Untitled

Funder

Organizational Unit

Publications

Assessing static and dynamic response variability due to parametric uncertainty on fibre-reinforced composites

Publication . Carvalho, Alda; Silva, Tiago A. N.; Loja, M.A.R.

Composite structures are known for their ability to be tailored according to specific operating requisites. Therefore, when modelling these types of structures or components, it is important to account for their response variability, which is mainly due to significant parametric uncertainty compared to traditional materials. The possibility of manufacturing a material according to certain needs provides greater flexibility in design but it also introduces additional sources of uncertainty. Regardless of the origin of the material and/or geometrical variabilities, they will influence the structural responses. Therefore, it is important to anticipate and quantify these uncertainties as much as possible. With the present work, we intend to assess the influence of uncertain material and geometrical parameters on the responses of composite structures. Behind this characterization, linear static and free vibration analyses are performed considering that several material properties, the thickness of each layer and the fibre orientation angles are deemed to be uncertain. In this study, multivariable linear regression models are used to model the maximum transverse deflection and fundamental frequency for a given set of plates, aiming at characterizing the contribution of each modelling parameter to the explanation of the response variability. A set of simulations and numerical results are presented and discussed.

2018-02-01Journal article

Open access

Assessing static and dynamic response variability due to parametric uncertainty on fibre-reinforced composites

Publication . Carvalho, Alda; Silva, Tiago A. N.; Ramos Loja, M.A.

2018-02-01Journal article

Open access

Selection of modelling parameters for stochastic model updating

Publication . Silva, Tiago A. N.; Mottershead, John E.

In structural dynamics, the adjustment of a set of modelling parameters based on the minimization of the discrepancy between experimental and model responses is known as model updating. In the context of stochastic model updating, the selection of a set of updating parameters from the modelling ones is very important, both in terms of computational efficiency and of the accuracy of the solution of this stochastic inverse problem. One can find in the literature several approaches to model updating. A simple expression was developed for covariance matrix correction in stochastic model updating and by its use one may observe the relevance of choosing the correct set of updating parameters. One may conclude that if the updating parameters are correctly chosen, then the covariance matrix of the outputs is correctly reconstructed, but when the updating parameters are wrongly chosen is found that the responses covariance matrix is generally not reconstructed accurately, although the reconstructing of the responses mean values is accurate. Hence, the selection of updating parameters is developed by assessing the contribution of each candidate parameter to the responses covariance matrix, thereby enabling the selection of updating parameters to ensure that both the responses mean values and covariance matrix are reconstructed by the updated model. It is shown that the scaled output covariance matrix may be decomposed to allow the contributions of each candidate parameter to be assessed. Numerical examples are given to illustrate this theory.

2017-04Conference object

Open access

Adaptive empirical distributions in the framework of inverse problems

Publication . Silva, Tiago; Loja, Amélia; Carvalho, Alda; Maia, Nuno. M.; Barbosa, Joaquim

This article presents an innovative framework regarding an inverse problem. One presents the extension of a global optimization algorithm to estimate not only an optimal set of modeling parameters, but also their optimal distributions. Regarding its characteristics, differential evolution algorithm is used to demonstrate this extension, although other population-based algorithms may be considered. The adaptive empirical distributions algorithm is here introduced for the same purpose. Both schemes rely on the minimization of the dissimilarity between the empirical cumulative distribution functions of two data sets, using a goodness-of-fit test to evaluate their resemblance.

2017Journal article

Restricted access