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Magnetohydrodynamics (MHD) simulation via an adaptive element free Galerkin method.

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Title: Magnetohydrodynamics (MHD) simulation via an adaptive element free Galerkin method.
Authors: Jannesari, Zahra1 (AUTHOR), Tatari, Mehdi1 (AUTHOR) mtatari@cc.iut.ac.ir
Source: Engineering with Computers. Feb2022, Vol. 38 Issue 1, p679-693. 15p.
Abstract: In this work, an adaptive element free Galerkin technique is presented to solve magnetohydrodynamics (MHD) equations. An a posteriori error estimation based on gradient recovery is suggested to find locations with largest error contribution. For stabilization, the MHD equations are converted into two convection diffusion equations and these equations are stabilized by adding an artificial diffusion based on local Péclet number. The presented numerical examples show efficiency of the adaptive technique. [ABSTRACT FROM AUTHOR]
Copyright of Engineering with Computers is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: In this work, an adaptive element free Galerkin technique is presented to solve magnetohydrodynamics (MHD) equations. An a posteriori error estimation based on gradient recovery is suggested to find locations with largest error contribution. For stabilization, the MHD equations are converted into two convection diffusion equations and these equations are stabilized by adding an artificial diffusion based on local Péclet number. The presented numerical examples show efficiency of the adaptive technique. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Engineering with Computers is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00366-020-01079-8
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              Text: Feb2022
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