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arXiv 1902.01715math.NAcs.NA
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一种用于结构力学的鲁棒自适应代数多重网格线性求解器

A robust adaptive algebraic multigrid linear solver for structural mechanics

Stanford University(斯坦福大学);University of Padova(帕多瓦大学);M 3 E s.r.l.(M 3 E 有限责任公司)

Andrea Franceschini, Victor A. Paludetto Magri, Gianluca Mazzucco, Nicolò Spiezia, Carlo Janna

AI总结:

本文提出了一种自适应的代数多重网格方法,用于解决结构力学中的大规模线性系统问题,通过数值实验验证了该方法在求解具有数百万未知数的实际应用中的效率和鲁棒性。

摘要:

通过有限元方法进行结构力学应用的数值模拟通常需要求解大规模且病态的线性系统,特别是当需要精确结果用于通过低阶函数插值导出的变量(如应力或变形场)时。此类任务是商业模拟器中最耗时的内核;因此,开发鲁棒且高效的线性求解器具有重要价值。在这一背景下,基于LU分解技术的直接求解器因其鲁棒性和易于设置而常被使用;然而,它们只能达到超线性复杂度,在最佳情况下也有限制,取决于问题规模。另一方面,基于代数多重网格(AMG)预条件的迭代求解器可以达到线性复杂度,适用于足够规则的问题,但并不总能收敛,且需要用户更多知识才能高效设置。在本文中,我们提出了一种自适应AMG方法,专门设计以提高其在求解结构问题中的可用性和效率。我们对几个具有数百万未知数的实际应用进行了数值实验,并将我们的方法与两种最先进的线性求解器进行比较,证明了其效率和鲁棒性。

英文摘要:

The numerical simulation of structural mechanics applications via finite elements usually requires the solution of large-size and ill-conditioned linear systems, especially when accurate results are sought for derived variables interpolated with lower order functions, like stress or deformation fields. Such task represents the most time-consuming kernel in commercial simulators; thus, it is of significant interest the development of robust and efficient linear solvers for such applications. In this context, direct solvers, which are based on LU factorization techniques, are often used due to their robustness and easy setup; however, they can reach only superlinear complexity, in the best case, thus, have limited applicability depending on the problem size. On the other hand, iterative solvers based on algebraic multigrid (AMG) preconditioners can reach up to linear complexity for sufficiently regular problems but do not always converge and require more knowledge from the user for an efficient setup. In this work, we present an adaptive AMG method specifically designed to improve its usability and efficiency in the solution of structural problems. We show numerical results for several practical applications with millions of unknowns and compare our method with two state-of-the-art linear solvers proving its efficiency and robustness.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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