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Accurate prediction of the responses from the anthropomorphic test devices (ATDs) in vehicle crash tests is critical to achieving better vehicle occupant performances. In recent years, automakers have used finite element (FE) models of the ATDs in computer simulations to obtain early assessments of occupant safety, and to aid in the development of occupant restraint systems. However, vehicle crash test results have variation, sometimes significant. This presents a challenge to assessing the accuracy of the ATD FE models, let alone improving them. To resolve this issue, it is important to understand the test variation and carefully select the target data for model improvement. This paper presents the work carried out by General Motors and Humanetics Innovative Solutions (formerly FTSS) in a joint project, aimed at improving the FE model of the Hybrid III-50 ATD (HIII-50) v5.1.

Battery thermal management for high power applications such as electrical/hybrid vehicles is crucial. Modeling is an indispensable tool to help engineers design better battery cooling systems. While Computational Fluid Dynamics (CFD) has been used quite successfully for battery thermal management, CFD models can be too large and too slow for repeated transient thermal analysis especially for a battery module or pack. An accurate but much smaller battery thermal model using a state space representation is proposed. The parameters in the state space model are extracted from CFD results. The state space model is then shown to provide identical results as those from CFD under transient power inputs. While a CFD model may take hours to run depending on the size of the problem, the corresponding state space model runs in seconds.

The demand for Out-Of-Position (OOP) simulation capabilities in safety/crash software increased after the National Highway Transportation Safety Administration (NHTSA) final ruling on FMVSS 208 in 2000. However, the development of this capability involves many technical challenges. To expedite the development and manage the technical difficulties, five benchmark problems were established in this study. Each of them addresses specific technical difficulties of airbag OOP simulations. One benchmark problem has an analytical solution; one has a well-known numerical solution and others have the test results. The benchmark set was designed starting from simple Computational Fluid Dynamics (CFD) problems to the complicated OOP applications such that the bugs and algorithmic errors of the code can be easily identified. This paper summarizes the solutions, test results, and associated technical issues of the benchmark problems.