Paul Treml, G. Mikota, B. Manhartsgruber, A. Brandl
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引用次数: 0
Abstract
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.
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:
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automation of modelling-
qualitative and modular modelling-
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uncertainties and the effects of modelling errors on system performance-
application of modelling to complex real-world systems.