Numerical and experimental optimization of local visco-elastic damping layer placements for design of calm, smart and smooth structures

局部粘弹性阻尼层布置的数值和实验优化，用于设计平静、智能和平滑的结构

• 批准号: 314987946
• 负责人: Professor Dr.-Ing. Boris Lohmann
• 金额: --
• 依托单位: Lehrstuhl für Regelungstechnik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/314987946?language=en
• 关键词: Numerical; experimental; optimization; local; visco

The main aim of this project is to enable efficient simulation, analysis, design optimization and control of calm, smooth and smart structures. In pursuit of this aim, the goal of this proposal is to develop reliable and efficient nonlinear model reduction techniques that avoid expensive training simulations and exploit the underlying physics of mechanical system models. Since both, finding the optimal design as well as finding the optimal actuator placement needed for vibration control require parameter sweeps, parametric dependencies will be included and considered during reduction.The work program includes:(1) Computation of reduced order bases using simulation-free nonlinear model reduction approaches(2) Computation of reduced order bases for different sets of parameters(3) Development of simulation-free hyper-reduction techniques(4) Development of simulation-free hyper-reduction techniques for parametric nonlinear systems(5) Validation of the individual methods against benchmark models of variable size and characteristics.The effectiveness of the results for the design of calm, smooth and smart structures will be illustrated by a case study with a parametric, nonlinear FE model of a structural dynamic system, preferably in interaction with other participants of the SPP 1897.

该项目的主要目标是实现平静、平滑和智能结构的高效模拟、分析、设计优化和控制。为了实现这一目标，本提案的目标是开发可靠且高效的非线性模型简化技术，避免昂贵的训练模拟并利用机械系统模型的基础物理原理。由于寻找最佳设计以及寻找振动控制所需的最佳执行器位置都需要进行参数扫描，因此在简化过程中将包括并考虑参数依赖性。工作计划包括：（1）使用免仿真计算降阶基非线性模型简化方法(2) 不同参数集的降阶基计算(3) 免仿真超简化技术的开发(4) 参数非线性系统免仿真超简化技术的开发(5) 验证个别方法针对可变尺寸和特性的基准模型。平静、平滑和智能结构设计结果的有效性将通过结构动力系统参数化、非线性有限元模型的案例研究来说明，最好与其他参与者进行交互SPP 1897。

项目成果

Efficient basis updating for parametric nonlinear model order reduction

参数非线性模型降阶的高效基础更新

• DOI: 10.1002/pamm.201800075
• 发表时间: 2018
• 期刊: PAMM
• 作者: C. H. Meyer;C. Lerch;M. Karamooz Mahdiabadi;D. Rixen
• 通讯作者: D. Rixen

COMPLEX MODAL DERIVATIVES FOR MODEL REDUCTION OF NONCLASSICALLY DAMPED, GEOMETRIC NONLINEAR STRUCTURES

用于非经典阻尼几何非线性结构模型简化的复模态导数

• DOI: 10.7712/120119.7240.18679
• 发表时间: 2019
• 期刊: Proceedings of the 7th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COMPDYN 2015)
• 作者: C. H. Meyer;F. M. Gruber;D. J. Rixen
• 通讯作者: D. J. Rixen

Parametric Nonlinear Model Reduction for Structural Dynamics

结构动力学参数化非线性模型简化

• DOI: 10.11128/arep.55.a55265
• 发表时间: 2018
• 作者: C. Lerch;C. Meyer
• 通讯作者: C. Meyer

2 Model order reduction in mechanical engineering

2 机械工程中的模型降阶

• DOI: 10.1515/9783110499001-002
• 发表时间: 2020
• 期刊: Applications
• 作者: B. Lohmann;T. Bechtold;P. Eberhard;J. Fehr;D. J. Rixen;M. C. Varona;C. Lerch;C. D. Yuan;E. B. Rudnyi;B. Fröhlich;P. Holzwarth;D. Grunert;C. H. Meyer;J. B. Rutzmoser
• 通讯作者: J. B. Rutzmoser

SIMULATION-LEAN TRAINING-SETS FOR HYPER-REDUCTION OF PARAMETRIC GEOMETRIC NON-LINEAR STRUCTURES

用于参数几何非线性结构超简化的精益模拟训练集

• DOI: 10.47964/1120.9007.19361
• 发表时间: 2020
• 期刊: XI International Conference on Structural Dynamics
• 作者: C. H. Meyer;D. J. Rixen
• 通讯作者: D. J. Rixen