The use of limited global information in multiscale simulations is needed when there is no scale separation. Previous approaches entail fine-scale simulations in the computation of the global information. The computation of the global information is expensive. In this paper, we propose the use of approximate global information based on partial upscaling. A requirement for partial homogenization is to capture long-range (non-local) effects present in the fine-scale solution, while homogenizing some of the smallest scales. The local information at these smallest scales is captured in the computation of basis functions. Thus, the proposed approach allows us to avoid the computations at the scales that can be homogenized. This results in coarser problems for the computation of global fields. We analyze the convergence of the proposed method. Mathematical formalism is introduced, which allows estimating the errors due to small scales that are homogenized. The proposed method is applied to simulate two-phase flows in heterogeneous porous media. Numerical results are presented for various permeability fields, including those generated using two-point correlation functions and channelized permeability fields from the SPE Comparative Project (Christie and Blunt, SPE Reserv Evalu Eng 4:308–317, 2001). We consider simple cases where one can identify the scales that can be homogenized. For more general cases, we suggest the use of upscaling on the coarse grid with the size smaller than the target coarse grid where multiscale basis functions are constructed. This intermediate coarse grid renders a partially upscaled solution that contains essential non-local information. Numerical examples demonstrate that the use of approximate global information provides better accuracy than purely local multiscale methods.
机构:
Cent S Univ Forestry & Technol, Inst Math & Phys, Changsha 410004, Hunan, Peoples R ChinaCent S Univ Forestry & Technol, Inst Math & Phys, Changsha 410004, Hunan, Peoples R China
Liu, Xiao-qi
ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, PT III,
2011,
7004
: 186
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194
机构:
S China Normal Univ, Sch Math Sci, Guangzhou 510631, Guangdong, Peoples R ChinaS China Normal Univ, Sch Math Sci, Guangzhou 510631, Guangdong, Peoples R China
Chen, Yanping
Huang, Yunqing
论文数: 0引用数: 0
h-index: 0
机构:
Xiangtan Univ, Hunan Key Lab Computat & Simulat Sci & Engn, Xiangtan 411105, Hunan, Peoples R China
Xiangtan Univ, Sch Math & Computat Sci, Xiangtan 411105, Hunan, Peoples R ChinaS China Normal Univ, Sch Math Sci, Guangzhou 510631, Guangdong, Peoples R China
Huang, Yunqing
Liu, Wenbin
论文数: 0引用数: 0
h-index: 0
机构:
Univ Kent, KBS, Canterbury CT2 7NF, Kent, England
Univ Kent, IMS, Canterbury CT2 7NF, Kent, EnglandS China Normal Univ, Sch Math Sci, Guangzhou 510631, Guangdong, Peoples R China
Liu, Wenbin
Yan, Ningning
论文数: 0引用数: 0
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机构:
Chinese Acad Sci, Acad Math & Syst Sci, Inst Syst Sci, Beijing 100080, Peoples R ChinaS China Normal Univ, Sch Math Sci, Guangzhou 510631, Guangdong, Peoples R China
机构:
Chinese Univ Hong Kong, Dept Math, Hong Kong, Hong Kong, Peoples R ChinaChinese Univ Hong Kong, Dept Math, Hong Kong, Hong Kong, Peoples R China
Chung, Eric T.
Efendiev, Yalchin
论文数: 0引用数: 0
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机构:
Texas A&M Univ, Dept Math, College Stn, TX 77843 USA
King Abdullah Univ Sci & Technol, Numer Porous Media SRI Ctr, Thuwal 239556900, Saudi ArabiaChinese Univ Hong Kong, Dept Math, Hong Kong, Hong Kong, Peoples R China
Efendiev, Yalchin
Leung, Wing Tat
论文数: 0引用数: 0
h-index: 0
机构:
Texas A&M Univ, Dept Math, College Stn, TX 77843 USAChinese Univ Hong Kong, Dept Math, Hong Kong, Hong Kong, Peoples R China
Leung, Wing Tat
Ye, Shuai
论文数: 0引用数: 0
h-index: 0
机构:
Texas A&M Univ, Dept Math, College Stn, TX 77843 USAChinese Univ Hong Kong, Dept Math, Hong Kong, Hong Kong, Peoples R China