Details
| Original language | English |
|---|---|
| Pages (from-to) | 57-79 |
| Number of pages | 23 |
| Journal | International Journal for Multiscale Computational Engineering |
| Volume | 20 |
| Issue number | 3 |
| Publication status | Published - 2022 |
Abstract
In this work, we are interested in parameter estimation in fractured media using Bayesian inversion. Therein, to reduce the computational costs of the forward model, a nonintrusive global–local approach is employed, rather than using fine-scale high-fidelity simulations. The crack propagates within the local region, and a linearized coarse model is employed in the global region. Here, a predictor–corrector mesh refinement approach is adopted, in which the local domain is dynamically adjusted to the current fracture state. Both subdomains change during the fluid injection time. Our algorithmic developments are substantiated with some numerical tests using phase-field descriptions of hydraulic fractures. The obtained results indicate that the global-local approach is an efficient technique for Bayesian inversion. It has the same accuracy as the full approach; however, the computational time is significantly lower.
Keywords
- Bayesian inversion, global-local, hydraulic fractures, multiscale, phase-field, porous media
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Computational Mechanics
- Computer Science(all)
- Computer Networks and Communications
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In: International Journal for Multiscale Computational Engineering, Vol. 20, No. 3, 2022, p. 57-79.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - BAYESIAN INVERSION USING GLOBAL-LOCAL FORWARD MODELS APPLIED TO FRACTURE PROPAGATION IN POROUS MEDIA
AU - Noii, Nima
AU - Khodadadian, Amirreza
AU - Wick, Thomas
N1 - Funding Information: Noii was founded by the Priority Program DFG SPP 2020 that is, Cyclical Damage Processes in High-Performance Concrete, within its second funding phase. Wick has been supported by the German Research Foundation, Priority Program 1962 (DFG SPP 1962) within the subproject, Optimizing Fracture Propagation Using a Phase-Field Approach, with the Project No. 314067056.
PY - 2022
Y1 - 2022
N2 - In this work, we are interested in parameter estimation in fractured media using Bayesian inversion. Therein, to reduce the computational costs of the forward model, a nonintrusive global–local approach is employed, rather than using fine-scale high-fidelity simulations. The crack propagates within the local region, and a linearized coarse model is employed in the global region. Here, a predictor–corrector mesh refinement approach is adopted, in which the local domain is dynamically adjusted to the current fracture state. Both subdomains change during the fluid injection time. Our algorithmic developments are substantiated with some numerical tests using phase-field descriptions of hydraulic fractures. The obtained results indicate that the global-local approach is an efficient technique for Bayesian inversion. It has the same accuracy as the full approach; however, the computational time is significantly lower.
AB - In this work, we are interested in parameter estimation in fractured media using Bayesian inversion. Therein, to reduce the computational costs of the forward model, a nonintrusive global–local approach is employed, rather than using fine-scale high-fidelity simulations. The crack propagates within the local region, and a linearized coarse model is employed in the global region. Here, a predictor–corrector mesh refinement approach is adopted, in which the local domain is dynamically adjusted to the current fracture state. Both subdomains change during the fluid injection time. Our algorithmic developments are substantiated with some numerical tests using phase-field descriptions of hydraulic fractures. The obtained results indicate that the global-local approach is an efficient technique for Bayesian inversion. It has the same accuracy as the full approach; however, the computational time is significantly lower.
KW - Bayesian inversion
KW - global-local
KW - hydraulic fractures
KW - multiscale
KW - phase-field
KW - porous media
UR - http://www.scopus.com/inward/record.url?scp=85131529917&partnerID=8YFLogxK
U2 - 10.1615/IntJMultCompEng.2021039958
DO - 10.1615/IntJMultCompEng.2021039958
M3 - Article
AN - SCOPUS:85131529917
VL - 20
SP - 57
EP - 79
JO - International Journal for Multiscale Computational Engineering
JF - International Journal for Multiscale Computational Engineering
SN - 1543-1649
IS - 3
ER -