CAREER: Branch-Following and Bifurcation Methods to Identify Active Materials for Tomorrow's Sensors and Actuators

职业：分支跟随和分叉方法来识别未来传感器和执行器的活性材料

• 批准号: 0746628
• 负责人: Ryan Elliott
• 金额: $ 40.23万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0746628&HistoricalAwards=false
• 关键词: CAREER; Branch; Following; Bifurcation; Methods

This Faculty Early Career Development (CAREER) award proposes to research a critical problem currently limiting the technological use of active materials for novel sensor/actuator applications is the lack of accurate analysis and design tools. The overall objective of this project is to develop and validate a branch-following and bifurcation computational methodology capable of systematically mapping out the active behavior of new sensor materials.New atomistic material models will be developed for active material behavior and computational branch-following and bifurcation (BFB) methods, adapted specifically for studying crystalline materials, will be created. These models and methods will be capable of revealing the lattice-level mechanisms responsible for active behavior. The new techniques will be validated against recently obtained experimental data for the shape memory alloy NiTi and its ternary alloys. The results of this project will include the creation of new computational methods for accurately modeling and interrogating the free energy landscape of active materials and will ultimately result in the development of a mature and robust technology for the design of active materials.This work is expected to provide a fresh perspective on the fundamental principles governing the behavior of active materials. In addition, this project will provide individual training for both graduate and undergraduate students. A web-site will be created to make the results of this work broadly accessible and to encourage the development of new interactions and collaborations between the PI and other research groups interested in similar problems. Finally, the PI will integrate research results into the undergraduate and graduate curriculum.

该教师早期职业发展（职业）奖提议研究一个关键问题，目前限制了在新型传感器/执行器应用中使用的技术用途，这是缺乏准确的分析和设计工具。 该项目的总体目的是开发和验证能够系统地绘制新传感器材料的主动行为的分支跟踪和分叉计算方法。新的原子材料模型将用于主动材料行为和计算材料的行为和计算分支遵循和分支分支（BFB）方法，专门用于研究Crystalline Interials，将创建用于研究Crystalline材料的改编。这些模型和方法将能够揭示负责主动行为的晶格级机制。 新技术将根据最近获得的形状内存合金NITI及其三元合金的实验数据进行验证。 该项目的结果将包括创建新的计算方法，以准确建模和询问活跃材料的自由能环境，并最终导致开发一种成熟且强大的技术来设计活性材料。这项工作预计将提供有关主持活跃材料行为的基本原理的全面观点。 此外，该项目将为研究生和本科生提供个人培训。 将创建一个网站，以使这项工作的结果广泛访问，并鼓励PI与对类似问题感兴趣的其他研究小组之间的新互动和合作的发展。 最后，PI将将研究结果整合到本科和研究生课程中。