On an efficient global/local stochastic methodology for accurate stress analysis, failure prediction and damage tolerance of laminated composites

International Journal of Solids and Structures, vol. 303, pp. 113026

Abstract

The quantification of uncertainties in the mechanical response of composite structures can be a computationally demanding task. This is due both to the number of uncertain parameters in a real study case and the complexity of the model to be analysed. In this paper, an efficient global/local approach to estimate the uncertainties of the quantities of interest in specific regions of interest with limited computational effort is proposed. This is achieved by refining only locally the model taking advantage of Refined Structural Theories. At the same time, since the variance of the uncertain parameters is usually relatively small, the stochastic analysis is dealt with a sensitivity study carried out both in the global and in the local model. In this way, it is possible to assess the influence of global and local uncertain parameters in the same submodeling analysis. The methodology presented is applied to several study cases of interest. The results focus on obtaining probabilistic distributions of the stress field that can be later used in failure criteria to evaluate the subsequent distribution of the failure index. Furthermore, a damage tolerance study case is investigated, showing good correlation with the reference Monte Carlo simulations.

Authors 7

1. Paolo Minigher

   Universidade do Porto · Institute of Mechanical Engineering and Industrial Mangement · Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial

   Affiliation as printed

   DEMec, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

   INEGI, Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Porto, Portugal

2. Albertino Arteiro

   Universidade do Porto · Institute of Mechanical Engineering and Industrial Mangement · Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial

   Affiliation as printed

   DEMec, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

   INEGI, Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Porto, Portugal

3. A. Turón

   Universitat de Girona

   Affiliation as printed

   University of Girona, AMADE, Polytechnic School, Girona, Spain

4. Javad Fatemi Aachen Airbus NL

   Airbus (Netherlands)

   Affiliation as printed

   Airbus NL, Leiden, The Netherlands

5. S. Guinard

   IRT Saint Exupéry

   Affiliation as printed

   IRT Saint Exupéry, Toulouse, France

6. Ludovic Barrière

   IRT Saint Exupéry

   Affiliation as printed

   IRT Saint Exupéry, Toulouse, France

7. Pedro Ponces Camanho corresponding

   Universidade do Porto · Institute of Mechanical Engineering and Industrial Mangement · Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial

   Affiliation as printed

   DEMec, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

   INEGI, Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Porto, Portugal