具有集中质量的液压系统的建模

IF 1.8 4区 数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Mathematical and Computer Modelling of Dynamical Systems Pub Date : 2022-07-07 DOI:10.1080/13873954.2022.2080718
Paul Treml, G. Mikota, B. Manhartsgruber, A. Brandl
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引用次数: 0

摘要

摘要建立了一个具有集中参数机械部件的耦合液压机械系统,并在频域中对其进行了测量和数学建模。本文的主要关注点是未知系统参数的识别,这取决于耦合和耗散模型。研究中的装置可以通过液压、流速或机械、力进行激励。测量了液压子系统的响应压力和机械子系统的加速度,由此可以计算激励和系统状态之间的传递函数。互易性用于测量数据的处理。通过适当的两步策略和非线性优化,可以从测量中识别未知的系统参数。此外,还检验了模型和测量的一致性以及这些参数的物理意义。所提出的模型成功地预测了测量的传递函数,其数据没有用于模型参数的识别。
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Modelling of a hydraulic system coupled with lumped masses
ABSTRACT A coupled hydraulic-mechanical system with a lumped parametric mechanical part has been set up, measured and mathematically modelled in the frequency domain. The main focus of this article is the identification of unknown system parameters, which depends on the models of coupling and dissipation. The set-up under investigation can be excited hydraulically, by flow rate, or mechanically, by force. The responding pressures of the hydraulic subsystem and the accelerations of the mechanical subsystem are measured, from which transfer functions between excitation and system states can be calculated. The property of reciprocity is used for the processing of measurement data. With a suitable two-step strategy and non-linear optimization unknown system parameters can be identified from measurements. Additionally, the agreement of model and measurement and the physical meaningfulness of these parameters are examined. The proposed model succeeds in predicting measured transfer functions, whose data weren't used for the identification of model parameters.
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来源期刊
CiteScore
3.80
自引率
5.30%
发文量
7
审稿时长
>12 weeks
期刊介绍: Mathematical and Computer Modelling of Dynamical Systems (MCMDS) publishes high quality international research that presents new ideas and approaches in the derivation, simplification, and validation of models and sub-models of relevance to complex (real-world) dynamical systems. The journal brings together engineers and scientists working in different areas of application and/or theory where researchers can learn about recent developments across engineering, environmental systems, and biotechnology amongst other fields. As MCMDS covers a wide range of application areas, papers aim to be accessible to readers who are not necessarily experts in the specific area of application. MCMDS welcomes original articles on a range of topics including: -methods of modelling and simulation- automation of modelling- qualitative and modular modelling- data-based and learning-based modelling- uncertainties and the effects of modelling errors on system performance- application of modelling to complex real-world systems.
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