基于元模型的被动振动抑制正反设计
Metamodel Based Forward and Inverse Design for Passive Vibration Suppression
摘要：
非周期性超材料代表了一类由不同的构建块（单元）组成的结构系统，而不是相同单元的自重复链。因此，优化非周期性细胞结构系统提出了高维的问题，是具有挑战性的解决使用纯粹的高保真结构优化方法。专门的分析建模以及基于元模型的优化可以提供更易于处理的替代解决方案。为此，本文提出了一种设计自动化框架应用于一维超材料系统，即钻柱，其中振动抑制是至关重要的。钻柱包括附接到纵向杆的外表面的一组不均匀环。因此，所得到的系统现在可以被感知为非周期性1D超材料，其中每个环/间隙表示单元。尽管是1D系统，但同时考虑多个DoF（即，扭转、轴向和横向运动）提出了显著的计算挑战。因此，采用传递矩阵法（TMM）来解析地确定钻柱的频率响应。一套神经网络（ANN）在TMM样本上进行训练（其呈现每次评估的分钟尺度计算成本），以对频率响应进行建模。然后执行基于ANN的优化，以在一种情况下最小化受到连续谐振峰之间的间隙约束的质量，并在第二种情况下最小化该间隙，从而导致相对于基线的关键改进。通过开发一种逆建模方法，可以即时产生具有给定期望非谐振频率范围的最小质量的1D超材料设计，从而做出进一步的新贡献。这是通过使用可逆的神经网络，结果显示有前途的对齐与前向解决方案。
英文摘要：
Aperiodic metamaterials represent a class of structural systems that are composed of different building blocks (cells), instead of a self-repeating chain of the same unit cells. Optimizing aperiodic cellular structural systems thus presents high-dimensional problems that are challenging to solve using purely high-fidelity structural optimization approaches. Specialized analytical modeling along with metamodel based optimization can provide a more tractable alternative solution approach. To this end, this paper presents a design automation framework applied to a 1D metamaterial system, namely a drill string, where vibration suppression is of utmost importance. The drill string comprises a set of nonuniform rings attached to the outer surface of a longitudinal rod. As such, the resultant system can now be perceived as an aperiodic 1D metamaterial with each ring/gap representing a cell. Despite being a 1D system, the simultaneous consideration of multiple DoF (i.e., torsional, axial, and lateral motions) poses significant computational challenges. Therefore, a transfer matrix method (TMM) is employed to analytically determine the frequency response of the drill string. A suite of neural networks (ANN) is trained on TMM samples (which present minute-scale computing costs per evaluation), to model the frequency response. ANN-based optimization is then performed to minimize mass subject to constraints on the gap between consecutive resonance peaks in one case, and minimizing this gap in the second case, leading to crucial improvements over baselines. Further novel contribution occurs through the development of an inverse modeling approach that can instantaneously produce the 1D metamaterial design with minimum mass for a given desired non-resonant frequency range. This is accomplished by using invertible neural networks, and results show promising alignment with forward solutions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
