Model validation and verification of large and complex space structures

Zimmerman, D. C.

doi:10.1080/174159700088027722

Cited by 19 publications

(14 citation statements)

References 9 publications

(12 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, most current vibration-based damage detection techniques observe changes in natural frequencies, modes of vibration, and frequency response functions (Loh and Tou, 1995;Doebling et al, 1996;Smyth et al, 2000;Farrar et al, 2001). Other techniques use subspace identification and updating (Abdalla et al, 1998;Pappa et al, 1998;Zimmerman, 2000;D'Souza and Epureanu, 2005a, b), wavelet analyses (Sohn and Farrar, 2001;Amizic et al, 2002;Amaravadi et al, 2002), and Ritz vectors (Cao and Zimmerman, 1999;Zimmerman, 1999). All these methods have been well developed for monitoring linear structures.…”

Section: Introductionmentioning

confidence: 97%

Enhanced nonlinear dynamics and monitoring bifurcation morphing for the identification of parameter variations

Yin

Epureanu

2005

Journal of Fluids and Structures

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 97%

Enhanced nonlinear dynamics and monitoring bifurcation morphing for the identification of parameter variations

Yin

Epureanu

2005

Journal of Fluids and Structures

View full text Add to dashboard Cite

“…they are focused on determining changes in the frequencies and modes of vibration [14,24,36,49,60]. Other similar techniques use wavelet analyses [3][4][5]62], Ritz vectors [11,77], stochastic approaches [57,64], subspace updating [1,26,32,50,51,72,78,79], or evolutionary algorithms [80]. All these methods have been developed for monitoring linear structures.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Nonlinear Dynamics for Accurate Identification of Stiffness Loss in a Thermo-Shielding Panel

2005

View full text Add to dashboard Cite

The dynamics of a panel forced by transverse loads and undergoing limit cycle oscillations and chaos is investigated. The nonlinear von Karman plate theory is used to obtain a model for healthy and damaged panels. Damage is modeled by a loss of stiffness in a portion of the plate. The presence of low levels of damage is identified by using an external nonlinear excitation and analyzing the attractor of the resulting dynamics in state space. Most of the current studies of such problems are based on linear theories and linear structures. In contrast, the results presented are obtained by using and enhancing nonlinear and chaotic dynamics, and have the advantage of an increased accuracy in detecting damage and monitoring structural health.

show abstract

“…Such methods monitor changes in vibratory characteristics of a structure, which reflect damage. Several of these techniques use subspace identification and updating [3][4][5][6], wavelet analyses [7,8], and Ritz vectors [9,10]. Although there have been numerous studies showing that changes in observed linear features can be used to detect the presence of damage, the low sensitivity of these features (to damage) limits the applicability of such methods.…”

mentioning

confidence: 98%

Nonlinear Feedback Excitation for System Interrogation by Bifurcation Morphing

Yin

Epureanu

2008

AIAA Journal

View full text Add to dashboard Cite

This paper demonstrates a novel system-interrogation method based on observing the morphing of bifurcations and the postbifurcation dynamics through both experimental and numerical methods. A sensing cantilever beam is built with lead-zirconate-titanate patches symmetrically bonded to both sides of its root. A desired bifurcation in the dynamics of the beam can be induced by applying a nonlinear feedback excitation to the beam. The nonlinear feedback excitation requires the active measurement of the dynamics and a feedback loop, and it is generated by applying the voltage (resulting from a nonlinear feedback) to the piezoelectrode. Also, a finite element model is used to design the nonlinear feedback excitation and to predict the response of the sensing beam. Numerical simulations and experiments are performed and compared that demonstrate the effectiveness and high level of sensitivity of the novel approach to detect very small amounts of mass added at the tip of the beam. Nomenclature a = linear gain C = damping matrix in a finite element model c = damping in a one-degree-of-freedom model F = forcing vector K = stiffness matrix in a finite element model k = stiffness in a one-degree-of-freedom model M = mass matrix in a finite element model m = mass in a one-degree-of-freedom model p i = parameters of the nonlinear controller V = voltage applied to lead-zirconate-titanate patches v = velocity at the tip of the sensing beam X = state-space vector in a finite element model , = structural damping characteristics , = nonlinear gains = damping ratio = frequency of the harmonic component of the excitation

show abstract

Model validation and verification of large and complex space structures

Cited by 19 publications

References 9 publications

Enhanced nonlinear dynamics and monitoring bifurcation morphing for the identification of parameter variations

Enhanced nonlinear dynamics and monitoring bifurcation morphing for the identification of parameter variations

Enhanced Nonlinear Dynamics for Accurate Identification of Stiffness Loss in a Thermo-Shielding Panel

Nonlinear Feedback Excitation for System Interrogation by Bifurcation Morphing

Contact Info

Product

Resources

About