Palmeri Flavia, Laurenzi Susanna
Department of Astronautical Electrical and Energy Engineering, Sapienza University of Rome, Via Salaria 851-881, 00138 Rome, Italy.
Biomimetics (Basel). 2024 Aug 14;9(8):494. doi: 10.3390/biomimetics9080494.
The collapsible tubular mast (CTM) can be compactly folded for transport and deployed in orbit to serve as a key structural element. Once deployed, the CTM is vulnerable to buckling under axial load and bending moments, compromising its load-bearing capacity. The intricate relationship between the CTM's cross-section and its buckling behavior poses a significant challenge for designers. This is due to the ultra-thin nature of the CTM, which gives rise to highly localized buckling modes rather than global ones. To overcome this challenge, we developed surrogate models using a neural network (NN) trained with data from finite element analysis (FEA). These NN-based surrogate models provide high computational accuracy in predicting nonlinear buckling loads under axial force and bending moments around the two principal axes of the CTM's cross-section, achieving R2 values of 0.9906, 0.9987, and 0.9628, respectively. These models also significantly improve computational efficiency, reducing prediction time to a fraction of a second compared to several minutes with FEA. Furthermore, the NN-based surrogate models enable the usage of the non-dominated sorting genetic algorithm (NSGA-II) for multi-objective optimization (MOO) of the CTMs. These models can be integrated in the NSGA-II algorithm to evaluate the objective function of existing and new individuals until a set of 1000 non-dominated solutions, i.e., cross-sectional configurations optimizing buckling performance, is identified. The proposed approach enables the design of ultra-thin CTMs with optimized stability and structural integrity by promoting design decisions based on the quantitative information provided by the NN-based surrogate models.
可折叠管状桅杆（CTM）可以紧凑地折叠起来以便运输，并在轨道上展开以用作关键结构元件。一旦展开，CTM在轴向载荷和弯矩作用下容易发生屈曲，从而损害其承载能力。CTM的横截面与其屈曲行为之间的复杂关系给设计师带来了重大挑战。这是由于CTM的超薄特性，这会导致高度局部化的屈曲模式而非整体屈曲模式。为了克服这一挑战，我们使用基于有限元分析（FEA）数据训练的神经网络（NN）开发了代理模型。这些基于NN的代理模型在预测CTM横截面两个主轴周围的轴向力和弯矩作用下的非线性屈曲载荷时具有很高的计算精度，R2值分别达到0.9906、0.9987和0.9628。这些模型还显著提高了计算效率，与FEA需要几分钟相比，将预测时间缩短到了几分之一秒。此外，基于NN的代理模型使得能够使用非支配排序遗传算法（NSGA-II）对CTM进行多目标优化（MOO）。这些模型可以集成到NSGA-II算法中，以评估现有个体和新个体的目标函数，直到识别出一组1000个非支配解，即优化屈曲性能的横截面配置。所提出的方法通过基于基于NN的代理模型提供的定量信息促进设计决策，从而实现具有优化稳定性和结构完整性的超薄CTM的设计。
