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引用次数: 0
摘要
本文提出了一种用于线性弹性力学问题降阶建模的适当广义分解(PGD)求解器。它主要侧重于提高之前推出的基于哈密顿形式主义的 PGD 求解器的计算效率。这项工作的新颖之处在于实现了介于模态分解和传统 PGD 框架之间的求解器,从而加速了定点迭代算法。为了确保算法的收敛性和稳定性,还采用了艾特肯三角平方过程和模式正交化等附加程序。本文提供了有关 ROM 精度、时间复杂性和可扩展性的数值结果,以证明新求解器在三维结构动态模拟中的性能。
An efficient PGD solver for structural dynamics applications.
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.
期刊介绍:
The research topics addressed by Advanced Modeling and Simulation in Engineering Sciences (AMSES) cover the vast domain of the advanced modeling and simulation of materials, processes and structures governed by the laws of mechanics. The emphasis is on advanced and innovative modeling approaches and numerical strategies. The main objective is to describe the actual physics of large mechanical systems with complicated geometries as accurately as possible using complex, highly nonlinear and coupled multiphysics and multiscale models, and then to carry out simulations with these complex models as rapidly as possible. In other words, this research revolves around efficient numerical modeling along with model verification and validation. Therefore, the corresponding papers deal with advanced modeling and simulation, efficient optimization, inverse analysis, data-driven computation and simulation-based control. These challenging issues require multidisciplinary efforts – particularly in modeling, numerical analysis and computer science – which are treated in this journal.