing systems have allowed for the advent of event data recorders (EDRs), which over the past decade, have provided increasingly more information related to air bag deployment events in the field.

The SAE Fuel Cell Vehicle (FCV) Safety Working Group has been addressing FCV safety for over 11 years. In the past couple of years, significant attention has been directed toward a revision to the standard for vehicular hydrogen systems, SAE J2579(1). In addition to streamlining test methodologies for verification of Compressed Hydrogen Storage Systems (CHSSs) as discussed last year,(2) the working group has been considering the effect of vehicle fires, with the major focus on a small or localized fire that could damage the container in the CHSS and allow a burst before the Pressure Relief Device (PRD) can activate and safely vent the compressed hydrogen stored from the container.

Analyses were performed to determine the sensitivity of stopping sight distance on vertical curves to driver eye height and other parameters entering into the stopping sight-distance equations. Sight distance was found to be relatively insensitive to eye height. On a given hill crest, sight distance for a driver whose eye height is 6-inches lower than the design eye height (3.75 ft) is only 5% less than the design sight distance. On the other hand, stopping distance is very sensitive to travel speed, pavement friction and reaction time. For example, a 1.8 mph decrease in speed reduces stopping distance by the same amount that a 6-inch decrease in eye height reduces sight distance. Also, sight distance is about 2.5 times more sensitive to obstacle height than eye height. It is argued that reductions in travel speed since the introduction of the 55-mph speed limit compensate for any recent or projected decreases in driver eye height.

Simulation based design optimization has become the common practice in automotive product development. Increasing computer models are developed to simulate various dynamic systems. Before applying these models for product development, model validation needs to be conducted to assess their validity. In model validation, for the purpose of obtaining results successfully, it is vital to select or develop appropriate metrics for specific applications. For dynamic systems, one of the key obstacles of model validation is that most of the responses are functional, such as time history curves. This calls for the development of a metric that can evaluate the differences in terms of phase shift, magnitude and shape, which requires information from both time and frequency domain. And by representing time histories in frequency domain, more intuitive information can be obtained, such as magnitude-frequency and phase-frequency characteristics.

Lower limb injury is becoming an increasingly important concern in vehicle safety for both occupants and pedestrians. To enable vehicle manufacturers to better understand the biomechanical effects of design changes, it is deemed beneficial to employ a biomechanically fidelic finite element model of the human lower limb. The model developed in this study includes long bones (tibia, fibula, femur) and flat bone (patella) as deformable bodies. The pelvis and foot bones are modeled as rigid bodies connected to the femur and tibia/fibula via rotational spring-dashpots. The knee is defined by scanned bone surface geometry and is surrounded by the menisci, major ligaments, and patellar tendon. Finite elements used to model include 6- and 8-node solids for cartilage, menisci, surrounding muscles, and cancellous bone; 3- and 4-node shells for skin and cortical bone; and nonlinear spring-dashpots for ligaments.

This paper reports a 2010 study undertaken to determine generic acceleration pulses for testing and evaluating advanced batteries for application in electric passenger vehicles. These were based on characterizing vehicle acceleration time histories from standard laboratory vehicle crash tests. Crash tested passenger vehicles in the United States vehicle fleet of the model years 2005-2009 were used. The crash test data were gathered from the following test modes and sources: 1 Frontal rigid flat barrier test at 35 mph (NHTSA NCAP) 2 Frontal 40% offset deformable barrier test at 40 mph (IIHS) 3 Side moving deformable barrier test at 38 mph (NHTSA side NCAP) 4 Side oblique pole test at 20 mph (US FMVSS 214/NHTSA side NCAP) 5 Rear 70% offset moving deformable barrier impact at 50 mph (US FMVSS 301). The accelerometers used were from locations in the vehicle where deformation is minor or non-existent, so that the acceleration represents the “rigid-body” motion of the vehicle.

A new inflator specification, the “inflator thrust variable,” was developed to better explain measured mid-sized male, instrumented test dummy responses in the chest-on-module test condition. Specifically, controlled laboratory experiments were conducted with non-production, driver airbag modules with inflators of various outputs and gas constituents in an effort to assess their effects on a pertinent occupant response. Regression analyses showed that the inflator thrust variable is a better predictor of the observed variation in peak viscous criterion responses than either peak tank pressure or the related pressure rise rate when inflators of differing gas composition were compared.

Robustness/Reliability Assessment and Optimization (RRAO) is often computationally expensive because obtaining accurate Uncertainty Quantification (UQ) may require a large number of design samples. This is especially true where computationally expensive high fidelity CAE simulations are involved. Approximation methods such as the Polynomial Chaos Expansion (PCE) and other Response Surface Methods (RSM) have been used to reduce the number of time-consuming design samples needed. However, for certain types of problems require the RRAO, one of the first question to consider is which method can provide an accurate and affordable UQ for a given problem. To answer the question, this paper tests the PCE, RSM and pure sampling based approaches on each of the three selected test problems: the Ursem Waves mathematical function, an automotive muffler optimization problem, and a vehicle restraint system optimization problem.

The helical spring is one of fundamental mechanical elements used in various industrial applications such as valves, suspension mechanisms, shock and vibration absorbers, hand levers, etc. In high speed applications, for instance in the internal combustion engine or in reciprocating compressor valves, helical springs are subjected to dynamic and impact loading, which can result in a phenomenon called “surge”. Hence, proper design and selection of helical springs should consider modeling the dynamic and impact response. In order to correctly characterize the physics of a helical spring and its response to dynamic excitations, a comprehensive model of spring elasticity for various spring coil and wire geometries, spring inertial effects as well as contacts between the windings leading to a non-linear spring force behavior is required. In practical applications, such models are utilized in parametric design and optimization studies.

We present research in progress to develop and implement a transportable instrumentation package (TIP) to collect driver data in a vehicle. The overall objective of the project is to investigate the symbiotic relationship between humans and their vehicles. We first describe the state-of-art technologies to build the components of TIP that meet the criteria of ease of installation, minimal interference with driving, and sufficient signals to monitor driver state and condition. This method is a viable alternative to current practice which is to first develop a fully instrumented test vehicle, often at great expense, and use it to collect data from each participant as he/she drives a prescribed route. Another practice, as for example currently being used in the SHRP-2 naturalistic driving study, is to install the appropriate instrumentation for data collection in each individual's vehicle, often requiring several hours.

Analytical models (math or computer simulation models) are typically built on the basis of many assumptions and simplifications and hence model prediction could be inaccurate in intended applications. Model validation is thus critical to quantify and improve the degree of accuracy of these models. So far, little work considers model validation for various design configurations so that model prediction is accurate in the intended design space. Furthermore, there is a lack of effective approaches that can be used to quantify model accuracy considering different number of experimental data. To overcome these limitations, objective of this paper is to develop a model validation approach for various design configurations with a reference metric for model accuracy check considering different number of experimental data.

One of the major challenges in multiobjective, multidisciplinary design optimization (MDO) is the long computational time required in evaluating the new designs' performances. To shorten the cycle time of product design, a data mining-based strategy is developed to improve the efficiency of heuristic optimization algorithms. Based on the historical information of the optimization process, clustering and classification techniques are employed to identify and eliminate the low quality and repetitive designs before operating the time-consuming design evaluations. The proposed method improves design performances within the same computation budget. Two case studies, one mathematical benchmark problem and one vehicle side impact design problem, are conducted as demonstration.