A condensed algorithm for adaptive component mode synthesis is proposed to compute the dynamics of viscoelastic flexible multibody systems efficiently and accurately. As studied, the continuous use of modes derived from the initial configuration will lead to poor convergence when dealing with geometric nonlinearity caused by large deformations and overall rotations. The modal reduction at a series of quasi‐static equilibrium configurations should be updated accordingly. According to the loss rate of system energy in the updating process of modal bases, an adaptive mode selection is proposed to reserve the optimal modal bases with their modal number automatically so as to achieve a high‐accuracy simulation. In the proposed condensed iteration algorithm, the order of reduced dynamic equations in the Newton‐Raphson is far less than the number of the unknowns to be discrete in generalized‐α scheme. Using an analytical mapping between the two parts of unknowns, the new algorithm solves a small part of the unknowns iteratively and solves the others noniteratively. Therefore, the saving of time cost comes not only from the proposed adaptive component mode synthesis, but also from the proposed condensed iteration algorithm. The modal bases of subsystems are updated by a series of frame‐like quasi‐static equilibrium configurations independently, in conjunction with the Craig‐Bampton method. Thus, the challenges in the model reduced of extensive ranges of stiffness and damping are removed via the successively updated modal bases. Finally, three numerical tests are made to illuminate the high accuracy and efficiency of the new algorithm proposed.

中文翻译：

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粘弹性柔性多体动力学自适应分量模态合成的凝聚算法

为了有效，准确地计算粘弹性柔性多体系统的动力学特性，提出了一种用于自适应模态合成的凝聚算法。研究表明，在处理由大变形和整体旋转引起的几何非线性时，连续使用源自初始配置的模式将导致收敛性较差。在一系列准静态平衡构型下的模态减小应相应地更新。根据模态库更新过程中系统能量的损失率，提出了一种自适应模式选择方法，以最优模态库为基础自动存储最优模态库，以实现高精度仿真。在提出的压缩迭代算法中，α方案。通过使用未知两部分之间的解析映射，新算法可以迭代地解决一小部分未知问题，而可以非迭代地解决其他未知问题。因此，节省时间成本不仅来自所提出的自适应分量模式合成，还来自于所提出的压缩迭代算法。子系统的模态基础通过一系列框架式准静态平衡配置独立地结合Craig-Bampton方法进行更新。因此，通过相继更新的模态基础消除了模型在降低刚度和阻尼范围方面的挑战。最后，进行了三个数值测试，以阐明所提出的新算法的高精度和高效率。