DMREF: Collaborative Research: GOALI: Accelerating Discovery of High Entropy Silicates for Extreme Environments

DMREF：合作研究：GOALI：加速极端环境中高熵硅酸盐的发现

基本信息

批准号：
2219788
负责人：
Cormac Toher
金额：
$ 43.75万
依托单位：
University of Texas at Dallas
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-05-15 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2219788&HistoricalAwards=false
关键词：
DMREF Collaborative Research GOALI Accelerating

项目摘要

Non-technical Description: The efficiency of turbine engines used for power and propulsion can be increased by operating at higher temperatures. However, this approach is limited by available materials that can withstand these extreme environments. In this Designing Materials to Revolutionize and Engineer our Future (DMREF) project, the discovery of new materials that enable higher temperature turbine operation will be accelerated through computational methods that are validated with experimental results. Materials to be studied include mixed rare earth silicates for potential high temperature coatings of turbine engine components. Coatings currently under development use a single rare earth element in the silicate. Mixing various combinations of the fifteen rare earth elements in the silicates provides opportunities to discover and optimize desirable coating properties, including low thermal conductivity and high stability in the reactive turbine engine environments. High throughput computational approaches will be used to understand trends in material properties as the composition is varied. The concept of accelerated material discovery will be taught to the university students involved in the project and the application and importance of materials in engines will be demonstrated to elementary students through outreach activities.Technical Description: This research will accelerate new understanding of the interplay of cation complexity on phase stability of high entropy rare earth silicates in extreme environments. The computation-experiment-feedback loop coupled with machine learning and high throughput computation will result in heretofore unrealized linkages of entropy-induced material stability, thermal properties, and corrosion resistance. The project will result in advances in fundamental understanding and discovery of novel materials that can be designed for specific extreme environment applications. The computational approach to materials discovery will utilize AFLOW: high throughput property prediction. These predictions will be tested by characterizing rare earth silicates synthesized via solid state sintering, chemical techniques for improved cation mixing, and gas phase pulsed laser deposition of thin films. Phase stability and chemical disorder will be characterized through use of techniques including X-ray diffraction and transmission electron microscopy. Resulting stability of rare earth silicate mixtures will inform improvements in the computational approach for materials discovery. Additionally, computational approaches will be used to predict phonon transport and thermal properties. These predicted thermal properties will be compared against thermal conductivity measurements as a function of temperature through use of time domain and steady state thermoreflectance, and hot disk techniques. Environmental stability will be experimentally characterized using "steam-jet" testing, an extreme environment laboratory test creating high-temperature, high-velocity, reactive steam representative of the combustion environment. Results from both the thermal and environmental testing will be used to validate and advance the computational approaches and property-based materials discovery.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.

非技术描述：通过在较高的温度下运行，用于功率和推进的涡轮发动机的效率可以提高。但是，这种方法受到可以承受这些极端环境的可用材料的限制。在这种设计材料以彻底改变和设计我们的未来（DMREF）项目中，可以通过通过实验结果验证的计算方法来加速使较高温度涡轮机操作的新材料的发现。要研究的材料包括混合稀土硅酸盐，用于涡轮发动机组件的潜在高温涂层。目前正在开发的涂层在硅酸盐中使用单个稀土元素。混合硅酸盐中15个稀土元素的各种组合为发现和优化所需的涂层特性，包括低导热率和高稳定性，在反应性涡轮发动机环境中。高通量计算方法将用于理解材料特性的趋势，因为该构图的变化。将向参与该项目的大学生讲授加速材料发现的概念，并通过宣传活动向基本学生证明材料的应用和重要性。技术描述：这项研究将加速对阳离子复杂性在极端环境中高entropy稀土相稳定性上的相互作用的新理解。计算实验反馈回路，加上机器学习和高吞吐量计算，将导致源自熵诱导的材料稳定性，热性能和耐腐蚀性的迄今未实现的联系。该项目将在理解和发现新颖材料的基本理解和发现方面的进步，这些材料可用于特定的极端环境应用。材料发现的计算方法将利用Aflow：高吞吐量属性预测。这些预测将通过表征通过固态烧结，改善阳离子混合的化学技术和薄膜的气相脉冲激光沉积合成的稀土硅酸盐来测试。相位稳定性和化学障碍将通过使用包括X射线衍射和透射电子显微镜在内的技术来表征。稀土硅酸盐混合物的稳定性将为材料发现的计算方法改进。此外，计算方法将用于预测声子传输和热性能。这些预测的热性能将与热导率测量结果进行比较，这是通过使用时域和稳态热室以及热盘技术的温度的函数。环境稳定性将通过“蒸汽喷射”测试实验表征，这是一种极端的环境实验室测试，从而产生高温，高速，反应性蒸汽的燃烧环境中的代表。热和环境测试的结果将用于验证和推进计算方法和基于财产的材料发现。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。