Preconditioned Explicit Decoupled Group Methods For Solving Elliptic Partial Differential Equations

dc.contributor.authorSaeed Ahmed, Abdulkafi Mohammed
dc.date.accessioned2019-08-28T07:45:23Z
dc.date.available2019-08-28T07:45:23Z
dc.date.issued2011-04
dc.description.abstractThe highly concern development of finite difference methods was stimulated by the need to cope with today’s complex problems in science and technology. The current requirement for faster solutions and for solving large size problems arises in a variety of applications in science, such as modeling, simulation of large systems and fluid dynamics. Therefore, studies regarding several accelerated techniques have been carried out to achieve these requirements. There are several discretisation techniques that can be used to construct approximation equations for approximating partial differential equations (PDEs) such as finite difference, finite element and finite volume. These approximation equations will be used to generate the corresponding systems of linear equations which are normally large and sparse. The iterative methods are more efficient compared to the other methods since the storage space required for iterative solutions on a computer is less when the coefficient matrix of the system is sparse. Group explicit iterative methods based on the rotated finite difference approximations have been shown to be much faster than the methods based on the standard five-point formula in solving PDEs which are due to the formers’ overall lower computational complexities. There are some new alternative approaches towards increasing the rate of convergence in solving large linear system resulting from the discretisation of these methods.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/8707
dc.language.isoenen_US
dc.publisherUniversiti Sains Malaysiaen_US
dc.subjectPreconditioned Explicit Decoupleden_US
dc.subjectElliptic Partialen_US
dc.titlePreconditioned Explicit Decoupled Group Methods For Solving Elliptic Partial Differential Equationsen_US
dc.typeThesisen_US
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