Computational shape optimisation for a gradient-enhanced continuum damage model
(2020) In Computational Mechanics 65(4). p.1105-1124- Abstract
An isotropic gradient-enhanced damage model is applied to shape optimisation in order to establish a computational optimal design framework in view of optimal damage distributions. The model is derived from a free Helmholtz energy density enriched by the damage gradient contribution. The Karush–Kuhn–Tucker conditions are solved on a global finite element level by means of a Fischer–Burmeister function. This approach eliminates the necessity of introducing a local variable, leaving only the global set of equations to be iteratively solved. The necessary steps for the numerical implementation in the sense of the finite element method are established. The underlying theory as well as the algorithmic treatment of shape optimisation are... (More)
An isotropic gradient-enhanced damage model is applied to shape optimisation in order to establish a computational optimal design framework in view of optimal damage distributions. The model is derived from a free Helmholtz energy density enriched by the damage gradient contribution. The Karush–Kuhn–Tucker conditions are solved on a global finite element level by means of a Fischer–Burmeister function. This approach eliminates the necessity of introducing a local variable, leaving only the global set of equations to be iteratively solved. The necessary steps for the numerical implementation in the sense of the finite element method are established. The underlying theory as well as the algorithmic treatment of shape optimisation are derived in the context of a variational framework. Based on a particular finite deformation constitutive model, representative numerical examples are discussed with a focus on and application to damage optimised designs.
(Less)
- author
- Guhr, Fabian ; Sprave, Leon ; Barthold, Franz Joseph and Menzel, Andreas LU
- organization
- publishing date
- 2020-04
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Gradient enhanced model, Large deformation, Non-local damage, Sensitivity analysis, Shape optimisation
- in
- Computational Mechanics
- volume
- 65
- issue
- 4
- pages
- 20 pages
- publisher
- Springer
- external identifiers
-
- scopus:85078418697
- ISSN
- 0178-7675
- DOI
- 10.1007/s00466-019-01810-3
- language
- English
- LU publication?
- yes
- id
- b148c5df-1e79-42d5-85f6-9d9081d42564
- date added to LUP
- 2020-02-10 13:56:07
- date last changed
- 2022-04-18 20:26:20
@article{b148c5df-1e79-42d5-85f6-9d9081d42564,
abstract = {{<p>An isotropic gradient-enhanced damage model is applied to shape optimisation in order to establish a computational optimal design framework in view of optimal damage distributions. The model is derived from a free Helmholtz energy density enriched by the damage gradient contribution. The Karush–Kuhn–Tucker conditions are solved on a global finite element level by means of a Fischer–Burmeister function. This approach eliminates the necessity of introducing a local variable, leaving only the global set of equations to be iteratively solved. The necessary steps for the numerical implementation in the sense of the finite element method are established. The underlying theory as well as the algorithmic treatment of shape optimisation are derived in the context of a variational framework. Based on a particular finite deformation constitutive model, representative numerical examples are discussed with a focus on and application to damage optimised designs.</p>}},
author = {{Guhr, Fabian and Sprave, Leon and Barthold, Franz Joseph and Menzel, Andreas}},
issn = {{0178-7675}},
keywords = {{Gradient enhanced model; Large deformation; Non-local damage; Sensitivity analysis; Shape optimisation}},
language = {{eng}},
number = {{4}},
pages = {{1105--1124}},
publisher = {{Springer}},
series = {{Computational Mechanics}},
title = {{Computational shape optimisation for a gradient-enhanced continuum damage model}},
url = {{http://dx.doi.org/10.1007/s00466-019-01810-3}},
doi = {{10.1007/s00466-019-01810-3}},
volume = {{65}},
year = {{2020}},
}