Academic Commons

Articles

A kd-tree-accelerated hybrid data-driven/model-based approach for poroelasticity problems with multi-fidelity multi-physics data

Bahmani, Bahador; Sun, WaiChing

We present a hybrid model/model-free data-driven approach to solve poroelasticity problems. Extending the data-driven modeling framework originated from Kirchdoerfer and Ortiz (2016), we introduce one model-free and two hybrid model-based/data-driven formulations capable of simulating the coupled diffusion-deformation of fluid-infiltrating porous media with different amounts of available data. To improve the efficiency of the model-free data search, we introduce a distance-minimized algorithm accelerated by a k-dimensional tree search. To handle the different fidelities of the solid elasticity and fluid hydraulic constitutive responses, we introduce a hybridized model in which either the solid and the fluid solver can switch from a model-based to a model-free approach depending on the availability and the properties of the data. Numerical experiments are designed to verify the implementation and compare the performance of the proposed model to other alternatives.

Files

  • thumnail for 1-s2.0-S004578252100205X-main.pdf 1-s2.0-S004578252100205X-main.pdf application/pdf 3.03 MB Download File

Also Published In

Title
Computer Methods in Applied Mechanics and Engineering
DOI
https://doi.org/10.1016/j.cma.2021.113868

More About This Work

Academic Units
Civil Engineering and Engineering Mechanics
Published Here
June 7, 2021