Comparison of barrier update strategies for interior point algorithms in single-crystal plasticity

IF 2.2 3区 工程技术 Q2 MECHANICS
Lisa Scheunemann, Felix Steinmetz, Paulo Nigro
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引用次数: 0

Abstract

This contribution discusses the influence of different barrier update strategies on the performance and robustness of an interior point algorithm for single-crystal plasticity at small strains. To this end, single-crystal plasticity is first briefly presented in the framework of a primal-dual interior point algorithm to outline the general algorithmic structure. The manner in which the barrier parameter is modified within the interior point method, steering the penalization of constraints, plays a crucial role for the robustness and efficiency of the overall algorithm. In this paper, we compare and analyze different strategies in the framework of crystal plasticity. In a thorough analysis of a numerical example covering a broad range of settings in monocrystals, we investigate robust hyperparameter ranges and identify the most efficient and robust barrier parameter update strategies.

Abstract Image

单晶塑性中内点算法的障碍更新策略比较
本文讨论了不同的势垒更新策略对小应变下单晶塑性内点算法的性能和鲁棒性的影响。为此,本文首先简要介绍了原始二元内点算法框架下的单晶塑性,以概述一般算法结构。在内含点算法中,如何修改障碍参数,引导约束条件的惩罚,对整个算法的稳健性和效率起着至关重要的作用。本文比较和分析了晶体塑性框架下的不同策略。通过对涵盖单晶体广泛设置的数值示例进行深入分析,我们研究了稳健超参数范围,并确定了最高效、最稳健的势垒参数更新策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.40
自引率
10.70%
发文量
234
审稿时长
4-8 weeks
期刊介绍: Archive of Applied Mechanics serves as a platform to communicate original research of scholarly value in all branches of theoretical and applied mechanics, i.e., in solid and fluid mechanics, dynamics and vibrations. It focuses on continuum mechanics in general, structural mechanics, biomechanics, micro- and nano-mechanics as well as hydrodynamics. In particular, the following topics are emphasised: thermodynamics of materials, material modeling, multi-physics, mechanical properties of materials, homogenisation, phase transitions, fracture and damage mechanics, vibration, wave propagation experimental mechanics as well as machine learning techniques in the context of applied mechanics.
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