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Large-scale stochastic topology optimization using adaptive mesh refinement and coarsening through a two-level parallelization scheme

Author
Baiges, J.; Martínez, J.; Herrero, D.; Otero, F.; Ferrer, A.
Type of activity
Journal article
Journal
Computer methods in applied mechanics and engineering
Date of publication
2019-01
Volume
343
First page
186
Last page
206
DOI
https://doi.org/10.1016/j.cma.2018.08.028 Open in new window
Project funding
Elastic Flow. Aumento de la eficiencia en procesos de mezcla y transmisión de calor utilizando fluidos viscoelásticos en regimen laminar y turbulento
Repository
http://hdl.handle.net/2117/124683 Open in new window
URL
https://www.sciencedirect.com/science/article/pii/S0045782518304237 Open in new window
Abstract
Abstract Topology optimization under uncertainty of large-scale continuum structures is a computational challenge due to the combination of large finite element models and uncertainty propagation methods. The former aims to address the ever-increasing complexity of more and more realistic models, whereas the latter is required to estimate the statistical metrics of the formulation. In this work, the computational burden of the problem is addressed using a sparse grid stochastic collocation meth...
Citation
Baiges, J., Martínez, J., Herrero, D., Otero, F., Ferrer, A. Large-scale stochastic topology optimization using adaptive mesh refinement and coarsening through a two-level parallelization scheme. "Computer methods in applied mechanics and engineering", Gener 2019, vol. 343, p. 186-206.
Keywords
Adaptive mesh refinement, Large scale, Parallel computing, Robust topology optimization, Sparse grid, Topological derivative
Group of research
(MC)2 - UPC Computational continuum mechanics
ANiComp - Numerical analysis and scientific computation

Participants

  • Baiges Aznar, Joan  (author)
  • Martínez Frutos, Jesús  (author)
  • Herrero Pérez, David  (author)
  • Otero, Fermín  (author)
  • Ferrer, Alex  (author)