Improved parallel preconditioners for multidisciplinary topology optimisations

Date

2016-01-01

Author

AKAY, HASAN UMUR
Oktay, E.
Manguoğlu, Murat
Sivas, A. A.

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Two commonly used preconditioners were evaluated for parallel solution of linear systems of equations with high condition numbers. The test cases were derived from topology optimisation applications in multiple disciplines, where the material distribution finite element methods were used. Because in this optimisation method, the equations rapidly become ill-conditioned due to disappearance of large number of elements from the design space as the optimisations progresses, it is shown that the choice for a suitable preconditioner becomes very crucial. In an earlier work the conjugate gradient (CG) method with a Block-Jacobi preconditioner was used, in which the number of CG iterations increased rapidly with the increasing number processors. Consequently, the parallel scalability of the method deteriorated fast due to the increasing loss of interprocessor information among the increased number of processors. By replacing the Block-Jacobi preconditioner with a sparse approximate inverse preconditioner, it is shown that the number of iterations to converge became independent of the number of processors. Therefore, the parallel scalability is improved.

Subject Keywords

Topology optimisation, Parallel methods, Parallel scalability, Iterative solvers, Preconditioners, Conjugate gradient, Block-Jacobi, Sparse approximate inverse

URI

https://hdl.handle.net/11511/48461

Journal

INTERNATIONAL JOURNAL OF COMPUTATIONAL FLUID DYNAMICS

DOI

https://doi.org/10.1080/10618562.2016.1205737

Collections

Department of Computer Engineering, Article

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Citation Formats

H. U. AKAY, E. Oktay, M. Manguoğlu, and A. A. Sivas, “Improved parallel preconditioners for multidisciplinary topology optimisations,” INTERNATIONAL JOURNAL OF COMPUTATIONAL FLUID DYNAMICS, pp. 329–336, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48461.