Evaluation of the deflated preconditioned CG method to solve bubbly and porous media flow problems on GPU and CPU

2015 ◽  
Vol 80 (11) ◽  
pp. 666-683 ◽  
Author(s):  
R. Gupta ◽  
D. Lukarski ◽  
M. B. van Gijzen ◽  
C. Vuik
Keyword(s):  
Porous Media ◽  
Porous Media Flow ◽  
Flow Problems

scholarly journals Prediction of Discretization of GMsFEM Using Deep Learning

Mathematics ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 412 ◽  
Author(s):  
Min Wang ◽  
Siu Wun Cheung ◽  
Eric T. Chung ◽  
Yalchin Efendiev ◽  
Wing Tat Leung ◽  
...  
Keyword(s):  
Neural Networks ◽  
Porous Media ◽  
Deep Learning ◽  
Multiscale Model ◽  
Porous Media Flow ◽  
Scale Parameters ◽  
Flow Problems ◽  
Local Problems ◽  
Nonlinear Relation ◽  
Model Reduction Technique

In this paper, we propose a deep-learning-based approach to a class of multiscale problems. The generalized multiscale finite element method (GMsFEM) has been proven successful as a model reduction technique of flow problems in heterogeneous and high-contrast porous media. The key ingredients of GMsFEM include mutlsicale basis functions and coarse-scale parameters, which are obtained from solving local problems in each coarse neighborhood. Given a fixed medium, these quantities are precomputed by solving local problems in an offline stage, and result in a reduced-order model. However, these quantities have to be re-computed in case of varying media (various permeability fields). The objective of our work is to use deep learning techniques to mimic the nonlinear relation between the permeability field and the GMsFEM discretizations, and use neural networks to perform fast computation of GMsFEM ingredients repeatedly for a class of media. We provide numerical experiments to investigate the predictive power of neural networks and the usefulness of the resultant multiscale model in solving channelized porous media flow problems.

scholarly journals Fronts in two‐phase porous media flow problems: The effects of hysteresis and dynamic capillarity

2020 ◽  
Vol 144 (4) ◽  
pp. 449-492
Author(s):  
K. Mitra ◽  
T. Köppl ◽  
I. S. Pop ◽  
C. J. van Duijn ◽  
R. Helmig
Keyword(s):  
Porous Media ◽  
Porous Media Flow ◽  
Two Phase ◽  
Flow Problems ◽  
Dynamic Capillarity

scholarly journals An OpenMP implementation of the MuMM solver for porous media flow problems

2020 ◽  
Vol 5 (6) ◽  
Author(s):  
Sérgio Felipe Ferreira Silva ◽  
Hanna Thaina Prates Arimatéia ◽  
Alexandre Santos Francisco ◽  
Weslley Luiz da Silva Assis
Keyword(s):  
Porous Media ◽  
Domain Decomposition ◽  
Iterative Procedure ◽  
Application Programming Interface ◽  
Heterogeneous Porous Media ◽  
Computational Effort ◽  
Multiscale Methods ◽  
Porous Media Flow ◽  
Flow Problems ◽  
Second Order Elliptic Problems

Multiscale methods are usually developed for solving second-order elliptic problems in which coefficients are of multiscale heterogeneous nature. The Multiscale Mixed Method (MuMM) was introduced aiming at the efficient and accurate approximation of large flow problems in highly heterogeneous porous media. In the MuMM numerical solver, first mixed multiscale basis functions are constructed, and next global domain decomposition iterations are performed to compute the discrete solution of the problems. However, this iterative procedure is a time-consuming step. In this paper, the authors improve the MuMM solver through the implementation of parallel computations in the step concerning the global iterative procedure. The parallel version of the solver employs the application programming interface Open Multi-Processing (OpenMP). The implementation with the OpenMP reduces significantly the computational effort to perform the domain decomposition iterations, as indicated by the numerical results.

Convective mixing in homogeneous porous media flow

2017 ◽  
Vol 2 (1) ◽  
Author(s):  
Jia-Hau Ching ◽  
Peilong Chen ◽  
Peichun Amy Tsai
Keyword(s):  
Porous Media ◽  
Porous Media Flow ◽  
Convective Mixing ◽  
Homogeneous Porous Media
Sign in / Sign up

Export Citation Format

Share Document