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International Journal for Multiscale Computational Engineering

Published 6 issues per year

ISSN Print: 1543-1649

ISSN Online: 1940-4352

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.4 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.3 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 2.2 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00034 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.46 SJR: 0.333 SNIP: 0.606 CiteScore™:: 3.1 H-Index: 31

Indexed in

NONLINEAR NONLOCAL MULTICONTINUA UPSCALING FRAMEWORK AND ITS APPLICATIONS

Volume 16, Issue 5, 2018, pp. 487-507
DOI: 10.1615/IntJMultCompEng.2018027832
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ABSTRACT

We discuss multiscale methods for nonlinear problems by extending recently developed multiscale concepts for linear problems. The main idea of these approaches is to use local constraints and solve problems in oversampled regions for constructing macroscopic equations. These techniques are intended for problems without scale separation and high contrast, which often occur in applications. For linear problems, the local solutions with constraints are used as basis functions. This technique is called Constraint Energy Minimizing Generalized Multiscale Finite Element Method (CEM-GMsFEM). GMsFEM identifies macroscopic quantities based on rigorous analysis. In corresponding upscaling methods, the multiscale basis functions are selected such that the degrees of freedom have physical meanings, such as averages of the solution on each continuum. This paper extends the linear concepts to local nonlinear problems. The main concept consists of: (1) identifying macroscopic quantities; (2) constructing appropriate oversampled local problems with coarse-grid constraints; (3) formulating macroscopic equations. We consider two types of approaches. In the first approach, the solutions of local problems are used as basis functions (in a linear fashion) to solve nonlinear problems. This approach is simple to implement; however, it lacks the nonlinear interpolation, which we present in our second approach. In this approach, the local solutions are used as a nonlinear forward map from local averages (constraints) of the solution in oversampling region. This local fine-grid solution is further used to formulate the coarse-grid problem. Both approaches are discussed on several examples and applied to single-phase and two-phase flow problems, which are challenging because of convection-dominated nature of the concentration equation. The numerical results show that we can achieve good accuracy using our new concepts for these complex problems.

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  1. Wang Yating, Cheung Siu Wun, Chung Eric T., Efendiev Yalchin, Wang Min, Deep multiscale model learning, Journal of Computational Physics, 406, 2020. Crossref

  2. Jiang Lijian, Li Mengnan, Model reduction for nonlinear multiscale parabolic problems using dynamic mode decomposition, International Journal for Numerical Methods in Engineering, 121, 16, 2020. Crossref

  3. Vasilyeva Maria, Leung Wing T., Chung Eric T., Efendiev Yalchin, Wheeler Mary, Learning macroscopic parameters in nonlinear multiscale simulations using nonlocal multicontinua upscaling techniques, Journal of Computational Physics, 412, 2020. Crossref

  4. Fu Shubin, Chung Eric, Mai Tina, Constraint energy minimizing generalized multiscale finite element method for nonlinear poroelasticity and elasticity, Journal of Computational Physics, 417, 2020. Crossref

  5. Bessaih Hakima, Maris Razvan Florian, Stochastic homogenization of multicontinuum heterogeneous flows, Journal of Computational and Applied Mathematics, 374, 2020. Crossref

  6. Chung Eric T., Efendiev Yalchin, Leung Wing T., Vasilyeva Maria, Nonlocal multicontinua with representative volume elements. Bridging separable and non-separable scales, Computer Methods in Applied Mechanics and Engineering, 377, 2021. Crossref

  7. Li Mengnan, Jiang Lijian, Deep learning nonlinear multiscale dynamic problems using Koopman operator, Journal of Computational Physics, 446, 2021. Crossref

  8. Chen Shi, Li Qin, Lu Jianfeng, Wright Stephen J., Manifold Learning and Nonlinear Homogenization, Multiscale Modeling & Simulation, 20, 3, 2022. Crossref

  9. Efendiev Yalchin, Leung Wing Tat, Multicontinuum homogenization and its relation to nonlocal multicontinuum theories, Journal of Computational Physics, 474, 2023. Crossref

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