Computational Study for Optimizing Microstructures and Properties of Polymer-Matrix Magnetostrictive Composite Materials

聚合物基磁致伸缩复合材料微观结构和性能优化的计算研究

• 批准号: 0968792
• 负责人: Yu Wang
• 金额: $ 20.7万
• 依托单位: Michigan Technological University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-10 至 2012-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0968792&HistoricalAwards=false
• 关键词: Computational; Study; Optimizing; Microstructures; Properties

TECHNICAL SUMMARY:This award supports theoretical and computational research and education on composite magnetostrictive materials. The PI will use phase field materials modeling methods to study the properties and microstructure of polymer matrix magnetostrictive composites. The work may have impact on the processing of magnetostrictive composite materials. This computational research focuses on technologically important composites that are composed of giant magnetostrictive Terfenol-D particles embedded in an epoxy resin matrix. The properties of these systems, in a cured epoxy resin, are studied by assuming a free-energy of the magnetostrictive composite system that includes the magnetocrystalline anisotropy energy, the domain-wall energy, the long-range magnetostatic dipolar interactions, the interactions of the magnetic dipoles with an external magnetic field, and the magneto elastic energy. The temporal evolution of the magnetization is determined by solving the Landau-Lifshitz-Gilbert equation. A similar technique is used to determine strategies for field-optimized assembly and control of the Terfenol-D nanoparticles in the uncured epoxy resin. In this case short-range interactions, which account for viscous drag on each particle, are included which ultimately provides a force and torque on each of the nanoparticles. Understanding such materials furthers technologies aimed at the development and application of magnetic sensors, actuators, and transducers. Polymer-bonded Terfenol-D composites significantly increase the electrical resistivity, reduce eddy current loss, improve mechanical toughness and tensile strength, and provide magnetostrictive strains comparable to that of monolithic Terfenol-D alloy, and extend the operational bandwidth. A thrust of this research is a detailed understanding of the connection between materials properties of magnetostrictive composites and microstructure and aims to address how microstructure can be controlled. This computational research complements and is closely related to a large body of experimental findings in both monolithic Terfenol-D and polymer matrix magnetostrictive composites, which together enable effective computer-aided design and fabrication of the composites. NON-TECHNICAL SUMMARYThis award supports theoretical and computational research on the magnetic properties of a class of polymer-matrix composite materials with an aim to understanding the relationship between the structure of the composite and its properties, and how they can be controlled. Polymer-matrix composites are important materials, composed of magnetic particles embedded in an epoxy resin, that offer a variety of advantages over crystalline materials composed of the same magnetic material. They are less susceptible to degradation and mechanical failure but still respond to applied magnetic fields in a way that is comparable to conventional crystalline materials. This research has potential impact on sensor and transducer technologies and will also develop and distribute computational tools for computational materials design and fabrication of advanced magnetostrictive composites. The research is integrated with educational activities that train future computational materials scientists, develops instructional materials, and distributes free source codes to the larger community of scientists in this field. Outreach activities to high school students and teachers are also included in this effort.

技术概述：该奖项支持复合磁致伸缩材料的理论和计算研究和教育。PI将使用相场材料建模方法研究聚合物基磁致伸缩复合材料的性能和微观结构。该工作可能对磁致伸缩复合材料的加工产生影响。这项计算研究的重点是技术上重要的复合材料，这些复合材料是由嵌入在环氧树脂基体中的巨磁致伸缩Terfenol-D颗粒组成的。通过假设磁致伸缩复合体系的自由能，包括磁晶各向异性能、畴壁能、远程静磁偶极相互作用、磁偶极子与外磁场的相互作用和磁弹性能，研究了固化环氧树脂中这些体系的性能。通过求解Landau-Lifshitz-Gilbert方程确定了磁化强度的时间演化。类似的技术也用于确定Terfenol-D纳米颗粒在未固化环氧树脂中的现场优化组装和控制策略。在这种情况下，考虑到每个颗粒上的粘性阻力的短程相互作用，最终为每个纳米颗粒提供了力和扭矩。了解这些材料可以进一步促进磁传感器、执行器和换能器的开发和应用。聚合物结合的Terfenol-D复合材料显著提高了电阻率，减少了涡流损耗，提高了机械韧性和抗拉强度，并提供了与单片Terfenol-D合金相当的磁致伸缩应变，并延长了工作带宽。本研究的重点是详细了解磁致伸缩复合材料性能与微观结构之间的联系，并旨在解决如何控制微观结构的问题。这项计算研究补充并密切相关于在单片Terfenol-D和聚合物基磁致伸缩复合材料中的大量实验发现，它们共同实现了复合材料的有效计算机辅助设计和制造。该奖项支持对一类聚合物基复合材料的磁性进行理论和计算研究，旨在了解复合材料的结构与其性能之间的关系，以及如何控制它们。聚合物基复合材料是一种重要的材料，由嵌入环氧树脂中的磁性颗粒组成，与由相同磁性材料组成的晶体材料相比，具有多种优势。它们不易受到降解和机械故障的影响，但仍然以与传统晶体材料相当的方式响应施加的磁场。这项研究对传感器和换能器技术有潜在的影响，也将开发和分发计算工具，用于先进磁致伸缩复合材料的计算材料设计和制造。这项研究与教育活动相结合，培训未来的计算材料科学家，开发教学材料，并向该领域更大的科学家社区分发免费源代码。这项工作还包括向高中学生和教师开展外展活动。