Predictive Modelling of Small Crack Formation in Superalloys

对高效发动机产生绿色能源的迫切需求推动了涡轮发动机工作温度的提高。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伤。了解裂缝是如何在这些环境中形成、发展和阻止它们的，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孕育和生长数据。这种方法需要多年的数据，而且无助于设计新的组件。因此，有必要通过机械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。在较小的尺度下，损伤机制对加载条件的依赖性较低，从而增加了附加价值。因此，故障预测的进展依赖于由独立的多尺度数据提供信息的更先进的模型。

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.