Reference : High-performance modeling of concrete ageing |
Scientific journals : Article | |||
Engineering, computing & technology : Civil engineering | |||
Computational Sciences | |||
http://hdl.handle.net/10993/39812 | |||
High-performance modeling of concrete ageing | |
English | |
Habera, Michal ![]() | |
Zilian, Andreas ![]() | |
In press | |
Proceedings in Applied Mathematics and Mechanics | |
Wiley | |
90th GAMM Annual Meeting - 2019 | |
Yes (verified by ORBilu) | |
International | |
1617-7061 | |
Weinheim | |
Germany | |
[en] hpc ; concrete ageing ; fenics | |
[en] Long-term behaviour of concrete structural elements is very important for evaluation of its health and serviceability range. The phenomena that must be considered are complex and lead to coupled multiphysics formulations. Such formulations are difficult not only from physical perspective, but also from computational perspective.
In this contribution attention to computational efficiency and effective implementation is payed. Presented model for concrete ageing is based on microprestress-solidification (MPS) theory of Bazant [1], Kunzel’s model for heat and moisture transport [2] and Mazars model for damage [3]. Ageing linear viscoelastic response, which is immanent to MPS theory and concrete creep, leads to ordinary differetial equation for internal variables solved for every quadrature/nodal point. Numerical structure of the finite element discretisation is examined. Few simplifications on physical model lead to a very efficient linear algebra problem for which standard preconditioned Krylov solvers are reviewed. In parallel, weak and strong scaling tests are performed. All results are produced within open-source finite element framework FEniCS [4]. These models are usually a basis for more involved thermo-hygro-chemo-mechanical (THCM) models with migrating chemical species. It is anticipated, that presented results will help practitioners or other structural engineerers with the choice of suitable and efficient methods for long-term concrete modeling. | |
European Regional Development Fund (2014-2020) (Grant agreement: 2017-02-015-15) | |
http://hdl.handle.net/10993/39812 |
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