Strain-Memory Effects on Solid-State Transformation

固态转变的应变记忆效应

• 批准号: 2331036
• 负责人: Jean-Briac le Graverend
• 金额: $ 56.69万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2331036&HistoricalAwards=false
• 关键词: Strain; Memory; Effects; Solid; State

The mechanical response of any material depends on its history of prior deformation. This phenomenon, called “strain memory”, greatly affects how materials respond at a certain point in time. Despite its large implications on the mechanical response and lifetime of materials, studies on strain-memory effects are scarce. This is especially true for functional materials that have phase-transforming properties such as shape memory alloys (SMAs). This award supports fundamental research to gain insights into how solid-state transformation, and martensitic transformation in particular, is affected by strain memory in SMAs. Insights from this project will also benefit the understanding of other structural alloys, like steel, in addition to functional materials, like SMAs, by bringing confidence in mechanical performance predictions. These improved predictions have a direct effect on cost savings and life-cycle management. Importantly, research funded by this award also includes outreach activities designed to encourage the participation of neuro-divergent middle and high school students in STEM. Students with “DYS” disorders such as dyslexia will be encouraged to engage in STEM fields where they can excel with the support of learning tools.Extreme-environment materials require accurate performance (mechanical response and lifetime) predicting capabilities that will only be achieved by accounting for strain-memory effects. The synergistic experimental and modeling approach in this project will fill this knowledge gap by providing a fundamental understanding on how thermally- (actuation) and mechanically-induced (superelasticity) solid-state transformations are affected by the history of deformations. This will help formulate a unique two-surface crystal-plasticity model that will be at the cornerstone of improved predictive capabilities for the in-service thermo-mechanical responses of advanced materials with solid-state transformations. However, though crystal-plasticity modeling has many advantages, full-field simulations are computationally expensive and call for faster computational techniques while maintaining texture sensitivity. Hence, a graph neural network (GNN) will also be developed and trained with full-field simulations from crystal-plasticity modeling. The project will, therefore, bring forth accurate and fast predictions of strain-memory effects that will contribute to a long-term engineering solution for materials with solid-state transformation exposed to complex solicitations and microstructures.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.

任何材料的机械响应都取决于其先前变形的历史，这种现象被称为“应变记忆”，它极大地影响了材料在特定时间点的响应，尽管它对材料的机械响应和寿命有很大影响。对于具有相变特性的功能材料（例如形状记忆合金（SMA））来说尤其如此。该奖项支持基础研究，以深入了解固态转变，特别是马氏体转变。受应变记忆影响除了 SMA 等功能材料之外，该项目的见解还将有助于理解其他结构合金，例如 SMA，这些改进的预测对节省成本和延长寿命有直接影响。重要的是，该奖项资助的研究还包括旨在鼓励神经分化中学生参与 STEM 的外展活动，鼓励患有阅读障碍等“DYS”疾病的学生参与他们所在的 STEM 领域。可以出类拔萃在学习工具的支持下。极端环境材料需要准确的性能（机械响应和寿命）预测能力，而这只能通过考虑应变记忆效应来实现，该项目中的协同实验和建模方法将填补这一知识空白。提供对热（驱动）和机械诱导（超弹性）固态转变如何受变形历史影响的基本了解，这将有助于制定独特的两个表面晶体塑性模型。然而，尽管晶体塑性建模具有许多优点，但全场模拟的计算成本很高，需要更快的计算技术，同时保持固态转变。因此，还将通过晶体塑性建模的全场模拟来开发和训练图形神经网络（GNN），从而对应变记忆效应做出准确和快速的预测。一项长期工程该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。