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Constraint preconditioners have proved very efficient for the solution of ill-conditioned finite element (FE) coupled consolidation problems in a sequential computing environment. Their implementation on parallel computers, however, is not straightforward because of their inherent sequentiality. The present paper describes a novel parallel inexact constraint preconditioner (ParICP) for the efficient solution of linear algebraic systems arising from the FE discretization of the coupled poro-elasticity equations. The ParICP implementation is based on the use of the block factorized sparse approximate inverse incomplete Cholesky preconditioner, which is a very recent and effective development for the parallel preconditioning of symmetric positive definite matrices. The ParICP performance is experimented with in real 3D coupled consolidation problems, proving a scalable and efficient implementation of the constraint preconditioning for high-performance computing. ParICP appears to be a very robust algorithm for solving ill-conditioned large-size coupled models in a parallel computing environment. [ABSTRACT FROM AUTHOR]

Abstract: The Finite Element Method (FEM) is widely used in civil and mechanical engineering to simulate the behavior of complex structures and, more specifically, to predict stress and deformation fields of structural parts or mechanical bodies. In the former case, the coupling between different types of elements, such as beams, trusses, and shells, is often required, while in the latter fully 3D discretizations are typically used. For both, FEM leads to symmetric positive definite (SPD) matrices that, depending on the type of discretization and especially on the topology of the nodal connections, may be efficiently solved by either the Preconditioned Conjugate Gradient (PCG) or a direct solver such as the routine MA57 of the Harwell Software Library. Numerical experiments are shown and discussed where the effect of spatial discretization, different solution techniques, and a possible nodal reordering, is explored. The PCG preconditioner used is a variant of the incomplete Cholesky factorization with variable fill-in. It is shown that for structures with 1D or 2D connections, such as for example a bridge, MA57 performs usually better than PCG. In this case it is noted that some reorderings specifically designed and implemented for direct elimination methods can be very helpful for PCG as well as they yield a cheaper preconditioner and lead to a much faster PCG convergence. The main disadvantage is the need for an appropriate degree of fill-in for the preconditioner which turns out to be problem dependent and must be found empirically. However, in fully 3D problems, arising for example from the FE discretization of structural components or geomechanical structures, PCG outperforms MA57 while also requiring much less memory, and thus allowing for the use of much refined grids, if needed. With the aid of a large geomechanical problem it is shown that direct solvers may not be (even) used on serial computers due to their prohibitive computational cost with PCG the only viable alternative solver. [Copyright &y& Elsevier]

Abstract: Contact mechanics can be addressed numerically by finite elements using either a penalty formulation or the Lagrange multipliers. The penalty approach leads to a linearized symmetric positive definite system which can prove severely ill-conditioned, with the iterative solution to large 3D problems requiring expensive preconditioners to accelerate, or even to allow for, convergence. In the present paper a mixed constraint preconditioner (MCP) is developed and implemented for the iterative solution of contact problems based on the penalty approach. A theoretical bound for the eigenspectrum of the preconditioned matrix is provided depending on the user-specified parameters that control the preconditioner fill-in. The performance of MCP combined with the preconditioned conjugate gradient is studied in two real 3D test cases describing the geomechanics of faulted porous media and compared to a well-established preconditioner such as the incomplete Cholesky decomposition ILLT with optimal fill-in. Numerical results show that typically MCP outperforms ILLT especially in the most challenging problems where very large penalty parameters must be used. Finally, a modification aimed at simplifying the MCP implementation and increasing its robustness is developed and experimented with, showing that the modified MCP can be viewed as an efficient and viable alternative to both MCP and ILLT, especially in problems where the size of the contact surfaces is large with respect to the global domain. [Copyright &y& Elsevier]