Flow-Induced Disentanglement in Shear and Elongational Flows of Entangled Polymers: A Hi-Fidelity Molecular Simulation Study

缠结聚合物剪切和拉伸流中的流动诱导解缠结：高保真分子模拟研究

• 批准号: 1602890
• 负责人: Brian Edwards
• 金额: $ 30万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1602890&HistoricalAwards=false
• 关键词: Flow; Induced; Disentanglement; Shear; Elongational

PI: Edwards, BrianProposal Number: 1602890The goal of the proposed research is to understand the behavior of macromolecules (like polymers or proteins) and the way that these macromolecules get entangled and disentangled when the fluid velocity is high. While this is a fundamental problem that can be answered by observations at the molecular scale, it can have significant macroscopic effects that are important during the processing of fluids common in the petrochemical and the food industry, e.g., polymers, surfactants, and liquid crystals.Many theories have been proposed to explain the microstructural responses of these complex liquids under flow, but each invariably diverged from experiment at high strain rates. Recent evidence suggests that part of the reason for these divergences is that most flow models track bulk-average properties that have effectively discarded the short-timescale dynamical phenomena of the individual molecules. It has recently been observed via nonequilibrium molecular dynamics (NEMD) simulations of moderately-entangled polyethylene liquids that a remarkable dynamical response occurs at high strain rates in both shear and elongational flows: the polymeric liquid experiences a dramatic decrease in the number of chain entanglements, which leads to a network of highly-stretched chains that form effective tube-like structures through which neighboring chains experience anisotropic diffusion. In shear dominated flows, this ultimately leads to chain rotation and retraction cycles, which give rise to characteristic timescales that are much shorter than the reptation time of the liquid. This project will study this behavior using an unprecedented suite of NEMD and Dissipative Particle Dynamics (DPD) simulations of polyethylene liquids with up to 50 entanglements per chain. In addition, the knowledge gained will be harvested to develop a mesoscopic anisotropic diffusion model that can be applied to high strain-rate flows. Results of the proposed work could contribute to more efficient modeling of existing processes and to the design of new fluid materials, or the processes to manufacture them. Data resulting from this work will become available to the research community through the PolyHub storage site. There are also outreach and education activities planned that involve the development of educational modules.

PI: Edwards， brian提案号：1602890提议研究的目标是了解大分子（如聚合物或蛋白质）的行为，以及当流体速度高时这些大分子纠缠和解开的方式。虽然这是一个可以通过在分子尺度上的观察来回答的基本问题，但它可能具有重要的宏观影响，这在石油化工和食品工业中常见的流体加工过程中很重要，例如聚合物、表面活性剂和液晶。人们提出了许多理论来解释这些复杂液体在流动下的微观结构响应，但在高应变率下，每种理论都与实验结果相背离。最近的证据表明，造成这些分歧的部分原因是，大多数流动模型追踪的是体积平均特性，这有效地抛弃了单个分子的短时间尺度动力学现象。最近，通过对中等纠缠聚乙烯液体的非平衡分子动力学（NEMD）模拟观察到，在剪切和拉伸流动中，在高应变率下，聚合物液体的链纠缠数量急剧减少，导致高度拉伸的链网络形成有效的管状结构，相邻链通过该网络经历各向异性扩散。在剪切主导的流动中，这最终导致链的旋转和收缩循环，从而产生比液体重复时间短得多的特征时间尺度。该项目将使用一套前所未有的NEMD和耗散粒子动力学（DPD）模拟来研究这种行为，每条链最多有50个缠结。此外，所获得的知识将用于开发可应用于高应变速率流动的介观各向异性扩散模型。拟议工作的结果可能有助于对现有工艺进行更有效的建模，并有助于设计新的流体材料或制造它们的工艺。这项工作产生的数据将通过PolyHub存储站点提供给研究社区。此外，还计划开展外联和教育活动，其中包括制定教育模块。