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Two-tier model reduction of viscoelastically damped finite element models

机译:粘纤维湿润有限元模型的双层模型减少

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Transient simulations of linear viscoelastically damped structures require excessive computational resources to directly integrate the full-order finite element model with time-stepping algorithms. Traditional modal reduction techniques are not directly applicable to these systems since viscoelastic materials depend on time and frequency. A more appropriate reduction basis is obtained from the nonlinear, complex eigenvalue problem, whose eigenvectors capture the appropriate kinematics and enable frequency-based mode selection; unfortunately, the computational cost is prohibitive for computing these modes from large-scale engineering models. To address this shortcoming, this work proposes a novel two-tier reduction procedure to reduce the upfront cost of solving the complex, nonlinear eigenvalue problem. The first reduction step reduces the full-order model with real mode shapes linearized about various centering frequencies to capture the kinematics over a full range of viscoelastic material behavior (glassy, rubbery, and glass-transition zones). This tier-one reduction preserves time-temperature superposition and allows the equations to depend parametrically on operating temperature. The second-level reduction then solves the complex, nonlinear eigenmode solutions in the tier-one reduced space about a fixed temperature to further reduce the equations-of-motion. The method is demonstrated on a cantilevered sandwich plate to showcase its accuracy and efficiency in comparison to full-order model predictions. (C) 2019 Published by Elsevier Ltd.
机译:线性粘接结构的瞬态模拟需要过多的计算资源,直接将全阶有限元模型与时间踩踏算法相结合。由于粘弹性材料取决于时间和频率,传统的模态降低技术不可直接适用于这些系统。从非线性,复杂的特征值问题获得更合适的还原基础,其特征向量捕获适当的运动学并实现基于频率的模式选择;不幸的是,计算成本对从大型工程模型计算这些模式的计算成本是禁止的。为了解决这种缺点,这项工作提出了一种新的二层减少程序,以降低解决复杂的非线性特征值问题的前期成本。第一减少步骤用实模式形状降低了全阶模型,其线性化了各种定心频率,以通过全系列的粘弹性材料行为(玻璃,橡胶和玻璃转换区域)捕获运动学。这一级减少保留了时间温度叠加,并且允许方程在工作温度上依赖于参数。然后,第二级别减少求解在层上的复合物,非线性特征模型围绕固定温度的减小空间,以进一步降低运动方程。与全阶模型预测相比,在悬臂式夹心板上证明了该方法,以展示其精度和效率。 (c)2019年由elestvier有限公司发布

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