Identification of composite uncertain material parameters from experimental modal data

Sepahvand, K.; Marburg, Steffen

doi:10.1016/j.probengmech.2014.06.008

Cited by 44 publications

(20 citation statements)

References 19 publications

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“…Model updating techniques have been widely applied to adjust theoretical structural models using modal data obtained experimentally in civil and industrial engineering during the last three decades [14][15][16][17], but also more recently for composite plates [18][19][20][21][22]. Model updating procedure can be treated as a problem of optimization, in which the weighted differences between experimental and theoretical values of some of the modal characteristics of the structure are computed to obtain the objective function.…”

Section: Introductionmentioning

confidence: 99%

Model updating of uncertain parameters of carbon/epoxy composite plates from experimental modal data

Cuadrado

Artero-Guerrero

Pernas-Sánchez

et al. 2019

Journal of Sound and Vibration

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This work presents a methodology to obtain physically-sound models of composite structure laminates using a combination of modal analysis, numerical modelling and parameter updating, avoiding the common uncertainties on the constructions of similar numerical models. Moreover this model establishes the baseline (pristine situation) of the dynamic behaviour of the set of composite plates. Therefore it could be applied for condition assessment or quality manufacturing control of existing structures through a nondestructive Structural Health Monitoring (SHM), and hence it could help to detect degradation or defects of the composite components. The driven data of the methodology were the modal frequencies and shapes of composite plates. To obtain these values an extensive experimental campaign of modal analysis has been performed on a set of carbon/epoxy laminates.A multiple input single output technique has been applied, using a roving hammer exciting the plates at evenly distributed Degrees of Freedom (DoF),

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Section: Introductionmentioning

confidence: 99%

Model updating of uncertain parameters of carbon/epoxy composite plates from experimental modal data

Cuadrado

Artero-Guerrero

Pernas-Sánchez

et al. 2019

Journal of Sound and Vibration

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“…The identification of composite material parameters belongs to the category of inverse problem. 9,10 Sepahvand and Marburg 11 developed an inverse stochastic method based on the non-sampling generalized polynomial chaos method for identifying uncertain elastic parameters from experiment modal data. Multiscale method is an important method to predict the cyclical composite parameters.…”

Section: Introductionmentioning

confidence: 99%

Temperature-dependent thermo-elastic parameter identification for composites using thermal modal data

Jiang

Zhu

et al. 2019

Advances in Mechanical Engineering

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To accurately determine the temperature-dependent parameters of composites, a thermo-elastic parameter identification approach using thermal modal data is proposed in this article. The investigation is based on two hypotheses: (1) the structure is at steady-state temperature field, which means the temperature distribution is time independent; (2) temperature distribution can be determined in advance of thermo-elastic parameter identification, and thermal-related large deformation is not considered. The thermal-dependent elastic constants and thermal expansion coefficients are expressed as intermediate functions with the independent variable of temperature, perturbation method is adapted to calculate the sensitivity of thermal modal frequencies with respect to the intermediate variables, and variables with high sensitivity are selected as the identifying parameters. By constructing the intermediate function and calculating the relative sensitivity, thermo-elastic parameters are identified by minimizing the residual between experimental and analytical natural frequencies under thermal circumstance. Two different types of composite models are employed to verify the strategies of parameter identification. Results show that on the basis of the intermediate function, the proposed approach can be applied to identify the thermo-elastic parameters effectively.

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“…The introduction of Galerkin projection technique using generalized Polynomial Chaos (gPC) theory [14,15] transfer the inverse problem as a deterministic one which involves to identify the unknown gPC coefficients instead of probabilistic parameter of the quantity. Sepahvand and Marburg [16,17] have efficiently estimated the elastic parameters of the orthotropic material via stochastic inverse method using non-sampling based gPC expansion technique. Non-Gaussian experimental modal data are used to identify elastic parameters.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Non-Parametric Identification in Composite Structures Using Experimental Modal Data

Chandra¹,

Sepahvand²,

Geweth³

et al. 2019

Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

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In the stochastic structural analysis of composite structure, the probabilistic knowledge of the uncertain parameters are essential. Variability of the manufacturing process of the composite structure introduces the uncertainty to the elastic parameters. It is easier to identify the uncertainty of the material parameters using stochastic inverse process. An efficient stochastic inverse identification of the elastic parameters of laminated composite plate using generalized Polynomial Chaos (gPC) theory is presented in this paper. A data set of measured eigen frequencies and mass density are used for stochastic inversion processes. Stochastic identification of the elastic parameters of composite plate transforms into estimation of deterministic coefficients of gPC expansion for the elastic parameters. A robust optimization technique by minimization of the quadratic difference between statistical moments is used to estimate the deterministic coefficients of the gPC expansion. These coefficients can effectively construct the distributions of the uncertain elastic parameters. Evaluation of the deterministic coefficients by higher order statistical moments minimization, can efficiently simulate the randomness of the experimental eigen frequencies.

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Identification of composite uncertain material parameters from experimental modal data

Cited by 44 publications

References 19 publications

Model updating of uncertain parameters of carbon/epoxy composite plates from experimental modal data

Model updating of uncertain parameters of carbon/epoxy composite plates from experimental modal data

Temperature-dependent thermo-elastic parameter identification for composites using thermal modal data

Stochastic Non-Parametric Identification in Composite Structures Using Experimental Modal Data

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