arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2602.08089cond-mat.softcond-mat.mtrl-sci
收藏

从连通性到破裂:一种粗粒化随机网络动力学方法用于聚合物网络力学

From Connectivity to Rupture: A Coarse-Grained Stochastic Network Dynamics Approach to Polymer Network Mechanics

Stanford University(斯坦福大学)

Shaswat Mohanty, Wei Cai

AI总结:

本文提出了一种粗粒化随机网络动力学方法,用于研究聚合物网络的变形与破裂机制,展示了其在计算效率和物理解释上的优势。

摘要:

我们介绍了一种粗粒化随机网络动力学(CGSND)框架,用于建模聚合物网络的变形和破裂。该方法用网络层面的演化规则取代显式分子动力学(MD)或粗粒化分子动力学(CGMD),同时保留链熵弹性和力控键断裂。在单轴加载下,CGSND再现了弹性网络的特征非线性应力-伸长响应,包括明确的极限拉伸强度和由于逐步键断裂导致的峰值后软化。与粗粒化分子动力学(CGMD)模拟相比,CGSND尽管其速率无关的公式,仍能捕捉应力响应的定性形式和灾难性破坏的起始。对断裂动力学的分析揭示了两种方法中在极限拉伸强度附近键断裂危险率出现显著峰值。此外,断裂段长度的分布在统计上与初始网络无区别,表明断裂不偏向于短或长链。最后,键力大小的基尼系数的演变揭示了破坏前的强力局部化。这些结果表明,CGSND提供了一种计算高效且物理可解释的框架,用于将力局部化和断裂动力学连接到聚合物网络的宏观破坏。

英文摘要:

We introduce a coarse-grained stochastic network dynamics (CGSND) framework for modeling deformation and rupture in polymer networks. The method replaces explicit molecular dynamics (MD) or coarse-grained molecular dynamics (CGMD) with network-level evolution rules while retaining chain entropic elasticity and force-controlled bond failure. Under uniaxial loading, CGSND reproduces the characteristic nonlinear stress--stretch response of elastomeric networks, including a well-defined ultimate tensile strength and post-peak softening due to progressive bond rupture. Comparison with coarse-grained molecular dynamics (CGMD) simulations shows that CGSND captures the qualitative form of the stress response and the onset of catastrophic damage despite its rate-independent formulation. Analysis of rupture kinetics reveals a pronounced peak in the bond-breaking hazard rate near the ultimate tensile strength in both approaches. In addition, the distribution of broken segment lengths remains statistically indistinguishable from the initial network, indicating that rupture is not biased toward short or long chains. Finally, the evolution of the Gini coefficient of bond force magnitudes reveals strong force localization preceding failure. These results demonstrate that CGSND provides a computationally efficient and physically interpretable framework for connecting force localization and rupture kinetics to macroscopic failure in polymer networks.

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。

↑