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Large‐scale elasto‐plastic topology optimization

Granlund, Gunnar LU orcid and Wallin, Mathias LU (2024) In International Journal for Numerical Methods in Engineering 125(23).
Abstract
This work presents large-scale elasto-plastic topology optimization for design of structures with maximized energy absorption and tailored mechanical response. The implementation uses parallel computations to address multi million element three-dimensional problems. Design updates are generated using the gradient-based method of moving asymptotes and the material is modeled using small strain, nonlinear isotropic hardening wherein the coaxiality between the plastic strain rate and stress is exploited. This formulation renders an efficient state solve and we demonstrate that the adjoint sensitivity scheme resembles that of the state update. Furthermore, the KKT condition is enforced directly into the path dependent adjoint sensitivity... (More)
This work presents large-scale elasto-plastic topology optimization for design of structures with maximized energy absorption and tailored mechanical response. The implementation uses parallel computations to address multi million element three-dimensional problems. Design updates are generated using the gradient-based method of moving asymptotes and the material is modeled using small strain, nonlinear isotropic hardening wherein the coaxiality between the plastic strain rate and stress is exploited. This formulation renders an efficient state solve and we demonstrate that the adjoint sensitivity scheme resembles that of the state update. Furthermore, the KKT condition is enforced directly into the path dependent adjoint sensitivity analysis which eliminates the need of monitoring the elasto-plastic switches when calculating the gradients and provides a straight forward framework for elasto-plastic topology optimization. Numerical examples show that structures discretized using several millions degrees of freedom and loaded in multiple load steps can be designed within a reasonable time frame. (Less)
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author
and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
International Journal for Numerical Methods in Engineering
volume
125
issue
23
publisher
John Wiley & Sons Inc.
external identifiers
  • scopus:85200384823
ISSN
1097-0207
DOI
10.1002/nme.7583
language
English
LU publication?
yes
id
3da38f90-1ca3-405d-b269-7c48bf2c58b8
date added to LUP
2024-08-12 10:19:52
date last changed
2025-04-04 14:48:08
@article{3da38f90-1ca3-405d-b269-7c48bf2c58b8,
  abstract     = {{This work presents large-scale elasto-plastic topology optimization for design of structures with maximized energy absorption and tailored mechanical response. The implementation uses parallel computations to address multi million element three-dimensional problems. Design updates are generated using the gradient-based method of moving asymptotes and the material is modeled using small strain, nonlinear isotropic hardening wherein the coaxiality between the plastic strain rate and stress is exploited. This formulation renders an efficient state solve and we demonstrate that the adjoint sensitivity scheme resembles that of the state update. Furthermore, the KKT condition is enforced directly into the path dependent adjoint sensitivity analysis which eliminates the need of monitoring the elasto-plastic switches when calculating the gradients and provides a straight forward framework for elasto-plastic topology optimization. Numerical examples show that structures discretized using several millions degrees of freedom and loaded in multiple load steps can be designed within a reasonable time frame.}},
  author       = {{Granlund, Gunnar and Wallin, Mathias}},
  issn         = {{1097-0207}},
  language     = {{eng}},
  number       = {{23}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{International Journal for Numerical Methods in Engineering}},
  title        = {{Large‐scale elasto‐plastic topology optimization}},
  url          = {{http://dx.doi.org/10.1002/nme.7583}},
  doi          = {{10.1002/nme.7583}},
  volume       = {{125}},
  year         = {{2024}},
}