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A 2D model for vehicle-to-vehicle impact analysis that was presented in an earlier paper [1], has been used to study several two-vehicle frontal impacts with different incidence angles, frontal overlap offsets, and mass ratios. The impacts have been evaluated in terms of energy and momentum change in the bullet vehicle and the target vehicle. Based on comparisons between pre- and post-impact longitudinal, lateral, and angular components of kinetic energy, and linear and angular momenta, the impacts experienced by the target vehicle and the bullet vehicle have been classified as collinear or oblique. These results have been used to propose a definition of frontal impact based on vehicle kinematics during a crash.

Connectivity and artificial intelligence will be major features of many upcoming products. The need for accurate estimation of the state of these products and their operational environments, and, the intricacy of their decision, planning, and control algorithms, will cause unprecedented growth in their design complexity as well as their software content. The failures of complex software-intensive electronically controlled products of today can often be traced to the interfaces between different subsystems and to the intersection between different engineering disciplines, i.e., mechanical, electrical, and software. Experts who possess intuition regarding the failure modes and robust design of complex electronically controlled products are few and consequently, information management solutions that can help capture and reuse the product failure modes are crucial for delivering dependable, software-intensive products.

A finite element model of the Transport Research Laboratory (TRL) honeycomb barrier, which is being proposed for use in vehicle compatibility studies, has been developed for use in LSDYNA. The model employs penalty parameters to enforce continuity between adjacent finite elements of the honeycomb barrier. Results of impact tests with indentors of various shapes and sizes were used to verify the performance of the computational model. Numerical simulations show reasonably good agreement with the test results.

System dynamics modeling of complex processes such as product development, manufacturing, and service, is an efficient approach for assessing value potential of different business transformation alternatives at small and large enterprises. Process elements such as generation of concepts, detailed designs, pilot level plant trials, etc. can be modeled including first-pass work, testing and review, rework identification and defect fixing, along with release readiness, staffing, schedule pressures, overtime and many other business metrics. Enterprise level processes, with their complex logic loops, can be represented as a system of coupled nonlinear differential equations, whose solutions can reveal the intricate underlying dynamics. Design of experiments, performed on the system dynamics models representing the business processes, are an inexpensive way of gaining insights into the impact of interactions between the numerous process control variables.