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Research Papers

Improving Model Parameters in Vibrating Systems Using Neumann Series

[+] Author and Article Information
Alyssa T. Liem

Department of Mechanical Engineering,
Boston University,
Boston, MA 02215
e-mail: atliem@bu.edu

J. Gregory McDaniel

Department of Mechanical Engineering,
Boston University,
Boston, MA 02215
e-mail: jgm@bu.edu

Andrew S. Wixom

Applied Research Laboratory,
Structural Acoustics Department,
Pennsylvania State University,
State College, PA 16804
e-mail: axw274@psu.edu

Contributed by the Technical Committee on Vibration and Sound of ASME for publication in the JOURNAL OF VIBRATION AND ACOUSTICS. Manuscript received April 12, 2018; final manuscript received August 6, 2018; published online September 17, 2018. Assoc. Editor: Jeffrey F. Rhoads.

J. Vib. Acoust 141(1), 011017 (Sep 17, 2018) (11 pages) Paper No: VIB-18-1161; doi: 10.1115/1.4041217 History: Received April 12, 2018; Revised August 06, 2018

A method is presented to improve the estimates of material properties, dimensions, and other model parameters for linear vibrating systems. The method improves the estimates of a single model parameter of interest by finding parameter values that bring model predictions into agreement with experimental measurements. A truncated Neumann series is used to approximate the inverse of the dynamic stiffness matrix. This approximation avoids the need to directly solve the equations of motion for each parameter variation. The Neumman series is shown to be equivalent to a Taylor series expansion about nominal parameter values. A recursive scheme is presented for computing the associated derivatives, which are interpreted as sensitivities of displacements to parameter variations. The convergence of the Neumman series is studied in the context of vibrating systems, and it is found that the spectral radius is strongly dependent on system resonances. A homogeneous viscoelastic bar in longitudinal vibration is chosen as a test specimen, and the complex-valued Young's modulus is chosen as an uncertain parameter. The method is demonstrated on simulated experimental measurements computed from the model. These demonstrations show that parameter values estimated by the method agree with those used to simulate the experiment when enough terms are included in the Neumann series. Similar results are obtained for the case of an elastic plate with clamped boundary conditions. The method is also demonstrated on experimental data, where it produces improved parameter estimates that bring the model predictions into agreement with the measured response to within 1% at a point on the bar across a frequency range that includes three resonance frequencies.

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Figures

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Fig. 1

Speedup of approximation for evaluation with one parameter at one frequency

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Fig. 2

Absolute value of accelerance for the true, nominal, and updated response versus frequency for the analytical rubber bar case

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Fig. 3

Logarithm of normalized error, ϵ(ω), versus frequency and order of approximation for the analytical rubber bar case

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Fig. 4

True, initial, and updated storage modulus, E′(ω), and loss factor, η(ω), versus frequency for the analytical rubber bar case

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Fig. 5

Logarithm of normalized error, ϵ(ω), versus CPU time used for the approximation method and iterative method

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Fig. 6

A schematic of the longitudinal bar experiment

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Fig. 7

Absolute value of accelerance for the nominal and updated responses, and experimental data versus frequency for the experimental rubber bar case

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Fig. 8

Logarithm of normalized error, ε(ω), versus frequency and order of approximation for the experimental rubber bar case

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Fig. 9

Spectral radius, ρ(−γ1(ω)A0−1(ω)A1(ω)), versus frequency for the experimental rubber bar case

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Fig. 10

Updated storage modulus, E′(ω), and loss factor, η(ω), versus frequency for the experimental rubber bar case

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Fig. 11

Absolute value of accelerance for the true, nominal, and updated response versus frequency for the analytical steel plate case

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Fig. 12

Logarithm of normalized error, ϵ(ω), versus frequency and order of approximation for the analytical steel plate case

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Fig. 13

Spectral radius, ρ(−γ1(ω)A0−1(ω)A1(ω)), versus frequency for the analytical steel plate case

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Fig. 14

Measured transient force versus time from impact hammer

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Fig. 15

Measured transient acceleration versus time from accelerometer

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