Daniel A. Castello

A structural defect identification approach based on a continuum damage model

Abstract This paper introduces a structural identification technique built on finite element (FE) model updating. The FE model is parameterized by a structural parameter that continuously describes the damage in the structure, and besides, an evolution equation of this damage parameter is presented. The model updating is accomplished by determining the subset of this damage parameters that minimizes a global error derived from the dynamic residue vectors, which is obtained by introducing the experimental modal properties into the original model eigenproblem. A mode-shape projection technique is used in order to achieve compatibility between the dimension of the experimental and analytical models. The adjusted model maintains basic properties of the analytical model as the sparsity and the symmetry, which plays an important role in model updating-based damage identification. The verification and assessment of the current structural defect identification is performed on a analytically derived bidimensional truss structure and on a cantilever bidimensional Euler–Bernouilli beam through a virtual test simulator. This simulator is used to realistically simulate the corrupting effects of noise, filtering, digital sampling and truncation of the modal spectrum. The eigensystem realization algorithm along with the common-based normalized system identification were utilized to obtain the required natural frequencies and mode shapes.

Scattering of ultrasonic waves by heterogeneous interfaces: formulating the direct scattering problem as a least-squares problem.

An analytic-numerical method to simulate the interaction of time-harmonic ultrasonic waves with imperfectly bonded layered structures is presented. In the proposed formulation, elastic layers may be either isotropic or anisotropic and adhesion interfaces are replaced by equivalent continuous distribution of normal and transversal springs. In addition, adhesion imperfections are allowed to be localized in space and are modeled by a corresponding local reduction in spring constants. The resulting direct scattering problem is formulated as a least-squares problem and solved accordingly. The formulation was extended for three different cases: Scattering from imperfectly bonded half-spaces, scattering from imperfectly bonded layered structures and scattering from imperfectly bonded layered plates immersed in acoustic fluid, and numerical simulations corresponding to each one of these cases are presented. The simulations indicate that the method is capable of capturing the scattering resulting from the interaction of ultrasonic waves with defective bonds as well as indicate that ultrasound has the potential of revealing the presence of defective bonds and interfacial heterogeneities.

A validation metrics based model calibration applied on stranded cables

The present work is aimed at building a computational model for a typical stranded cable based on the basic principles of Verification and Validation. The model calibration and model tracking are guided based on a pool of validation metrics suitable for data which are commonly used in structural dynamics. The estimator used for the associated inverse problem is the Maximum a Posteriori estimator and the parameter estimation process is performed sequentially over experiments. Experimental tests have been performed at CEPELs (Electric Power Research Center) laboratory span with the overhead conductor Grosbeak in order to provide the measured data. The predictive capacity of the computational model is assessed by means of frequency-and time-domain validations through FRFs, band limited white-noise and sine sweep excitations. We also present novel and reliable estimates for the bending stiffness and damping parameters of a widely used transmission line conductor.

Vibration Reduction in Steel Catenary Risers by the Use of Viscoelastic Materials

The increasing need for petroleum is leading to an exploration in waters each time deeper, leading the structures to have a complex behavior. One of the main components in this area are the risers. These structures are submitted to dynamic loads, among them, it can stand out the one that it is induced by the detachment of vortexes when the structure is submitted to sea currents. These loads may reduce their lifetime due to fatigue. Therefore, many methodologies are being developed to increase the damping of the risers and reduce the vibration effects. One of the ways to reduce the vibrations is the use of viscoelastic materials associated with constraining layers, known in literature as “sandwich structures”. One possible application for this concept is related to the control and the reduction of vibrations in steel catenary risers. The main purpose of this work is present a methodology in order to increase the structural damping factors using the concept of sandwich structures with viscoelastic materials. This application differs from the usual because offshore structures work in lower frequencies than the civil ones, leading the need of an own development for its application. It was developed a numerical model of the sandwich tubes, and its results are evaluated through experimental tests in simple structures accomplished at the Structures Laboratory at COPPE/UFRJ. Through this analysis it is observed a great addition of damping that would allow a reduction of the vibration levels and an increase of the structures lifetime, for example, in steel catenary risers.Copyright

Calibration of adhesion models based on the extended Kalman filtering

ABSTRACT This paper presents a strategy to be used for the calibration of adhesion models using a modified extended Kalman filter algorithm. A constitutive model coupling unilateral contact, adhesion and damage is considered for the analysis. The effect of the uncertainties in the constitutive adhesion parameters onto the adhesion reaction force is performed considering four uncertain scenarios. Furthermore, the effect of the speed in the loading-unloading of the system is also taken into account for these analyses. The parameter estimation strategy is assessed considering noise-corrupted synthetic data, two loading profiles and two possible measurement scenarios. The results indicate the key-role played by the sensitivity analysis when estimating the constitutive adhesion parameters and highlights the low sensitivity of system responses with respect to the viscous parameter when compared to the elastic parameter and also to the parameter associated with the energy of decohesion.

