Chen Hongyun, Li Xin, Wang Shujing, Zhao Yan, Zheng Yu
College of Mechanical and Electrical Engineering, Suqian University, Suqian 223800, China.
Biomimetics (Basel). 2025 May 15;10(5):320. doi: 10.3390/biomimetics10050320.
In this paper, a beetle with excellent flight ability and a large folding ratio of its hind wings is selected as the biomimetic design. We mimicked the geometric patterns formed during the folding process of the hind wings to construct a deployable mechanism while calculating the sector angles and dihedral angles of the origami mechanism. In the expandable structure of thick plates, hinge-like steps are added on the thick plate to effectively avoid interference motion caused by the folding of the thick plate. The kinematic characteristics of two deployable mechanisms were characterized by ADAMS 2018 software to verify the feasibility of the mechanism design. The finite element method is used to analyze the structural performance of the deployable mechanism, and its modal response is analyzed in both unfolded and folded configurations. The aerodynamic generation of a spatially deployable wing is characterized by computational fluid dynamics (CFD) to study the vortex characteristics at different frame rates. Based on the aerodynamic parameters obtained from CFD simulation, a wavelet neural network is introduced to learn and train the aerodynamic parameters.
在本文中,选取了一种具有出色飞行能力且后翅折叠比大的甲虫作为仿生设计对象。我们模仿后翅折叠过程中形成的几何图案来构建一种可展开机构,同时计算折纸机构的扇形角和二面角。在厚板的可展开结构中,在厚板上添加类似铰链的台阶,以有效避免厚板折叠引起的干涉运动。利用ADAMS 2018软件对两种可展开机构的运动学特性进行表征,以验证机构设计的可行性。采用有限元方法分析可展开机构的结构性能,并在展开和折叠构型下分析其模态响应。通过计算流体动力学(CFD)对空间可展开机翼的气动生成进行表征,以研究不同帧率下的涡旋特性。基于CFD模拟获得的气动参数,引入小波神经网络来学习和训练气动参数。