DMREF: Accelerating the Development of High Temperature Shape Memory Alloys

高温形状记忆合金（htsma）是在高应力和高温下表现出较大形状变化的合金。如果能够控制和定制形状变化行为，htsma可以作为坚固紧凑的固态致动器使用，其性能超过任何其他当前技术。由于htsma的行为高度依赖于化学和加工，因此仅使用实验手段定制特定应用的htsma是不现实的。该奖项支持框架的开发，该框架可以允许化学和加工步骤的设计，以实现这些材料的给定性能要求。这项工作的直接技术影响是加速了用于航空航天和汽车工业的高温固态执行器的发展。此外，该奖项将让七名研究生和两到四名本科生参与一个高度跨学科的研究项目，该项目结合了材料科学、力学、计算机科学、机器学习和设计的思想。这项工作通过整合实验和计算研究，使数字数据可访问，以及培训未来的劳动力，支持与材料基因组计划相关的工作。目前的研究人员和他们的合作者最近发现，NiTiHf htsma中的纳米沉淀在高温下的显著应力下导致了前所未有的循环稳定性和可逆相变。为了加速他们的开发，该研究团队将开发一个框架，根据任意性能要求规定NiTiHf HTSMA的必要初始成分和后续加工时间表：两级物理严格建模方法将化学和加工与性能联系起来。第一级建模通过沉淀模型将化学和加工联系起来，而第二级通过基于热力学的微力学公式将微观结构与形状记忆响应联系起来。在贝叶斯框架中，模型最初使用关于其参数可能值的先验知识进行校准。校准后的模型又被用来设计最优实验，使实验在信息增益或所需材料响应方面的效用最大化，从而提高模型的精细化和可预测性。考虑到模型参数的不确定性和微观结构的异质性，模型通过规定可行的组成和加工集来优化形状记忆响应。总体框架将通过传统渠道传播，而通过这项研究产生的模型、模型参数和数据将通过国家标准与技术研究所开发的材料数据管理系统实例提供给更广泛的科学界。

High Temperature Shape Memory Alloys (HTSMAs) are alloys that exhibit large shape changes at high stresses and high temperatures. If the shape change behavior had be controlled and tailored, HTSMAs can be used as robust and compact solid-state actuators with performance exceeding any other current technology. Since the behavior of HTSMAs is highly dependent on chemistry and processing, tailoring of HTSMAs for specific applications using solely experimental means is unrealistic. This award supports the development of a framework that can allow for the design of chemistry and processing steps to achieve a given performance requirement in these materials. The immediate technological impact of the work is the accelerated development of high-temperature solid-state actuators for the aerospace and automotive industries. Furthermore, the award will expose seven graduate and two to four undergraduate students to a highly interdisciplinary research project, combining ideas from materials science, mechanics, computer science, machine learning and design. The work supports efforts related to the Materials Genome Initiative by integrating experimental and computational research, making digital data accessible, and training the future workforce.The current investigators and their collaborators have recently discovered that nano-recipitation in NiTiHf HTSMAs leads to unprecedented cyclic stability with reversible phase transformation under significant stresses at elevated temperatures. To accelerate their development, this research team will develop a framework to prescribe the necessary initial composition and subsequent processing schedule of a NiTiHf HTSMA based on arbitrary performance requirements: A two-level physically rigorous modeling approach links chemistry and processing to performance. The first modeling level connects chemistry and processing through a precipitation model, while the second level connects microstructure to shape memory response through a thermodynamics-based micromechanics formulation. Within a Bayesian framework, models are initially calibrated using prior knowledge about the likely value of their parameters. Calibrated models are in turn used to design optimal experiments, that maximize the utility of experiments in terms of information gain or desired materials response, that then lead to enhanced model refinement and predictability. Models are in turn used to optimize shape memory response by prescribing feasible composition plus processing sets taking into account uncertainty in model parameters and heterogeneities in microstructure. The overall framework will be disseminated through conventional channels, while the models, model parameters and data generated through this research will be made available to the wider scientific community through an instance of the Materials Data Curation System developed by the National Institute of Standards and Technology.