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.

该学院早期职业发展（CAREER）奖旨在研究目前限制活性材料在新型传感器/执行器应用中的技术使用的一个关键问题，即缺乏准确的分析和设计工具。 该项目的总体目标是开发和验证一种分支跟踪和分叉计算方法，能够系统地绘制新型传感器材料的活性行为。将为活性材料行为和计算分支跟踪和分叉开发新的原子材料模型将创建专门用于研究晶体材料的（BFB）方法。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记忆合金镍钛及其三元合金的实验数据进行验证。 该项目的成果将包括创建新的计算方法，用于精确建模和询问活性材料的自由能景观，并最终开发出一种成熟而稳健的活性材料设计技术。这项工作预计将为控制活性材料行为的基本原理提供了新的视角。 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，以便广泛获取这项工作的结果，并鼓励 PI 和其他对类似问题感兴趣的研究小组之间发展新的互动和合作。 最后，PI 将把研究成果融入本科生和研究生课程中。