Calibration of adhesion models based on Bayesian inference

The use of composite materials in a myriad of applications fostered the development of reliable procedures to connect components with adhesives. This led to a demand for reliable adhesion models to be used in engineering designs that are based on computer simulations. This paper presents a strategy to be used for calibration of adhesion models. The proposed methodology is built on the formalism of Statistical Inverse Problems. Uncertainties about the unknowns are inferred using Population-Based Markov Chain Monte Carlo and Adaptive Metropolis. It is proposed to perform model assessments based on the analysis of a validation metric. Realizations of the validation metric are computed with the posterior densities of model parameters that are provided by the calibration process. The analysis of the validation metric allows for model selection to be performed. Some numerical experiments are presented with noise-contaminated data. The calibration strategy proved effective when dealing with both the nonlinearity and nondifferentiability of the adhesion constitutive equation.

Identification of material properties using full-field and non contact measurements

In the present work, the Digital Image Correlation (DIC) measurement method is used to obtain the displacement field of specific regions of a cantilever beam under bending. These fields are used within an inverse analysis scheme in order to obtain the elasticity modulus of the beam material. The parameter estimation is performed by means of the minimization of an error function comprising of the difference between the displacement fields obtained from the experiment and from an appropriate mathematical model. The inverse problem is solved by means of the classical Levenberg-Marquardt nonlinear parameter estimation technique. The estimate obtained for the elasticity modulus is validated taking into account new experimental data obtained through modal analysis of another beam-like specimen which, in turn, is made of the same material as the original one.

A Bayesian framework for the calibration of cohesive zone models

ABSTRACT Adhesively bonded joints are used in several industrial sectors. Cohezive Zone Modes can be used to predict the adhesive mechanical behaviour. This work presents an approach to calibrate Cohesive Zone Models (CZM) by means of Statistical Inverse Analysis. The Bayesian framework for Inverse Problems is used to infer about the CZM model parameters. The solution corresponds to the exploration of the posterior probability density function of the model parameters. The exploration of the posterior density is performed by means of Markov Chain Monte Carlo (MCMC) methods mixing Population-Based MCMC with Adaptive Metropolis (AD) strategies. The assessment of the approach is performed using measured data from a single-lap shear experimental set-up. Measured data from 5 test-specimens is used for calibration and measured data from five other test-specimens is used for model validation. It is proposed a stochastic effective model for the CZM parameters. The predictions of maximum force and maximum displacement that are provided by the effective model are in accordance with measured data that is used for validation.

On the model building for transmission line cables: a Bayesian approach

ABSTRACT This work is aimed at building models to predict the bending vibrations of stranded cables used in high-voltage transmission lines. The present approach encompasses model calibration, validation and selection based on a statistical framework. Model calibration is tackled using a Bayesian framework and the Delayed Rejection Adaptive Metropolis (DRAM) sampling algorithm is employed to explore the posterior probability of the unknown model parameters. Two model classes are proposed to predict the bending vibrations of a typical high-voltage stranded cable. Both model classes account for the aerodynamic damping with the surrounding medium and the bending stiffness of the cable. The difference between the two relies on the damping model chosen to quantify the energy dissipation due to friction among the constituent wires of the cable. Model ranking is rigorously quantified by means of a Bayesian model class selection approach, in which both the data-fitting capability and complexity of each model class are simultaneously taken into account. Experimental tests are performed on a laboratory span with a typical high-voltage stranded cable. The measured frequency response functions are the observable quantities employed in the Bayesian model updating for the two model classes proposed. Both model classes provide comparable and accurate predictions for the cable’s frequency response functions within the range [5, 25] Hz, with the fractional derivative-based model class providing the most accurate predictions. Nonetheless, both model classes failed to accurately reproduce the measured cable’s dynamic response within the frequency range [25, 30] Hz.

Estimação do parâmetro não-local de um nanotubo de carbono de parede única via Inferência Bayesiana.

Com o advento dos nanotubos de carbono, CNT, e seu uso em sistemas na- noeletromecanicos, ha um grande interesse atualmente por parte da comunidade cientifica na modelagem do comportamento mecanico de nanovigas. O presente trabalho propoe a formulacao de um problema inverso baseado em medicoes de frequencias em nanotubos de carbono em condicao biapoiada, com o objetivo de estimar o parametro nao-local. O problema inverso e solucionado por Inferencia Bayesiana, onde amostras da densidade de probabilidade a posteriori para o parametro estimado sao obtidas atraves do Metodo de Monte Carlo via Cadeias de Markov (MCMC). O resultado da aplicacao do MCMC apresentou desvios relativamente baixos considerando-se ruidos de 1% e 0, 01% nas frequencias naturais, no qual foram analisadas cinco razoes de aspectos diferentes levando em conta os primeiros cinco modos de vibracao. O processo culminou em dados estatisticos a cerca do parametro nao-local e o detalhamento quanto as formulacoes direta e inversa sao apresentadas ao longo do texto.