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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.

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.

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.

A recent study of engine sensitivity revealed that spark plugs used in conventional spark-ignited gasoline-fueled engines do not always fire in the intended fashion. Rather than firing to the ground strap during each ignition event, the arc frequently travels to the “rim” or “shell” of the spark plug. This behavior is termed rim-fire and although observed by other researchers in industry, its effects on engine performance are not widely reported. This paper addresses some of the quantitative effects of rim-fire on engine performance. Combustion data were recorded for various repeat conditions on a Ford 1.8L Zetec engine. The first set of engine tests used four, new, conventional, automotive spark plugs. The second set of engine tests used four modified spark plugs that induced 100% rim-fire when the ground strap was permanently removed. The study focused on part- and full-load engine performance, EGR tolerance, and step-transient characteristics.

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.

In the last decade, considerable advances have been made in reliability-based design optimization (RBDO). One assumption in RBDO is that the complete information of input uncertainties are known. However, this assumption is not valid in practical engineering applications, due to the lack of sufficient data. In practical engineering design, information concerning uncertainty parameters is usually in the form of finite samples. Existing methods in uncertainty based design optimization cannot handle design problems involving epistemic uncertainty with a shortage of information. Recently, a novel method referred to as Bayesian Reliability-Based Design Optimization (BRBDO) was proposed to properly handle design problems when engaging both epistemic and aleatory uncertainties [1]. However, when a design problem involves a large number of epistemic variables, the computation task for BRBDO becomes extremely expensive.

This paper presents an innovative approach for quality engineering using the Eigenvector Dimension Reduction (EDR) Method. Currently industry relies heavily upon the use of the Taguchi method and Signal to Noise (S/N) ratios as quality indices. However, some disadvantages of the Taguchi method exist such as, its reliance upon samples occurring at specified levels, results to be valid at only the current design point, and its expensiveness to maintain a certain level of confidence. Recently, it has been shown that the EDR method can accurately provide an analysis of variance, similar to that of the Taguchi method, but is not hindered by the aforementioned drawbacks of the Taguchi method. This is evident because the EDR method is based upon fundamental statistics, where the statistical information for each design parameter is used to estimate the uncertainty propagation through engineering systems.

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.

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.

Various fire scenarios involving a hydrogen fuel system were simulated to evaluate their associated safety hazards. Scenarios included finite releases of hydrogen with delayed ignition as well as small hydrogen jet-fire releases. The scenarios tested resulted in minimal damage to the vehicle, minimal hazards to the vehicle's surroundings, and no observable damage or hazards within the passenger compartment.

Forty-eight materials from parts used inside and outside the passenger compartment of six motor vehicles were tested according to FMVSS 302. All samples passed the test although the FMVSS 302 test requirements do not apply to exterior materials. The same materials were also tested in the Cone Calorimeter (ASTM E 1354) at three heat fluxes. The FMVSS 302 performance diagram developed earlier on the basis of Cone Calorimeter data for 18 exterior materials from two vehicles appears to have more general validity for solid plastic parts, regardless whether they are taken from locations inside or outside of the passenger compartment. The previously-developed performance diagram is not applicable to plastic foams and fabrics. Additional criteria are proposed to predict whether a foam or fabric is likely to pass the FMVSS 302 test based on ignition time and peak heat release rate measured in the Cone Calorimeter at a heat flux of 35 kW/m2.

Comparative abuse tests were performed on commercially available 12 V and 36 V battery designs. Four methods were chosen from SAE J2464 standard, Electrical Vehicle Battery Abuse Testing, March 1999, and modified to apply them to typical-sized automotive batteries. The four tests included a Penetration Test, Crush Test, Radiant Heat Test, and Short Circuit Test. Both the 12 V and 36 V batteries showed minimal reactions to the tests, and there was no significant difference between results of the two designs with respect to the abuse tests performed. It should be stressed however, that this project was limited in scope and was not intended to be a thorough investigation in the batteries safety hazards.

Radioactive tracer technology (RAT) is an important tool in measuring component wear in an operating engine on a real-time basis. This paper will discuss the use of RAT to study and evaluate boundary lubricant and surfactant chemistries aimed at providing benefits in wear control. In particular, RAT was employed to study ring and bearing wear as a function of engine operating condition (speed, load, and temperature) and lubricant characteristics. Prior to testing, the engine's compression rings and connecting rod bearings were subjected to bulk thermal neutron bombardment in a nuclear reactor to produce artificial radioisotopes that were separately characteristic of the ring and bearing wear surfaces. The irradiated parts were installed in the test engine, after which testing to a specific test matrix was accomplished.

