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Nonlinear homogenization for topology optimization

Wallin, Mathias LU and Tortorelli, Daniel A. (2020) In Mechanics of Materials 145.
Abstract

Non-linear homogenization of hyperelastic materials is reviewed and adapted to topology optimization. The homogenization is based on the method of multiscale virtual power in which the unit cell is subjected to either macroscopic deformation gradients or equivalently to Bloch type displacement boundary conditions. A detailed discussion regarding domain symmetry of the unit cell and its effect on uniaxial loading conditions is provided. The density approach is used to formulate the topology optimization problem which is solved via the method of moving asymptotes. The adjoint sensitivity analysis considers response functions that quantify both the displacement and incremental displacement. Notably, the transfer of the sensitivities from... (More)

Non-linear homogenization of hyperelastic materials is reviewed and adapted to topology optimization. The homogenization is based on the method of multiscale virtual power in which the unit cell is subjected to either macroscopic deformation gradients or equivalently to Bloch type displacement boundary conditions. A detailed discussion regarding domain symmetry of the unit cell and its effect on uniaxial loading conditions is provided. The density approach is used to formulate the topology optimization problem which is solved via the method of moving asymptotes. The adjoint sensitivity analysis considers response functions that quantify both the displacement and incremental displacement. Notably, the transfer of the sensitivities from the microscale to the macroscale is presented in detail. A periodic filter and thresholding are used to regularize the topology optimization problem and to generate crisp boundaries. The proposed methodology is used to design hyperelastic microstructures comprised of Neo-Hookean constituents for maximum load carrying capacity subject to negative Poisson's ratio constraints.

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author
and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Mechanics of Materials
volume
145
article number
103324
publisher
Elsevier
external identifiers
  • scopus:85082832228
ISSN
0167-6636
DOI
10.1016/j.mechmat.2020.103324
language
English
LU publication?
yes
id
d370cdc9-b861-4aec-8243-38d70b0c42d3
date added to LUP
2020-04-15 16:55:43
date last changed
2022-04-18 21:40:31
@article{d370cdc9-b861-4aec-8243-38d70b0c42d3,
  abstract     = {{<p>Non-linear homogenization of hyperelastic materials is reviewed and adapted to topology optimization. The homogenization is based on the method of multiscale virtual power in which the unit cell is subjected to either macroscopic deformation gradients or equivalently to Bloch type displacement boundary conditions. A detailed discussion regarding domain symmetry of the unit cell and its effect on uniaxial loading conditions is provided. The density approach is used to formulate the topology optimization problem which is solved via the method of moving asymptotes. The adjoint sensitivity analysis considers response functions that quantify both the displacement and incremental displacement. Notably, the transfer of the sensitivities from the microscale to the macroscale is presented in detail. A periodic filter and thresholding are used to regularize the topology optimization problem and to generate crisp boundaries. The proposed methodology is used to design hyperelastic microstructures comprised of Neo-Hookean constituents for maximum load carrying capacity subject to negative Poisson's ratio constraints.</p>}},
  author       = {{Wallin, Mathias and Tortorelli, Daniel A.}},
  issn         = {{0167-6636}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Mechanics of Materials}},
  title        = {{Nonlinear homogenization for topology optimization}},
  url          = {{http://dx.doi.org/10.1016/j.mechmat.2020.103324}},
  doi          = {{10.1016/j.mechmat.2020.103324}},
  volume       = {{145}},
  year         = {{2020}},
}