轻质多胞Origami吸能结构优化设计方法研究

With the lightweight development of electric vehicles, it is a challenge that how to satisfy their safety requirements simultaneously. Taking the automotive energy absorber as the study object, the project proposed a novel lightweight multicell energy absorber, which uses the Origami structure to further improve the energy-absorbing efficiency while utilizing the superior energy-absorbing performance of the multicellular structure. This multicell Origami structure guides the favorable deformation mode through the crease design to improve the energy absorption characteristics of the multi-cell tube, while reducing the peak force generated during the impact process, and reducing the restriction of the peak force on the energy absorption performance during the optimization process. On the other hand, due to the change of the deformation mode of the energy-absorbing structure, discontinuities in the responses with respect to the design variables are presented in the crashworthiness optimization, which poses a challenge for the optimal design of the energy-absorbing structure. For this reason, an efficient optimization method has been developed here based on the data mining method to identify the number of discontinuities and their position in the design domain. In this project, the studies on the novel lightweight multi-cell Origami energy absorber structure and data mining-based crashworthiness optimization methods are significant for improving the energy-absorbing efficiency of automotive energy absorbers and promoting the lightweight and safety development of electric vehicles in China.

在电动汽车轻量化的发展趋势下，如何同时保证汽车耐撞性的安全设计要求成为一个难题。本项目以汽车吸能结构为研究对象，提出了一种轻质多胞Origami吸能结构，利用多胞结构优异吸能性能的同时通过引入Origami结构进一步提高吸能效率。这种多胞Origami结构通过折痕设计引导有利的变形模式，以提高多胞管的吸能特性，同时降低冲击过程中产生的峰值力，减少优化过程中峰值力对吸能性能的制约。另一方面，耐撞性优化问题中，由于吸能结构的变形模式改变，导致优化中存在不连续问题，这为吸能结构的优化设计带来了挑战。为此，本项目提出基于数据挖掘的耐撞性优化方法，识别不连续点的数量和位置，以提高优化效率。本课题研究的新型轻质多胞Origami吸能结构设计和基于数据挖掘的耐撞性优化方法，对于提高汽车吸能结构的吸能效率、推动我国电动汽车轻量化和安全性发展具有重要意义。

安全和续航里程问题仍然是制约电动汽车发展的主要问题。多胞结构由于其优秀的吸能性能和轻量化潜力被越来越多广泛研究应用于电动汽车吸能结构中。但是，多胞存在较大的峰值力和不同设计产生的变形模式不同。本文将Origami结构引入到传统多胞结构中，对其进行试验、仿真和理论的分析。了解多胞结构的变形特点和基础变形单元的可能变形模式，基于基础单元的最有效变形模式来设计Origami的预折方式；对轻质多胞Origami 结构进行理论分析，根据理论分析设计了不同折痕形状。另一方面，耐撞性优化问题中，耐撞问题复杂，有限元分析计算量大，这为吸能结构的优化设计带来了挑战。为此，本项目提出基于主动学习的耐撞性优化方法，以提高优化效率。本课题研究的新型轻质多胞Origami吸能结构设计和基于主动学习的耐撞性优化方法，对于提高汽车吸能结构的吸能效率、推动我国电动汽车轻量化和安全性发展具有重要意义。