This paper describes the heat transfer model of a composite aluminum brake rotor and compares the predicted temperatures to dynamometer measurements taken during a 15 fade stop trial. The model is based on meshed surface geometry which is simulated using RadTherm software. Methods for realistically modeling heat load distribution, surface rotation, convection cooling and radiation losses are also discussed. A comparison of the simulation results to the dynamometer data shows very close agreement throughout the fade stop trial. As such, the model is considered valid and will be used for further Steel Clad Aluminum (SCA) rotor development.

This paper examines the role of inhalation toxicity of the products of combustion that are generated in post-collision motor vehicle fires by automotive materials used under the hood. Small-scale toxic gas measurements were performed at Southwest Research Institute® (SwRI®) on eighteen components of two of the vehicles that were tested previously at the Factory Mutual Test Center (FMTC). The small-scale toxic gas measurements were obtained under dynamic flow-through conditions in the Cone Calorimeter (ASTM E 1354) and under static conditions in two smoke chamber methods (ASTM E 662 and ASTM E 1995); all methods were supplemented with FTIR gas analysis. Average yields of toxic gases measured in the Cone Calorimeter are comparable to but consistently lower than values reported in the literature for the Fire Propagation Apparatus (ASTM E 2058).

The primary objective of this study was to compare the performance of “new” plastic fuel tanks vs. “aged” plastic fuel tanks when subjected to the standard fire exposure test described in ECE R34.01, Annex 5 Section 5.0 “Resistance to Fire.” The program also included a comparison of failure modes of plastic vs. metal fuel tanks when subjected to a simulated post-crash pool fire. The “new” tanks were purchased from the OEM suppliers (not weathered or pre-conditioned with fuel). The “aged” tanks were obtained from vehicles that were operated in a warm climate and considered to be weathered and fully conditioned with fuel. Three vehicle types, representing three fuel tank shapes and installations, were evaluated: 1.) “thin profile” tank, typical of front wheel drive cars with the tank mounted on the underbody near the rear seat area and in front of the rear axle; 2.) “square profile” tank, typical of SUV's with the tank mounted behind the rear axle; and 3.)

A fire exposure test was conducted on a 72.4 liter composite (Type HGV-4) hydrogen fuel tank at an initial hydrogen pressure of 34.3 MPa (ca 5000 psi). No Pressure Relief Device was installed on the tank to ensure catastrophic failure for analysis. The cylinder ruptured at 35.7 MPa after a 370 kW fire exposure for 6 min 27 seconds. Blast wave pressures measured along a line perpendicular to the cylinder axis were 18% to 25% less the values calculated from ideal blast wave correlations using a blast energy of 13.4 MJ, which is based on the ideal gas internal energy at the 35.7 MPa burst pressure. The resulting hydrogen fireball maximum diameter of 7.7 m is about 19% less than the value predicted from existing correlations using the 1.64 kg hydrogen mass in the tank.

Injuries produced by standard three point restraint systems with retractors will be compared between cadavers in laboratory simulated collisions at 30 mph barrier equivalent speed and lap and shoulder belted front seat occupants in real world frontal collisions of '73-'75 full sized cars. Tests conducted at SwRI with belted, unembalmed, fresh cadavers have resulted in extremely severe thoracic and cervical injuries, including multiple rib fractures, fractures of the sternum, clavicle and cervical vertebrae. On the other hand, injury data from a national accident investigation study to evaluate the effectiveness of restraints in late model passenger cars indicates that such injuries in real world crashes of equivalent severity are not always observed. The reasons possible for these differences are discussed. Both programs at SwRI are funded by the National Highway Traffic Safety Administration.

General agreement exists that the ultimate goals of traffic accident research are to reduce fatality, mitigate injury and decrease economic loss to society. Although massive quantities of data have been collected in local, national and international programs, attempts by analysts to use these data to explore ideas or support hypotheses have been met by a variety of problems. Specifically, the coded variables in the different files are not consistent and little information on accident etiology is collected. Examples of the inadequacies of present data in terms of the collected and coded variables are shown. The vehicular, environmental and human (consisting of human factors and injury factors) variables are disproportionately represented in most existing data files in terms of recognized statistical evidence of accident causation. A systems approach is needed to identify critical, currently neglected variables and develop units of measurement and data collection procedures.