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Dong, Wenxiang
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Development of Subject-Specific Elderly Female Finite Element Models for Vehicle Safety

Chongqing University-Yunlei Yin, Junming Li, Qingmiao Wang
State Key Lab of Veh NVH & Safety Technology/Chongqing Univ-Wenxiang Dong, Zhenfei Zhan
Published 2019-04-02 by SAE International in United States
Previous study suggested that female, thin, obese, and older occupants had a higher risk of death and serious injury in motor vehicle crashes. Human body finite element models were a valuable tool in the study of injury biomechanics. The mesh deformation method based on radial basis function(RBF) was an attractive alternative for morphing baseline model to target models. Generally, when a complex model contained many elements and nodes, it was impossible to use all surface nodes as landmarks in RBF interpolation process, due to its prohibitive computational cost. To improve the efficiency, the current technique was to averagely select a set of nodes as landmarks from all surface nodes. In fact, the location and the number of selected landmarks had an important effect on the accuracy of mesh deformation. Hence, how to select important nodes as landmarks was a significant issue. In the paper, an efficient peak point-selection RBF mesh deformation method was used to select landmarks. The multiple peak points were selected to expand landmarks set, so as to improve the morphing quality compared…
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A Feature-Based Responses Prediction Method for Simplified CAE Models

China Automotive Engineering Research Institute Co., Ltd.-Qingjiang Zhao, Wei Xu
Chongqing University-Huijie Xu, Xin Yang, Wenxiang Dong
Published 2019-04-02 by SAE International in United States
In real-world engineering problems, the method of model simplification is usually adopted to increase the simulation efficiency. Nevertheless, the obtained simulation results are commonly with low accuracy. To research the impact from model simplification on simulation results, a feature-based predictive method for simplified CAE model analysis is proposed in this paper. First, the point clouds are used to represent the features of simplified model. Then the features are quantified according to the factors of position for further analysis. A formulated predictive model is then established to evaluate the responses of interest for different models, which are specified by the employed simplification methods. The proposed method is demonstrated through an engineering case. The results suggest that the predictive model can facilitate the analysis procedure to reduce the cost in CAE analysis.
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A Maximum Incompatibility Constrained Collaborative Optimization Method for Vehicle Weight Reduction

China Automotive Engrg Rsch Inst Co Ltd-Wei Xu, Qingjiang Zhao
Chongqing University-Wenxiang Dong, Zhenfei Zhan, Chong Chen, Yudong Fang, Ling Zheng
Published 2018-04-03 by SAE International in United States
Collaborative optimization is an important design tool in complex vehicle system engineering. However, there are many problems yet to be resolved when applying the conventional collaborative optimization method in vehicle body weight reduction, such as convergence difficulties and low optimization efficiency. To solve these problems, a maximum incompatibility constrained collaborative optimization method is proposed in the paper. First, the 1-norm equality constraints expression of the system level is used to replace the traditional 2-norm inequality constraints. Then, a maximum incompatibility is selected from modified inequality constraints to improve optimization efficiency. Finally, an overall compatibility constraint is introduced to decrease the influence caused by the initial point. A mathematical example is used to verify the effectiveness and stability of the proposed method. The proposed collaborative optimization method is further demonstrated through a vehicle body weight reduction problem concerning vehicle safety and NVH performances.
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Investigation of the Samples Size Effects on Hybrid Surrogate Model Component Surrogates for Crashworthiness Design

Changan Automobile Co., Ltd.-Huili YU, Hui Zhao
Chongqing University-Chong Chen, Zhenfei Zhan, Wenxiang Dong
Published 2018-04-03 by SAE International in United States
Surrogate model based design optimization has been widely adopted in automotive industry. Hybrid surrogate model with multiple component surrogates is considered to be a better choice when simulating highly non-linear responses in vehicle crashworthiness analysis. Currently, the number of component surrogates has to be decided before-hand when constructing of a hybrid surrogate model. This paper conducts a comparative study on the performances of three popular hybrid modeling methods including heuristic computation strategy, and two kinds of optimal weighted surrogates. The effects of samples size on the number of individual surrogates that should be included into the final hybrid surrogate models for crashworthiness responses are investigated. Different hybrid modeling techniques and multiple validation criteria are evaluated. Some observations and conclusions on the selection of component surrogates in hybrid surrogate modeling are given in the end.
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