Browse Publications Technical Papers 2020-01-0627

Kriging-Assisted Structural Design for Crashworthiness Applications Using the Extended Hybrid Cellular Automaton (xHCA) Framework 2020-01-0627

The Hybrid Cellular Automaton (HCA) algorithm is a generative design approach used to synthesize conceptual designs of crashworthy vehicle structures with a target mass. Given the target mass, the HCA algorithm generates a structure with a specific acceleration-displacement profile. The extended HCA (xHCA) algorithm is a generalization of the HCA algorithm that allows to tailor the crash response of the vehicle structure. Given a target mass, the xHCA algorithm has the ability to generate structures with different acceleration-displacement profiles and target a desired crash response. In order to accomplish this task, the xHCA algorithm includes two main components: a set of meta-parameters (in addition target mass) and surrogate model technique that finds the optimal meta-parameter values. This work demonstrates the capabilities of the xHCA algorithm tailoring acceleration and intrusion through the use of one meta-parameter (design time) and the use of Kriging-assisted optimization. The numerical example of B-pillar under a side rigid wall impact is used to illustrate the targeting capability of the xHCA using the proposed method. Optimal design parameters, namely, design time and target mass (volume fraction), are predicted by the Kriging-assisted algorithm. In this approach, the Kriging model is iteratively updated by adding linearly interpolated designs that minimize the distance with the target crash response. The accuracy of the different targeting methods and their computation costs are compared. In order to generate the initial Kriging model, two set of sample sizes are compared in this work. The first samples size consists of 24 initial crash simulations. The second sample size consists of only four initial simulations. While the fewer number of initial simulations require more iterations to converge, the overall computational cost is lower.


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