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Whiplash-associated disorder is one of the most common injuries from rear-impact crash scenarios. Knowing the injury mechanism is one of the keys in designing the seat to reduce the risk of injury. Due to the effects of variation, whiplash prevention is one of the most challenging safety-related topics in automotive industry. The test variation can originate from the dummy itself, seat components, materials, assembly tolerance, and as well as typical test setup variations. It is important to understand these variations and take them into account using Computer-Aided Engineering (CAE) analysis in order to identify how to reduce the risk of injury. In this paper, a robust methodology to predict occupant response from CAE simulations is developed by combining a Design of Experiment (DOE) with an Automated Process (AP). A Whiplash Variation Map (WVM) is developed to serve as a seat design aid.

Over the past decades, Computer Aided Engineering (CAE) based assessment of vehicle durability, NVH (Noise, Vibration and Harshness) and crash performance has become very essential in vehicle development and verification process. CAE activity is often organized as different groups based on the specific attributes (durability, NVH and crash). Main reasons for this are the expertise required and the difference in the finite element software technologies (explicit vs implicit) used to perform and interpret various CAE analyses in each of the attributes. This leads to individual attribute team creating its own model of the vehicle and there is not much exchange of the CAE models between the attribute teams. Different model requirements for each attribute make model sharing challenging. However, CAE analyses for all attributes start with common CAD and follow the same sub-process in vehicle development cycle.

The modeling of plastic fuel tank systems for crash safety applications has been very challenging. The major challenges include the prediction of fuel sloshing in high speed impact conditions, the modeling of multilayer thermoplastic fuel tanks with post-forming (non-uniform) material properties, and the modeling of tank straps with pre-tensions. Extensive studies can be found in the literature to improve the prediction of fuel sloshing. However, little research had been conducted to model the post-forming fuel tank and to address the tension between the fuel tank and the tank straps for crash safety simulations. Hoping to help improve the modeling of fuel systems, the authors made the first attempt to tackle these major challenges all at once in this study by dividing the modeling of the fuel tank into eight stages. An ALE (Arbitrary Lagrangian-Eulerian) method was adopted to simulate the interaction between the fuel and the tank.

The objective of this study is to evaluate the influence of the hydro-forming process and the effect of strain rate on crash performance and develop a modeling approach to improve the accuracy of crash prediction. Work hardening, thinning and strain rate effects are investigated in both component and full vehicle analyses to understand their sensitivities. Gages measured and material properties tested from post-formed tubes are compared with hydro-forming simulation results to confirm accuracy of the modeling methodology proposed in the paper. Front crash simulation using strain rate and forming effects are correlated with the test data for both component and full vehicle analyses and conclusion has been drawn from this comparison.

Vehicle to vehicle crash compatibility is becoming an increasingly more important consideration during vehicle safety development due to the increasing numbers of SUVs and pickups in the vehicle fleet. According to the Insurance Institute for Highway Safety (IIHS), their side impact crash test represents what happens when a passenger vehicle is struck by a pickup truck or SUV. The IIHS side impact test measures 37 different response criteria using an instrumented 5th percentile female SID-IIs ATD (anthropomorphic test device) in driver and left rear passenger seats. These measures are grouped into head and neck, torso, and pelvis and left leg regions. This paper will describe the development of transfer function equation models to assess the performance of design countermeasures by comparing the response measures of the torso region of the body.