Modelling and in-situ monitoring of solute cluster effects on grain growth

溶质簇对晶粒生长影响的建模和原位监测

• 批准号: 197278-2010
• 负责人: Militzer, Matthias
• 金额: $ 3.93万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2010
• 资助国家: 加拿大
• 起止时间: 2010-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=452803
• 关键词: Modelling; situ; monitoring; solute; cluster

The metals industry forms a critical part of the Canadian economy where a high level of innovation is required to enable a globally competitive manufacturing sector in Canada. Given the extensive use of metals and alloys any improvement of these materials will enable solutions of pressing societal problems, in particular for sustainable economic growth that mitigates concerns of rapid climate change. Therefore, it is paramount to conduct leading edge metallurgical research in Canada such that Canadians take a leadership role worldwide. The competitiveness of metal producers depends on their ability to develop new alloying strategies and novel and/or optimized processing routes. It is important for researchers to embrace state-of-the-art modelling and experimental methods for the investigation of metals and alloys and to translate these investigations into tools that provide impact to industry. Even though significant progress in research has been made, much detail of the interactions of alloying elements and impurities with moving boundaries and the associated consequences for grain growth and phase transformation kinetics need to be investigated on a much more fundamental level before this knowledge can be used in the development of next generation industrial process models.

金属行业是加拿大经济的重要组成部分，需要高水平的创新才能使加拿大的制造业具有全球竞争力。鉴于金属和合金的广泛使用，这些材料的任何改进都将有助于解决紧迫的社会问题，特别是可持续的经济增长，减轻对快速气候变化的担忧。因此，在加拿大开展前沿冶金研究至关重要，这样加拿大人就可以在全球发挥领导作用。金属生产商的竞争力取决于他们开发新的合金化策略和新的和/或优化的加工路线的能力。研究人员必须采用最先进的建模和实验方法来研究金属和合金，并将这些研究转化为对工业产生影响的工具。即使在研究方面取得了重大进展，合金元素和杂质与移动边界的相互作用以及晶粒生长和相变动力学的相关后果的许多细节需要在更基本的水平上进行研究，然后才能将这些知识用于下一代工业过程模型的开发。