Model Order Reduction for Parametric Non‐linear Mechanical Systems: State of the Art and Future Research
Model Order Reduction for Parametric Non‐linear Mechanical Systems: State of the Art and Future Research
复制标题
参数非线性机械系统的模型降阶：最新技术和未来研究
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
D. Rixen
中科院分区：
文献类型：
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作者：
C. H. Meyer;C. Lerch;B. Lohmann;D. Rixen
德国研究基金会 (DFG – SPP 1897) 1897 年优先计划“冷静、平稳和智能”的一项研究目标是开发参数化非线性机械系统的模型降阶技术，以实现这些系统的高效设计、仿真、分析、优化和控制。作为我们研究的起点，本文概述了该研究领域在该阶段的主要挑战和完善的简化技术，忽略了参数依赖性。这包括基于模拟和无模拟的归约基生成以及非线性力项的超归约。还概述了基于模态导数概念的矩匹配中 Krylov 方向的扩展。 （© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA，魏因海姆）
One research objective in the Priority Program 1897 “Calm, Smooth and Smart” of the German Research Foundation (DFG – SPP 1897) is the development of model order reduction techniques for parametric non‐linear mechanical systems to enable efficient design, simulation, analysis, optimization and control of those. As a starting point for our research, this contribution provides an overview of the main challenges and well‐established reduction techniques in this research area, at that stage, neglecting parameter dependencies. This includes simulation‐based as well as simulation‐free reduction bases generation and hyperreduction of the non‐linear force terms. An extension of the Krylov directions in moment matching based on the concept of modal derivatives is also sketched. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)