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Computer simulation was used as a complement to crash and injury field data analysis and physical sled and barrier tests to investigate and predict the spinal injuries of a rear impact in an IndyCar. The model was expected to relate the spinal loads to the observed injuries, thereby predicting the probability and location of spinal fractures. The final goal is to help reduce the fracture risk by optimizing the seat and restraint system design and the driver's position using computer modeling and sled testing. MADYMO Full FE Human Body Model (HBM) was selected for use because of its full spinal structural details and its compatibility with the vehicle and restraint system models. However, the IndyCar application imposed unique challenges to the HBM. First, the driver position in a race car is very different from that in a typical passenger car.

A twenty-five kilowatt (peak power for one minute), permanent magnet electric machine for a hybrid electric vehicle application was designed and tested. The electric machine is located in the clutch housing of an automatically shifted manual transmission and is subjected to 120 °C continuous ambient temperatures. The package constraints and duty cycle requirements resulted in an extremely challenging thermal design for an electric machine. The losses in the machine were predicted using models based on first principles and the heat transfer in the machine was modeled using computational fluid dynamics. The simulations were compared to test results over a variety of operating conditions and the results were used to validate the models. Parametric studies were conducted to evaluate the performance of potting materials and cooling topologies.

Computer modeling and simulation have become one of the primary methods for development and design of automobile occupant protection systems (OPS). To ensure the accuracy and reliability of a math-based OPS design, the correlation quality assessment of mathematical models is essential for program success. In a typical industrial approach, correlation quality is assessed by comparing chart characteristics and scored based on an engineer's modeling experience and judgment. However, due to the complexity of the OPS models and their responses, a systematic approach is needed for accuracy and consistency. In this paper, a correlation grading methodology for the OPS models is presented. The grading system evaluates a wide spectrum of a computer model's performances, including kinematics, dynamic responses, and dummy injury measurements. Statistical analysis is utilized to compare the time histories of the tested and simulated dynamic responses.

GENPAD® is a knowledge-based, three-dimensional modeling computer tool developed by Visteon to create occupant-friendly interiors. GENPAD quickly and easily produces zones to evaluate ergonomic aspects of vehicle interiors such as reach, clearance, vision, and reflection. These zones are produced from automated design studies based on experience and engineering standards accepted by the automotive industry. Without GENPAD, a single study requires an experienced engineer 4-6 hours to complete. Multiple studies require several engineers weeks to perform. The methods used are also error-prone due to complex instructions. To overcome these challenges, GENPAD provides over 50 ergonomic packaging studies that produce accurate results in minutes, not weeks, every time.

Induction noise, which radiates from the open end of the engine air induction system, can be of significant importance in reducing vehicle interior noise and tuning the interior sound to meet customer expectations. This makes understanding the source noise critical to the development of the air induction system and the vehicle interior sound quality. Given the ever-decreasing development times, it is highly desirable to use computer-aided engineering (CAE) tools to accelerate this process. Many tools are available to simulate induction noise or, more generally, duct acoustics. The tools vary in degrees of complexity and inherent assumptions. Three-dimensional tools will account for the most general of geometries. However, it is also possible to model the duct acoustics with quasi-three-dimensional or one-dimensional tools, which may be faster as well.

A coupled vibration and pressure loading procedure has been developed to perform a CAE virtual test for engine air intake manifolds. The CAE virtual test simulates the same physical test configuration and environments, such as the base acceleration vibration excitation and pressure pulsation loads, as well as temperature conditions, for design validation (DV) test of air intake manifolds. The original vibration and pressure load data, measured with respect to the engine speed rpm, are first converted to their respective vibration and pressure power spectrum density (PSD) profiles in frequency domain, based on the duty cycle specification. The final accelerated vibration excitation and pressure PSD load profiles for design validation are derived based on the key life test (KLT) duration and reliability requirements, using the equivalent fatigue damage technique.

A dynamic computer model of automotive air conditioning systems was developed. The model uses simulation software for the coding of 1-D heat transfer, thermodynamics, fluid flow, and control valves. The same software is used to model 3-D solid dynamics associated with mechanical mechanisms of the compressor. The dynamics of the entire AC system is thus simulated within the same software environment. The results will show the models potential applications in component and system design, calibration and control.

Reliability allocation of system objectives for Reliability validation purposes must account for Confidence levels. Misallocating Confidence levels can lead to unrealistic and unmanageable objectives, resulting in increased development times and associated costs. Therefore, it is necessary to correctly model both Reliability and Confidence levels. Unfortunately, modeling for anything more complex than the simplest pass/fail test criteria can become quite complex in a multi-component system. The easiest case to model is time-censored testing with no failures. But time-censored testing with no failures is just a small subset of all viable validation strategies. Given that the validation strategy for each component can be different, trying to isolate a single one-size-fits-all model is extremely difficult. For these complex scenarios, computer simulation provides the best approach to calculating true system performance.