The effectiveness of Q6 and PIPER 6-year-old models on quantification of the change of child sitting posture with AEB and its impact on child's injures in frontal crash.

OBJECTIVE: Automatic Emergency Braking (AEB) has a direct impact on the effectiveness of the restraint systems in providing protection toward child occupants. The objective is to evaluate the effectiveness of Q6 and PIPER 6-year-old models in predicting the kinematic responses of child models, and further to quantify and analyze the child injuries during a frontal crash with and without AEB. METHODS: The finite element model of a booster seat has been validated through a full vehicle test. Based on the validated finite element model, two sled test finite element models for the rear seat booster seat with Q6 and PIPER 6-year-old models were constructed. AEB condition was imposed on above the two models and the kinematic responses of sitting posture including head, neck and chest have been compared in detail. The peak head displacement and neck curvature of Q6 dummy and PIPER 6-year-old models have been compared with the test data from child volunteers. Based on the child model with better predictive capability for kinematic response under AEB, child injuries were evaluated and analyzed for the 50 km/h frontal crash test with and without AEB. Last, this study discussed the effects of internal neck and chest structure difference between Q6 and PIPER 6-year-old models on child kinematic response and the injury risks. RESULTS: Under AEB condition, PIPER 6-year-old model has higher head displacement and lower trunk displacement than Q6 dummy model, and the peak head displacement and neck curvature of PIPER 6-year-old model are similar to the test data of child volunteers. During the 50 km/h frontal crash simulation with pre-crash AEB, the HIC15, Head acceleration 3 ms, Nij decrease 43.7%, 19.6% and 28.8%, respectively and the chest deflection increases 15.5% compared to the simulation without AEB. CONCLUSIONS: This study shows that PIPER 6-year-old model is more suitable for the quantification of sitting posture change under AEB due to its higher biofidelity. The pre-crash AEB can substantially reduce the head, neck injuries. But it also increases the chest injury due to the chest pre-compression. Future efforts are recommended to lower the child chest injury by integrating AEB with active pre-tensioning seatbelts.

Inverse Identification and Design of Thermal Parameters of Woven Composites through a Particle Swarm Optimization Method.

Designing thermal conductivity efficiently is one of the most important study fields for taking the advantages of woven composites. This paper presents an inverse method for the thermal conductivity design of woven composite materials. Based on the multi-scale structure characteristics of woven composites, a multi-scale model of inversing heat conduction coefficient of fibers is established, including a macroscale composite model, mesoscale fiber yarn model, microscale fiber and matrix model. In order to improve computational efficiency, the particle swarm optimization (PSO) algorithm and locally exact homogenization theory (LEHT) are utilized. LEHT is an efficient analytical method for heat conduction analysis. It does not require meshing and preprocessing but obtains analytical expressions of internal temperature and heat flow of materials by solving heat differential equations and combined with Fourier's formula, relevant thermal conductivity parameters can be obtained. The proposed method is based on the idea of optimum design ideology of material parameters from top to bottom. The optimized parameters of components need to be designed hierarchically, including: (1) combing theoretical model with the particle swarm optimization algorithm at the macroscale to inverse parameters of yarn; (2) combining LEHT with the particle swarm optimization algorithm at the mesoscale to inverse original fiber parameters. To identify the validation of the proposed method, the present results are compared with given definite value, which can be seen that they have a good agreement with errors less than 1%. The proposed optimization method could effectively design thermal conductivity parameters and volume fraction for all components of woven composites.

Targeting the finite-deformation response of wavy biological tissues with bio-inspired material architectures.

The Particle Swarm Optimization algorithm driven by a homogenized-based model is employed to target the response of three types of heart-valve chordae tendineae with different stiffening characteristics due to different degrees of waviness of collagen fibril/fiber bundles. First, geometric and material parameters are identified through an extensive parametric study that produce excellent agreement of the simulated response based on simplified unit cell architectures with the actual response of the complex biological tissue. These include amplitude and wavelength of the crimped chordae microstructure, elastic moduli of the constituent phases, and degree of microstructural refinement of the stiff phase at fixed volume fraction whose role in the stiffening response is elucidated. The study also reveals potential non-uniqueness of bio-inspired wavy microstructures in attaining the targeted response of certain chordae tendineae crimp configurations. The homogenization-based Particle Swarm Optimization algorithm, whose predictions are validated through the parametric study, is then shown to be an excellent tool in identifying optimal unit cell architectures in the design space that exhibits very steep gradients. Finally, defect criticality of optimal unit cell architectures is investigated in order to assess their feasibility in replacing actual biological tendons with stiffening characteristics.