Predictive Modelling of Small Crack Formation in Superalloys

高温合金中小裂纹形成的预测模型

• 批准号: 2436900
• 金额: --
• 依托单位: CRANFIELD UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2436900
• 关键词: Predictive; Modelling; Small; Crack; Formation

The urgent need for high efficiency engines to produce greener energy drives the increase in operating temperature of turbine engines. Consequently, components that were once considered 'low risk' are susceptible to high-temperature corrosion and fatigue damage. Understanding how cracks form, grow and arrests them in these environments is critical to enable greener engines. Current life prognosis is based on empirical crack incubation and growth data. This approach requires years of data and does not aid in designing new components. Hence, there is a need to advance life predictions with mechanistic understanding. To mitigate prognosis uncertainty, researchers developed models informed with physical mechanisms at many length scales. The value added relies on the lower dependence of damage mechanisms on loading conditions at smaller scales. Hence, advances in failure prognosis depends on more advanced models that are informed by independent multiscale data.

迫切需要高效发动机生产更绿色的能源会驱动涡轮发动机的工作温度升高。因此，曾经被认为是“低风险”的组件容易受到高温腐蚀和疲劳损害。了解在这些环境中如何形成裂纹，成长和逮捕它们对于启用更绿色的发动机至关重要。当前的寿命预后基于经验裂纹孵化和生长数据。这种方法需要多年的数据，并且无助于设计新组件。因此，有必要通过机械理解来提高生活预测。为了减轻预后不确定性，研究人员开发了许多长度尺度的物理机制的模型。增值的依赖于损坏机制对较小尺度下的加载条件的较低依赖性。因此，失败预后的进展取决于更先进的模型，这些模型由独立的多尺度数据告知。