Improving eigenpairs of automated multilevel substructuring with subspace iterations

This paper improves the eigenpair approximations obtained from the automated multilevel substructuring (AMLS) method by subspace iterations. Two variants of AMLS hybrid Subspace Iteration Method (AMLS-SIMa and AMLS-SIMb) are proposed. AMLS-SIMa is a derivative of the basic subspace iteration by utilizing the AMLS approximations as initial vectors. AMLS-SIMb further takes advantage of the AMLS transformed block diagonal stiffness matrix to avoid factorization of the original stiffness matrix. Numerical experiments show that: (a) the error of AMLS approximate eigenpairs can be significantly reduced with just a few iteration steps; (b) AMLS-SIMb is more efficient than AMLS-SIMa with less execution time.

► The error of AMLS eigenvector approximations is reported. ► Two subspace iteration methods (AMLS-SIMa and AMLS-SIMb) are proposed to improve the standard AMLS. ► The error of AMLS eigenvector approximations is greatly reduced with a few subspace iterations. ► AMLS-SIMb is more efficient than AMLS-SIMa with less execution time. ► AMLS-SIMa and AMLS-SIMb are compared with the basic subspace iteration method (BSIM) and an improve subspace iteration method (ISIM).