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An Efficient Parallel MLPG Method for Poroelastic Models

Authors: BERGAMASCHI, LUCA; MARTINEZ CALOMARDO, ANGELES; PINI, GIORGIO;

An Efficient Parallel MLPG Method for Poroelastic Models

Abstract

A meshless model, based on the Meshless Local Petrov-Galerkin (MLPG) approach, is developed and implemented in parallel for the solution of axi-symmetric poroelastic problems. The parallel code is based on a concurrent construction of the stiffness matrix by the processors and on a parallel preconditioned iterative method of Krylov type for the solution of the resulting linear system. The performance of the code is investigated on a realistic application concerning the prediction of land subsidence above a deep compacting reservoir. The overall code is shown to obtain a very high parallel efficiency (larger than 78% for the solution phase) and it is successfully applied to the solution of a poroelastic problem with a fine discretization which produces a linear system with more than 6 million equations using up to 512 processors on the HPCx supercomputer.

Country
Italy
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Keywords

poroelasticity, parallel computer, preconditioner, meshless method, poroelasticity, preconditioners, parallel computations, scalability, meshless method, meshless methods; poroelasticity; preconditioners; parallel computer; scalability, scalability

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold