Measurement and identification of the nonlinear dynamics of a jointed structure using full-field data; Part II - Nonlinear system identification. (1st March 2022)
- Record Type:
- Journal Article
- Title:
- Measurement and identification of the nonlinear dynamics of a jointed structure using full-field data; Part II - Nonlinear system identification. (1st March 2022)
- Main Title:
- Measurement and identification of the nonlinear dynamics of a jointed structure using full-field data; Part II - Nonlinear system identification
- Authors:
- Jin, Mengshi
Kosova, Giancarlo
Cenedese, Mattia
Chen, Wei
Singh, Aryan
Jana, Debasish
Brake, Matthew R.W.
Schwingshackl, Christoph W.
Nagarajaiah, Satish
Moore, Keegan J.
Noël, Jean-Philippe - Abstract:
- Abstract: The dynamic responses of assembled structures are greatly affected by the mechanical joints, which are often the cause of nonlinear behavior. To better understand and, in the future, tailor the nonlinearities, accurate methods are needed to characterize the dynamic properties of jointed structures. In this paper, the nonlinear characteristics of a jointed beam is studied with the help of multiple identification methods, including the Hilbert Transform method, Peak Finding and Fitting method, Dynamic Mode Decomposition method, State-Space Spectral Submanifold, and Wavelet-Bounded Empirical Mode Decomposition method. The nonlinearities are identified by the responses that are measured via accelerometers in a series of experiments that consist of hammer testing, shaker ringdown testing, and response/force-control stepped sine testing. In addition to accelerometers, two high-speed cameras are used to capture the motion of the whole structure during the shaker ringdown testing. Digital Image Correlation (DIC) is then adopted to obtain the displacement responses and used to determine the mode shapes of the jointed beam. The accuracy of the DIC data is validated by the comparison between the identification results of acceleration and displacement signals. As enabled by full-field data, the energy-dependent characteristics of the structure are also presented. The setup of the different experiments is described in detail in Part I (Chen et al., 2021) of this research. TheAbstract: The dynamic responses of assembled structures are greatly affected by the mechanical joints, which are often the cause of nonlinear behavior. To better understand and, in the future, tailor the nonlinearities, accurate methods are needed to characterize the dynamic properties of jointed structures. In this paper, the nonlinear characteristics of a jointed beam is studied with the help of multiple identification methods, including the Hilbert Transform method, Peak Finding and Fitting method, Dynamic Mode Decomposition method, State-Space Spectral Submanifold, and Wavelet-Bounded Empirical Mode Decomposition method. The nonlinearities are identified by the responses that are measured via accelerometers in a series of experiments that consist of hammer testing, shaker ringdown testing, and response/force-control stepped sine testing. In addition to accelerometers, two high-speed cameras are used to capture the motion of the whole structure during the shaker ringdown testing. Digital Image Correlation (DIC) is then adopted to obtain the displacement responses and used to determine the mode shapes of the jointed beam. The accuracy of the DIC data is validated by the comparison between the identification results of acceleration and displacement signals. As enabled by full-field data, the energy-dependent characteristics of the structure are also presented. The setup of the different experiments is described in detail in Part I (Chen et al., 2021) of this research. The focus of this paper is to compare nonlinear system identification methods applied to different measurement techniques and to exploit the use of high spatial resolution data. Highlights: The results of several nonlinear system identification methods are compared. Backbone and damping curves of a jointed beam are identified. Free decay and forced responses data reveal consistent nonlinear characteristics. The amplitude-dependent mode shape is reconstructed using digital image correlation. Digital image correlation allows the computation of the kinetic energy of the beam. … (more)
- Is Part Of:
- Mechanical systems and signal processing. Volume 166(2022)
- Journal:
- Mechanical systems and signal processing
- Issue:
- Volume 166(2022)
- Issue Display:
- Volume 166, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 166
- Issue:
- 2022
- Issue Sort Value:
- 2022-0166-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-03-01
- Subjects:
- Jointed structures -- Nonlinear system identification -- Digital Image Correlation (DIC) -- Amplitude-dependent characteristics -- Energy-dependent characteristics
Structural dynamics -- Periodicals
Vibration -- Periodicals
Constructions -- Dynamique -- Périodiques
Vibration -- Périodiques
Structural dynamics
Vibration
Periodicals
621 - Journal URLs:
- http://www.sciencedirect.com/science/journal/08883270 ↗
http://firstsearch.oclc.org ↗
http://firstsearch.oclc.org/journal=0888-3270;screen=info;ECOIP ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.ymssp.2021.108402 ↗
- Languages:
- English
- ISSNs:
- 0888-3270
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5419.760000
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British Library HMNTS - ELD Digital store - Ingest File:
- 20195.xml