| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | August 27, 2015 |
| Latest Amendment Date: | September 11, 2018 |
| Award Number: | 1534534 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Mary Toney
CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | September 1, 2015 |
| End Date: | August 31, 2019 (Estimated) |
| Total Intended Award Amount: | $1,467,133.00 |
| Total Awarded Amount to Date: | $1,667,133.00 |
| Funds Obligated to Date: |
FY 2018 = $200,000.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
3124 TAMU COLLEGE STATION TX US 77843-3124 (979)862-6777 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
TEES State Headquarters College Station TX US 77845-4645 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | DMREF |
| Primary Program Source: |
01001819DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
High-temperature Shape Memory Alloys (HTSMAs) can enable smart actuation of components at elevated temperatures. They can potentially replace expensive, heavy and bulky hydraulic actuators in aerospace vehicles and can enable next-generation capabilities such as wing morphing and active reconfiguration of aerospace systems. Unfortunately, HTSMAs are extremely sensitive to chemistry and processing and their optimization requires considerable experimental and computational efforts, making progress towards optimal HTSMAs extremely slow.
To address this problem, a highly interdisciplinary team with expertise in computational materials science, micromechanics, machine learning, design as well as experimental synthesis and characterization tackled the problem by combining materials science tools with powerful machine learning frameworks.
In this project, the team developed an interdisciplinary framework to accelerate the discovery and design of next-generation HTSMAs based on the Ni-Ti-Hf system. New models and simulations tools were used to better predict the effect of chemistry and processing on the mechanical response of these active materials. Additionally, the team developed entirely new frameworks for the design of HTSMAs with specific active response through the use of Bayesian Optimization approaches. These methods are common in machine learning as they provide the means to optimize the respone of an Artificial Intelligence system with minimal data and are thus especially suited to the materials discovery problem, which is time consuming and quite resource intensive.
The project resulted in a wealth of data on the effect of chemistry and processing on the response of HTSMAs, in addition to more powerful computational models and materials discovery and design frameworks. A major achievement of this project was the discovery of the HTSMA with the lowest hysteresis measured to date. Students and postdoctoral scholars involved in this project were trained in a number of interdisciplinary skills and in some cases participated in an interdisciplinary graduate program on materials science, informatics and design.
Last Modified: 12/23/2019
Modified by: Raymundo Arroyave
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