An Efficient PGD Solver for Structural Dynamics Applications
CoRR(2024)
摘要
We propose in this paper a Proper Generalized Decomposition (PGD) solver for
reduced-order modeling of linear elastodynamic problems. It primarily focuses
on enhancing the computational efficiency of a previously introduced PGD solver
based on the Hamiltonian formalism. The novelty of this work lies in the
implementation of a solver that is halfway between Modal Decomposition and the
conventional PGD framework, so as to accelerate the fixed-point iteration
algorithm. Additional procedures such that Aitken's delta-squared process and
mode-orthogonalization are incorporated to ensure convergence and stability of
the algorithm. Numerical results regarding the ROM accuracy, time complexity,
and scalability are provided to demonstrate the performance of the new solver
when applied to dynamic simulation of a three-dimensional structure.
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