Large‐scale elasto‐plastic topology optimization
(2024) In International Journal for Numerical Methods in Engineering- 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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3da38f90-1ca3-405d-b269-7c48bf2c58b8
- author
- Granlund, Gunnar
LU
and Wallin, Mathias LU
- organization
- publishing date
- 2024
- type
- Contribution to journal
- publication status
- epub
- subject
- in
- International Journal for Numerical Methods in Engineering
- 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
- 2024-08-13 15:55:45
@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}}, 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}}, year = {{2024}}, }