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In the Big Data era, the capability in statistical and probabilistic data characterization, data pattern identification, data modeling and analysis is critical to understand the data, to find the trends in the data, and to make better use of the data. In this paper the fundamental probability concepts and several commonly used probabilistic distribution functions, such as the Weibull for spectrum events and the Pareto for extreme/rare events, are described first. An event quadrant is subsequently established based on the commonality/rarity and impact/effect of the probabilistic events. Level of measurement, which is the key for quantitative measurement of the data, is also discussed based on the framework of probability. The damage density function, which is a measure of the relative damage contribution of each constituent is proposed. The new measure demonstrates its capability in distinguishing between the extreme/rare events and the spectrum events.

In an environment of tougher engineering constraints to deliver tomorrow's aerodynamic vehicles, evaluation of aerodynamics early in the design process using digital prototypes and simulation tools has become more crucial for meeting cost and performance targets. Engineering needs have increased the demands on simulation software to provide robust solutions under a range of operating conditions and with detailed geometry representation. In this paper the application of simulation tools to wheel design in on-road operating conditions is explored. Typically, wheel and wheel cover design is investigated using physical tests very late in the development process, and requires costly testing of many sets of wheels in an on-road testing environment (either coast-down testing or a moving-ground wind-tunnel).

Durability life is one of the major concerns in the automotive industry. Road Load Data Acquisition (RLDA) is one of the most important steps to verify exhaust system durability performance. RLDA will not only provide data for system level rig testing drive file development but also for exhaust components validation (computing safety factors). Modal transient FEA can be utilized to simulate either vehicle durability testing or sub-system level rig testing. How to simulate correctly is critical in the simulation. One of the most challenging portions in the full exhaust system simulation is isolator modeling due to its non-linear characteristics. However, we have to use linear modeling to simulate isolator in modal transient analysis, which induces errors.

The Cardan joint of a steerable beam front axle is a complicated mechanical component. It is subjected to drive torque, speed fluctuations, and joint articulation due to powertrain inputs, steering, and suspension kinematics. This combination of high torque and speed fluctuations of the Cardan joint, due to high input drive torque and/or high steer angle maneuvers, can result in premature joint wear. Initially, some observations of premature wear were not well understood based on the existing laboratory and road test data. The present work summarizes a coordinated program of computer modeling, vehicle Rough Road data acquisition, and physical testing used to predict the joint dynamics and to develop advanced testing procedures. Results indicate analytical modeling can predict forces resulting from Cardan joint dynamics for high torque/high turn angle maneuvers, as represented by time history traces recorded in rough road data acquisition.

Fuel Tank Straps very often get different durability fatigue test results from different types of durability testing such as shaker table vibration, road test simulator (RTS) vehicle testing and proving ground vehicle durability testing. One test produces good durability results and other may indicate some durability risk. A special study was conducted to address this inconsistency. It was found that fuel level in the tank plays a big role in fuel tank strap durability. Higher fuel levels in a tank produce higher loads in straps and lower fatigue life. This paper will use a CAE fuel tank strap model and acquired proving ground strap load data to study fuel level influence in fuel tank strap durability. The fuel level study includes a full tank of fuel, 3 quarters tank of fuel, a half tank of fuel and one quarter tank of fuel.