4.6 Article

Non-linear thermoelastic analysis of thin-walled structures with cohesive-like interfaces relying on the solid shell concept

Journal

FINITE ELEMENTS IN ANALYSIS AND DESIGN
Volume 202, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.finel.2021.103696

Keywords

A; Solid shell; B; Cohesive zone model; C; Finite element methods; D; Non-linear thermo-elasticity; E; Finite elasticity

Funding

  1. Consejeria de Economia y Conocimiento of the Junta de Andalucia (Spain) [US1265577]
  2. Italian Ministry of Education, University and Research [CUP: D68D19001260001]

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This work proposes a thermodynamically consistent framework for coupled thermo-mechanical simulations in thin-walled structures with cohesive interfaces, utilizing solid shell parametrization and locking-free thermo-mechanical solid shell elements. It also extends the interface finite element for geometrical nonlinearities to model thermo-mechanical decohesion events, with computational implementation in ABAQUS. The predictability of the model is demonstrated through several representative examples.
In this work, a thermodynamically consistent framework for coupled thermo-mechanical simulations for thin walled structures with the presence of cohesive interfaces is proposed. Regarding the shell formulation, a solid shell parametrization scheme is adopted, which is equipped with the mixed Enhanced Assumed Strain (EAS) method to alleviate Poisson and volumetric locking pathologies. It is further combined with the Assumed Natural Strain (ANS) method leading to a locking-free thermo-mechanical solid shell element using a fully-integrated interpolation scheme. In order to model thermo-mechanical decohesion events in thin-walled structures with imperfect internal boundaries, an interface finite element for geometrical nonlinearities is herein extended to account for the thermal field and thermo-elastic coupling. The computational implementation of the current finite element formulation has been performed as a user element in ABAQUS via user-defined capabilities. The predictability of the model is demonstrated using several representative examples.

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