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Applications of Data Assimilation Methods on a Coupled Dual Porosity Stokes Model

Porous media and conduit coupled systems are heavily used in a variety of areas such as groundwater system, petroleum extraction, and biochemical transport. A coupled dual porosity Stokes model has been proposed to simulate the fluid flow in a dual-porosity media and conduits coupled system. Data as...

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Detalles Bibliográficos
Autores principales: Hu, Xiukun, Douglas, Craig C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304736/
http://dx.doi.org/10.1007/978-3-030-50433-5_6
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author Hu, Xiukun
Douglas, Craig C.
author_facet Hu, Xiukun
Douglas, Craig C.
author_sort Hu, Xiukun
collection PubMed
description Porous media and conduit coupled systems are heavily used in a variety of areas such as groundwater system, petroleum extraction, and biochemical transport. A coupled dual porosity Stokes model has been proposed to simulate the fluid flow in a dual-porosity media and conduits coupled system. Data assimilation is the discipline that studies the combination of mathematical models and observations. It can improve the accuracy of mathematical models by incorporating data, but also brings challenges by increasing complexity and computational cost. In this paper, we study the application of data assimilation methods to the coupled dual porosity Stokes model. We give a brief introduction to the coupled model and examine the performance of different data assimilation methods on a finite element implementation of the coupled dual porosity Stokes system. We also study how observations on different variables of the system affect the data assimilation process.
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spelling pubmed-73047362020-06-22 Applications of Data Assimilation Methods on a Coupled Dual Porosity Stokes Model Hu, Xiukun Douglas, Craig C. Computational Science – ICCS 2020 Article Porous media and conduit coupled systems are heavily used in a variety of areas such as groundwater system, petroleum extraction, and biochemical transport. A coupled dual porosity Stokes model has been proposed to simulate the fluid flow in a dual-porosity media and conduits coupled system. Data assimilation is the discipline that studies the combination of mathematical models and observations. It can improve the accuracy of mathematical models by incorporating data, but also brings challenges by increasing complexity and computational cost. In this paper, we study the application of data assimilation methods to the coupled dual porosity Stokes model. We give a brief introduction to the coupled model and examine the performance of different data assimilation methods on a finite element implementation of the coupled dual porosity Stokes system. We also study how observations on different variables of the system affect the data assimilation process. 2020-05-25 /pmc/articles/PMC7304736/ http://dx.doi.org/10.1007/978-3-030-50433-5_6 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Hu, Xiukun
Douglas, Craig C.
Applications of Data Assimilation Methods on a Coupled Dual Porosity Stokes Model
title Applications of Data Assimilation Methods on a Coupled Dual Porosity Stokes Model
title_full Applications of Data Assimilation Methods on a Coupled Dual Porosity Stokes Model
title_fullStr Applications of Data Assimilation Methods on a Coupled Dual Porosity Stokes Model
title_full_unstemmed Applications of Data Assimilation Methods on a Coupled Dual Porosity Stokes Model
title_short Applications of Data Assimilation Methods on a Coupled Dual Porosity Stokes Model
title_sort applications of data assimilation methods on a coupled dual porosity stokes model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304736/
http://dx.doi.org/10.1007/978-3-030-50433-5_6
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