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Augmented Tube Models for Blends of Star and Linear Polymers

星形和线性聚合物混合物的增强管模型

基本信息

批准号：
1727870
负责人：
Sachin Shanbhag
金额：
$ 24.62万
依托单位：
Florida State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-05-01 至 2022-04-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1727870&HistoricalAwards=false
关键词：
Augmented Tube Models Blends Star

项目摘要

NONTECHNICAL SUMMARYThis award supports computational research and education into the development of fast and accurate models of flow behavior in polymers. Properties of synthetic polymers can be tuned by engineering their molecular structure. This allows polymers to be used in a variety of applications ranging from packaging to automobiles, and from fabrics to surgical sutures. Polymers are typically processed in the liquid or melt state, where the molecular structure strongly influences flow properties. This relationship between molecular structure and flow properties is often studied using a theory called the tube model. Failures of the standard tube model can be traced to the manner in which it simplifies "multibody interactions" between polymer molecules. These interactions are more accurately described by a simulation model called the slip link model, which is unfortunately computationally costly. This project seeks to combine the strength of tube model (speed) with that of the slip link model (accuracy) through the development of augmented tube models. If successful, this research will provide a template for blending theories and simulations, which is expected have far reaching consequences for materials characterization, even outside the polymer industry.The award also supports the education of minority students at Florida State University by providing modern training in materials modeling, Markov chain Monte Carlo, and statistical analysis. The software and datasets developed by the research team will be made publicly available.TECHNICAL SUMMARYThis award supports computational research and education into the development of fast and accurate models of flow behavior in polymers. The tube model is a popular mean-field theory for entangled polymer melts. Unfortunately, it fails consistently even for relatively simple systems like binary blends of monodisperse polymers with widely separated relaxation times. On the other hand, different slip link models, all of which explicitly treat entanglements between chains as slip links, are remarkably successful for such systems. This is primarily due to the different treatment of a relaxation mechanism called constraint release, in the two models. In slip link models, the description of constraint release is "natural", while it can be complicated, and still inaccurate, in the tube model. A drawback of slip link models is their computational cost. This project seeks to combine the strength of tube models (speed) with that of the slip link models (natural and accurate representation of constraint release). The overarching research objectives are (i) to explore the development of augmented tube models, which combine these strengths, and (ii) to test these augmented models on the computationally intensive inverse problem of inferring the composition of a polymer mixture from experimental measurements of rheology. The award supports the development of a fast slip link model called ecoSLM, and its validation, using binary blends of star and linear polymers. Research activities to service the overall objectives are broken down into four specific tasks: (i) validating the ecoSLM against all available experimental data on linear-linear, star-linear, and star-star blends, (ii) using the validated ecoSLM to map out the design space to help identify regions where the tube model fails, (iii) developing augmented tube model, which uses ecoSLM to guide the tube model to the correct physics/parameters, and (iv) to use these augmented tube models for inverse modeling.The project explores the augmentation of a mean-field theory with a stochastic simulation model, ecoSLM, in order to combine speed with accuracy. If successful, it can provide a template for other problems in materials science that seek to combine a mean-field theory with simple simulation models. Using a Bayesian framework for inverse modeling, this can have far reaching implications for materials characterization, even outside the polymer industry.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项支持计算研究和教育，以开发快速准确的聚合物流动行为模型。合成聚合物的性质可以通过改造其分子结构来调整。这使得聚合物可以用于各种应用，从包装到汽车，从织物到手术缝合线。聚合物通常在液体或熔融状态下加工，其中分子结构强烈影响流动特性。分子结构和流动特性之间的关系通常用一种叫做管模型的理论来研究。标准管模型的失败可以追溯到它简化聚合物分子之间“多体相互作用”的方式。这些相互作用可以通过一种称为滑移连接模型的模拟模型来更准确地描述，不幸的是，这种模型的计算成本很高。本项目旨在通过增强管模型的开发，将管模型的强度（速度）与滑移连接模型的强度（精度）结合起来。如果成功，这项研究将为混合理论和模拟提供一个模板，预计这将对材料表征产生深远的影响，甚至在聚合物工业之外。该奖项还通过提供材料建模、马尔可夫链蒙特卡洛和统计分析方面的现代培训，支持佛罗里达州立大学少数民族学生的教育。研究小组开发的软件和数据集将向公众开放。该奖项支持计算研究和教育，以开发快速准确的聚合物流动行为模型。管模型是一种常用的纠缠聚合物熔体平均场理论。不幸的是，即使是相对简单的体系，比如弛豫时间间隔很大的单分散聚合物的二元共混体系，它也始终失败。另一方面，不同的滑移链模型，所有这些模型都明确地将链之间的缠结视为滑移链，对于这样的系统是非常成功的。这主要是由于在两个模型中对一种称为约束释放的松弛机制的不同处理。在滑移杆模型中，约束释放的描述是“自然的”，而在管模型中，约束释放的描述可能是复杂的，而且仍然是不准确的。滑移连接模型的一个缺点是它们的计算成本。本项目力求将管件模型的强度（速度）与滑移杆模型的强度（约束释放的自然和准确表示）结合起来。总体研究目标是(i)探索结合这些优势的增强管模型的发展，以及（ii）在从流变学实验测量推断聚合物混合物组成的计算密集型逆问题上测试这些增强模型。该合同支持一种名为ecoSLM的快速滑动连接模型的开发及其验证，该模型使用星形和线性聚合物的二元共混物。服务于整体目标的研究活动分为四项具体任务：(i)根据线性-线性、星形-线性和星形-星形混合的所有可用实验数据验证ecoSLM，（ii）使用经过验证的ecoSLM来绘制设计空间，以帮助识别管模型失败的区域，（iii）开发增强管模型，该模型使用ecoSLM来指导管模型获得正确的物理/参数，以及（iv）使用这些增强管模型进行逆建模。该项目探索了随机模拟模型ecoSLM对平均场理论的扩展，以便将速度与准确性结合起来。如果成功，它可以为材料科学中寻求将平均场理论与简单模拟模型相结合的其他问题提供一个模板。使用贝叶斯框架进行逆建模，这可以对材料表征产生深远的影响，甚至在聚合物工业之外。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。