Details
| Original language | English |
|---|---|
| Pages (from-to) | 515–545 |
| Number of pages | 31 |
| Journal | Computational mechanics |
| Volume | 75 |
| Early online date | 8 Jul 2024 |
| Publication status | Published - Feb 2025 |
Abstract
This study explores reduced-order modeling for analyzing time-dependent diffusion-deformation of hydrogels. The full-order model describing hydrogel transient behavior consists of a coupled system of partial differential equations in which the chemical potential and displacements are coupled. This system is formulated in a monolithic fashion and solved using the finite element method. We employ proper orthogonal decomposition as a model order reduction approach. The reduced-order model performance is tested through a benchmark problem on hydrogel swelling and a case study simulating co-axial printing. Then, we embed the reduced-order model into an optimization loop to efficiently identify the coupled problem’s material parameters using full-field data. Finally, a study is conducted on the uncertainty propagation of the material parameter.
Keywords
- FEniCS, Hydrogels modeling, Model material parameters identification, Model-order reduction, Proper orthogonal decomposition, RBniCS, Uncertainty propagation
ASJC Scopus subject areas
- Engineering(all)
- Computational Mechanics
- Engineering(all)
- Ocean Engineering
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computational Theory and Mathematics
- Mathematics(all)
- Computational Mathematics
- Mathematics(all)
- Applied Mathematics
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In: Computational mechanics, Vol. 75, 02.2025, p. 515–545.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Parameter identification and uncertainty propagation of hydrogel coupled diffusion-deformation using POD-based reduced-order modeling
AU - Agarwal, Gopal
AU - Urrea-Quintero, Jorge Humberto
AU - Wessels, Henning
AU - Wick, Thomas
N1 - Publisher Copyright: © The Author(s) 2024.
PY - 2025/2
Y1 - 2025/2
N2 - This study explores reduced-order modeling for analyzing time-dependent diffusion-deformation of hydrogels. The full-order model describing hydrogel transient behavior consists of a coupled system of partial differential equations in which the chemical potential and displacements are coupled. This system is formulated in a monolithic fashion and solved using the finite element method. We employ proper orthogonal decomposition as a model order reduction approach. The reduced-order model performance is tested through a benchmark problem on hydrogel swelling and a case study simulating co-axial printing. Then, we embed the reduced-order model into an optimization loop to efficiently identify the coupled problem’s material parameters using full-field data. Finally, a study is conducted on the uncertainty propagation of the material parameter.
AB - This study explores reduced-order modeling for analyzing time-dependent diffusion-deformation of hydrogels. The full-order model describing hydrogel transient behavior consists of a coupled system of partial differential equations in which the chemical potential and displacements are coupled. This system is formulated in a monolithic fashion and solved using the finite element method. We employ proper orthogonal decomposition as a model order reduction approach. The reduced-order model performance is tested through a benchmark problem on hydrogel swelling and a case study simulating co-axial printing. Then, we embed the reduced-order model into an optimization loop to efficiently identify the coupled problem’s material parameters using full-field data. Finally, a study is conducted on the uncertainty propagation of the material parameter.
KW - FEniCS
KW - Hydrogels modeling
KW - Model material parameters identification
KW - Model-order reduction
KW - Proper orthogonal decomposition
KW - RBniCS
KW - Uncertainty propagation
UR - http://www.scopus.com/inward/record.url?scp=85197682314&partnerID=8YFLogxK
U2 - 10.1007/s00466-024-02517-w
DO - 10.1007/s00466-024-02517-w
M3 - Article
AN - SCOPUS:85197682314
VL - 75
SP - 515
EP - 545
JO - Computational mechanics
JF - Computational mechanics
SN - 0178-7675
ER -