具有快速旋转柔体的弹性多体系统的模型阶数约简

Lennart Frie, O. Dieterich, P. Eberhard
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引用次数: 0

摘要

弹性多体系统(EMBS)的动力学行为通常是用高维微分方程组描述的虚拟样机来分析的。模型降阶(MOR)是通过用降阶代理模型近似整个系统来进行有效系统评估的关键步骤。如何描述被还原系统中各体的耦合所引起的动力学是EMBS的一个难点。在这篇贡献中,提出了一个快速旋转体EMBS的简化工作流程。由于惯性力的作用,旋转引起动力学行为的变化,因此不能忽略。本文给出了恒角速度旋转体的线性描述。比较了不同的基于投影的MOR技术,并将其应用于旋翼直升机的工业模型。为此,简要介绍了EMBS和MOR的建模方法。然后将子结构约简与模态约简、基于Krylov子空间的矩匹配和固有正交分解的耦合系统约简进行了对比。在频域比较了简化后系统的近似误差。结果表明,旋转相关项是正确描述系统动态行为的必要条件。利用子结构固有正交分解得到了近似误差小、加速大的简化模型,并优于模态截断和克雷格-班普顿约简等标准方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Model Order Reduction for Elastic Multibody Systems with Fast Rotating Flexible Bodies
The dynamical behavior of Elastic Multibody Systems (EMBS) is often analyzed using virtual prototypes described by high-dimensional systems of differential equations. Model Order Reduction (MOR) is a key step to permit efficient system evaluations by approximating the full system with a reduced order surrogate model. It is one challenge in MOR of EMBS, to describe the dynamics induced through the coupling of bodies in the reduced system. In this contribution, a workflow for the reduction of EMBS with fast rotating bodies is presented. The rotation causes a change of dynamical behavior due to inertia forces and, therefore, cannot be neglected. In the scope of this work a linear description of rotating bodies with constant angular velocity is given. Different projection-based MOR techniques are compared and applied to an industrial model of a helicopter with rotating rotor. For this purpose, a short introduction on modeling of EMBS and MOR is given. Substructured reduction is then contrasted to the reduction of the coupled system for modal reduction techniques, moment matching based on Krylov subspaces, and Proper Orthogonal Decomposition. The approximation errors of the reduced systems are compared in frequency domain. It is shown that rotation-dependent terms are essential to describe the dynamic behavior of the system correctly. Reduced models with low approximation errors and large speed-up are obtained with substructured Proper Orthogonal Decomposition and outperform the standard techniques modal truncation and Craig-Bampton reduction.
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