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The objective of this paper focused on the modeling of an adaptive energy absorbing steering column which is the first phase of a study to develop a modeling methodology for an advanced steering wheel and column assembly. Early steering column designs often consisted of a simple long steel rod connecting the steering wheel to the steering gear box. In frontal collisions, a single-piece design steering column would often be displaced toward the driver as a result of front-end crush. Over time, engineers recognized the need to reduce the chance that a steering column would be displaced toward the driver in a frontal crash. As a result, collapsible, detachable, and other energy absorbing steering columns emerged as safer steering column designs. The safety-enhanced construction of the steering columns, whether collapsible, detachable, or other types, absorb rather than transfer frontal impact energy.

This paper presents the final phase of a study to develop the modeling methodology for an advanced steering assembly with a safety-enhanced steering wheel and an adaptive energy absorbing steering column. For passenger cars built before the 1960s, the steering column was designed to control vehicle direction with a simple rigid rod. In severe frontal crashes, this type of design would often be displaced rearward toward the driver due to front-end crush of the vehicle. Consequently, collapsible, detachable, and other energy absorbing steering columns emerged to address this type of kinematics. These safety-enhanced steering columns allow frontal impact energy to be absorbed by collapsing or breaking the steering columns, thus reducing the potential for rearward column movement in severe crashes. Recently, more advanced steering column designs have been developed that can adapt to different crash conditions including crash severity, occupant mass/size, seat position, and seatbelt usage.

Computer programs and models are playing an increasing role in simulating vehicle crashworthiness, dynamic, and fuel efficiency. To maximize the effectiveness of these models, the validity and predictive capabilities of these models need to be assessed quantitatively. For a successful implementation of Computer Aided Engineering (CAE) models as an integrated part of the current vehicle development process, it is necessary to develop objective validation metric that has the desirable metric properties to quantify the discrepancy between multiple tests and simulation results. However, most of the outputs of dynamic systems are multiple functional responses, such as time history series. This calls for the development of an objective metric that can evaluate the differences of the multiple time histories as well as the key features under uncertainty.

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.

In the automobile industry, the reliability and predictive capabilities of computer models for a dynamic system need to be assessed quantitatively. Quantitative validation allows engineers to assess and improve model reliability and quality objectively and ultimately lead to potential reduction in the number of prototypes built and tests. A good metric, which is essential in model validation, requires considering uncertainties in both testing and computer modeling. In addition, it needs to be able to compare multiple responses simultaneously, as multiple quantities are often encountered at different spatial and temporal points of a dynamic system. In this paper, a state-of-the-art validation technology is developed for multivariate complex dynamic systems by exploiting a probabilistic principal component analysis method and Bayesian statistics approach.

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.

Laminated glass consisting of two soda lime glass plies adhered by a polyvinyl butyral interlayer (PVB) is used for automotive glazing. This paper describes the application of a dynamic, nonlinear finite element method to investigate the failure modes of a laminated glass subjected to low-velocity impact with a spherical headform. Crack type, crack location and crack initiation time are evaluated using the maximum principal stress and J-integral criterion. Failure occurred due to flexural stresses and not bearing stresses. The first crack always initiated at the center of the outer impacted ply and PVB interface, and later on the exterior surface of the inner ply. The PVB thickness and velocity of impact had little or no effect on the first crack initiation.

In December 2015, the National Highway Traffic Safety Administration (NHTSA) published a Request for Comments on proposed changes to the New Car Assessment Program (NCAP). One potential change is the addition of a frontal oblique impact (OI) crash test using the Test Device for Human Occupant Restraint (THOR). The resultant acetabulum force, which is a unique and specifically defined in the THOR dummy, will be considered as a new injury metric. In this study, the results of ten OI tests conducted by NHTSA on current production mid-sized vehicles were investigated. Specifically, the test data was used to study the lower extremity kinematics for the driver and front passenger THOR dummies. It was found that the acetabulum force patterns varied between the driver and passenger and between the left leg and the right leg of the occupants. The maximum acetabulum force can occur either on the left side or right side of a driver or a front passenger in an OI event.

This paper examines the theoretical worst case of normal headform impact on an infinitely rigid surface with the help of a dynamic spring-mass model. It is pointed out that the current approach is not an actual representation of any vehicle upper interior but is useful in gaining insight into the headform impact phenomenon and determining how to further enhance design. After considering force-deflection characteristics of a variety of commonly used headform impact protection countermeasures, a mathematical model is set up with spring properties that approximate those of physical countermeasures. Closed-form solutions are derived for various dynamic elasto-plastic phases including elastic unloading and contact. A parametric study is then carried out with HIC(d) as the dependent variable, and spring stiffness, yield force and spring length (representing countermeasure crush space) as the design variables.