DMREF: Accelerating the Development of High Temperature Shape Memory Alloys

高温形状记忆合金(HTSMA)是一种在高应力和高温下表现出较大形状变化的合金。如果形状变化行为得到控制和定制，HTSMA可以用作坚固紧凑的固态执行器，其性能超过任何其他当前技术。由于HTSMAs的行为高度依赖于化学和工艺，因此仅用实验手段为特定应用量身定做HTSMAs是不现实的。该奖项支持开发一种框架，允许设计化学和加工步骤，以在这些材料中实现给定的性能要求。这项工作的直接技术影响是加速开发用于航空航天和汽车行业的高温固态执行器。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，结合材料科学、力学、计算机科学、机器学习和设计的想法。这项工作通过整合实验和计算研究，使数字数据可访问，并培训未来的工作人员，支持与材料基因组倡议相关的努力。目前的研究人员和他们的合作者最近发现，NiTiHf HTSMAs中的纳米沉淀导致了前所未有的循环稳定性，在高温下的显著应力下发生了可逆的相变。为了加速它们的发展，这个研究团队将开发一个框架，根据任意的性能要求来规定NiTiHf HTSMA所需的初始成分和后续处理时间表：一个两级物理严格的建模方法将化学和处理与性能联系起来。第一个模型层通过沉淀模型连接化学和加工，而第二个模型层通过基于热力学的细观力学公式将微观结构与形状记忆响应联系起来。在贝叶斯框架内，最初使用关于其参数的可能值的先验知识来校准模型。校准后的模型又被用来设计最优实验，最大限度地提高实验在信息增益或所需材料响应方面的效用，从而提高模型精确度和可预测性。考虑到模型参数的不确定性和微观结构的异质性，模型又被用来通过规定可行的成分和工艺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和技术研究所开发的材料数据收集系统实例向更广泛的科学界提供。

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.