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.

对高效发动机以产生更绿色的能量的迫切需要驱动涡轮机发动机的操作温度的增加。因此，曾经被认为是“低风险”的部件容易受到高温腐蚀和疲劳损坏。了解裂纹如何在这些环境中形成，生长和阻止它们对于实现更环保的发动机至关重要。目前的寿命预测是基于经验裂纹孵化和增长数据。这种方法需要多年的数据，并且无助于设计新组件。因此，有必要用机械的理解来推进生命预测。为了减轻预后的不确定性，研究人员开发了具有许多长度尺度的物理机制的模型。附加值依赖于较小尺度下损伤机制对载荷条件的较低依赖性。因此，故障预测的进展取决于由独立的多尺度数据提供信息的更先进的模型。