Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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Chinchilla, Sergio Cantero

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University of Bristol

in Cooperation with on an Cooperation-Score of 37%

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Publications (7/7 displayed)

  • 2024Uncertainty quantification of damage localization based on a probabilistic convolutional neural network3citations
  • 2021Bayesian damage localization and identification based on a transient wave propagation model for composite beam structures35citations
  • 2021Structural health monitoring using ultrasonic guided-waves and the degree of health index27citations
  • 2021A homogenisation scheme for ultrasonic Lamb wave dispersion in textile composites through multiscale wave and finite element modelling2citations
  • 2020Ultrasonic guided wave testing on cross-ply composite laminate7citations
  • 2020A fast Bayesian inference scheme for identification of local structural properties of layered composites based on wave and finite element-assisted metamodeling strategy and ultrasound measurements31citations
  • 2017A multilevel Bayesian method for ultrasound-based damage identification in composite laminates38citations

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Lu, H. Y.
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Gryllias, K.
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  • Lu, H. Y.
  • Gryllias, K.
  • Chronopoulos, D.
  • Mardanshahi, A.
  • Chiachío, Juan
  • Chronopoulos, Dimitrios
  • Malik, Muhammad Khalid
  • Aranguren, Gerardo
  • Calvo-Echenique, Andrea
  • Chiachío, Manuel
  • Royo, José Manuel
  • Etxaniz, Josu
  • Thierry, V.
  • Lhemery, A.
  • Wu, W.
  • Gil-Garcia, Jose M.
  • Yuen, Ka Veng
  • Yan, Wang Ji
  • Papadimitriou, Costas
  • Bochud, Nicolas
  • Rus, Guillermo
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article

A fast Bayesian inference scheme for identification of local structural properties of layered composites based on wave and finite element-assisted metamodeling strategy and ultrasound measurements

  • Chinchilla, Sergio Cantero
  • Yuen, Ka Veng
  • Yan, Wang Ji
  • Papadimitriou, Costas
  • Chronopoulos, Dimitrios
Abstract

<p>Reliable verification and evaluation of the mechanical properties of a layered composite ensemble are critical for industrially relevant applications, however it still remains an open engineering challenge. In this study, a fast Bayesian inference scheme based on multi-frequency single shot measurements of wave propagation characteristics is developed to overcome the limitations of ill-conditioning and non-uniqueness associated with the conventional approaches. A Transitional Markov chain Monte Carlo (TMCMC) algorithm is employed for the sampling process. A Wave and Finite Element (WFE)-assisted metamodeling scheme in lieu of expensive-to-evaluate explicit FE analysis is proposed to cope with the high computational cost involved in TMCMC sampling. For this, the Kriging predictor providing a surrogate mapping between the probability spaces of the model predictions for the wave characteristics and the mechanical properties in the likelihood evaluations is established based on the training outputs computed using a WFE forward solver, coupling periodic structure theory to conventional FE. The valuable uncertainty information of the prediction variance introduced by the use of a surrogate model is also properly taken into account when estimating the parameters’ posterior probability distribution by TMCMC. A numerical study as well as an experimental study are conducted to verify the computational efficiency and accuracy of the proposed methodology. Results show that the TMCMC algorithm in conjunction with the WFE forward solver-aided metamodeling can sample the posterior Probability Density Function (PDF) of the updated parameters at a very reasonable cost. This approach is capable of quantifying the uncertainties of recovered independent characteristics for each layer of the composite structure under investigation through fast and inexpensive experimental measurements on localized portions of the structure.</p>

Topics
  • density
  • impedance spectroscopy
  • theory
  • layered
  • composite