Finite Strain Robust Topology Optimization Considering Multiple Uncertainties
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910801266212864 |
|---|---|
| author | Feng, Nan Zhang, Guodong Khandelwal, Kapil |
| author_facet | Feng, Nan Zhang, Guodong Khandelwal, Kapil |
| contents | This paper presents a computational framework for the robust stiffness design of hyperelastic structures at finite deformations subject to various uncertain sources. In particular, the loading, material properties, and geometry uncertainties are incorporated within the topology optimization framework and are modeled by random vectors or random fields. A stochastic perturbation method is adopted to quantify uncertainties, and analytical adjoint sensitivities are derived for efficient gradient-based optimization. Moreover, the mesh distortion of low-density elements under finite deformations is handled by an adaptive linear energy interpolation scheme. The proposed robust topology optimization framework is applied to several examples, and the effects of different uncertain sources on the optimized topologies are systematically investigated. As demonstrated, robust designs are less sensitive to the variation of target uncertain sources than deterministic designs. Finally, it is shown that incorporating symmetry-breaking uncertainties in the topology optimization framework promotes stable designs compared to the deterministic counterpart, where -- when no stability constraint is included -- can lead to unstable designs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15337 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Finite Strain Robust Topology Optimization Considering Multiple Uncertainties Feng, Nan Zhang, Guodong Khandelwal, Kapil Computational Engineering, Finance, and Science Optimization and Control This paper presents a computational framework for the robust stiffness design of hyperelastic structures at finite deformations subject to various uncertain sources. In particular, the loading, material properties, and geometry uncertainties are incorporated within the topology optimization framework and are modeled by random vectors or random fields. A stochastic perturbation method is adopted to quantify uncertainties, and analytical adjoint sensitivities are derived for efficient gradient-based optimization. Moreover, the mesh distortion of low-density elements under finite deformations is handled by an adaptive linear energy interpolation scheme. The proposed robust topology optimization framework is applied to several examples, and the effects of different uncertain sources on the optimized topologies are systematically investigated. As demonstrated, robust designs are less sensitive to the variation of target uncertain sources than deterministic designs. Finally, it is shown that incorporating symmetry-breaking uncertainties in the topology optimization framework promotes stable designs compared to the deterministic counterpart, where -- when no stability constraint is included -- can lead to unstable designs. |
| title | Finite Strain Robust Topology Optimization Considering Multiple Uncertainties |
| topic | Computational Engineering, Finance, and Science Optimization and Control |
| url | https://arxiv.org/abs/2501.15337 |