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arXiv 1801.06016math.NAcs.NA
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关于提高高度非线性弹性问题数值收敛性的方法

On improving the numerical convergence of highly nonlinear elasticity problems

Swansea University(斯旺西大学);Pontificia Universidad Católica de Chile(智利天主教 Pontificia 大学)

Yue Mei, Daniel E. Hurtado, Sanjay Pant, Ankush Aggarwal

AI总结:

本文提出一种新方法,通过转换离散化控制方程以降低非线性,提升牛顿求解器的收敛性,尤其在大载荷步时表现显著。

摘要:

有限弹性问题通常包含材料和几何非线性,并通过各种数值方法求解。然而,对于高度非线性问题,实现收敛较为困难,需要小的载荷步长。本文提出了一种新方法,将离散化控制方程转换,使转换后的问题非线性显著降低,因此牛顿求解器表现出改进的收敛特性。我们研究了软组织中的指数型非线性和压缩中的几何非线性,并为这两种问题提出了新的公式。我们在多个数值示例中测试了新公式,并展示了收敛所需迭代次数的显著减少,尤其是在大载荷步时。值得注意的是,所提出的公式即使在10到100倍更大的载荷步下也能产生收敛解。所提出的框架是通用的,也可应用于其他类型的非线性。

英文摘要:

Finite elasticity problems commonly include material and geometric nonlinearities and are solved using various numerical methods. However, for highly nonlinear problems, achieving convergence is relatively difficult and requires small load step sizes. In this work, we present a new method to transform the discretized governing equations so that the transformed problem has significantly reduced nonlinearity and, therefore, Newton solvers exhibit improved convergence properties. We study exponential-type nonlinearity in soft tissues and geometric nonlinearity in compression, and propose novel formulations for the two problems. We test the new formulations in several numerical examples and show significant reduction in iterations required for convergence, especially at large load steps. Notably, the proposed formulation is capable of yielding convergent solution even when 10 to 100 times larger load steps are applied. The proposed framework is generic and can be applied to other types of nonlinearities as well.

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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