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A METHOD OF DYNAMIC MESH ADAPTATION
Author(s) -
DRAKE R.,
MANORANJAN V. S.
Publication year - 1996
Publication title -
international journal for numerical methods in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 168
eISSN - 1097-0207
pISSN - 0029-5981
DOI - 10.1002/(sici)1097-0207(19960330)39:6<939::aid-nme888>3.0.co;2-a
Subject(s) - speedup , computer science , grid , overhead (engineering) , partial differential equation , dimension (graph theory) , finite element method , action (physics) , reduction (mathematics) , domain (mathematical analysis) , algorithm , adaptation (eye) , code (set theory) , parallel computing , computational science , mathematical optimization , mathematics , set (abstract data type) , engineering , mathematical analysis , physics , geometry , structural engineering , quantum mechanics , pure mathematics , programming language , operating system , optics
Dynamic mesh adaptation is a very useful technique for reducing the computational time and memory requirements when solving evolutionary partial differential equations. The reduction is greater when the solution exhibits localized behaviour as in the case of a moving front where the ‘action’ occurs over a small fraction of the domain. Difficulties arising in the use of dynamic grid adaptation include significant overhead, added storage, and errors introduced due to grid manipulation. We propose a method that maintains a fine uniform grid in the important regions of the domain by using inexpensive action indicators to trigger selective refinement. The method is simple to code and adapt to existing finite element solvers. It requires low added storage and overhead per element, and can significantly reduce grid manipulation errors. We present the selective refinement method and its use of the solution in the gridding decision process, and detail the streamlined storage structure. Theoretical speedup compared to fixed grid methods is derived and the improvement in storage is analysed. Finally, we use the method to solve some problems exhibiting localized behaviour in one dimension and compare to theory.
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