Crack Growth During Fatigue in Ni Superalloys: Physical Origin of Stochastic Jumps and Their Predictive Role Using Statistical Approaches

坚固耐用的材料是我们社会不可或缺的一部分。在航空航天和海洋工业中使用的涡轮机发动机中发现的一类材料被称为超合金。高温合金具有优异的机械性能（强度、抗蠕变性、耐腐蚀性）。然而，由于所需的材料制造工艺，这些材料涉及很大程度的结构无序，这是一个问题。在涡轮机的高温下，由于持续的负载条件，这种无序的影响变得更加明显，导致材料中的微观损伤和裂纹。这些裂纹在机器的使用寿命期间会加剧。因此，了解这些裂纹的行为并防止灾难性的机械故障是至关重要的。在非常不均匀的材料中，裂纹的萌生和扩展不仅是有害的，而且是非常不可预测的，因此需要统计方法和协议来评估构件在疲劳载荷的各个阶段的可靠性。该项目将推进金属异质材料在循环加载（疲劳）过程中随机裂纹扩展跳跃的科学，特别关注镍高温合金。由这种小裂纹产生的机械噪声的有用性在于，它可能包含能够识别涡轮机部件中的损坏程度的独特统计特征。一个由工程师和科学家组成的团队将联合收割机多尺度建模方法、统计方法和实验相结合，最终开发出实验和理论相结合的协议，用于表征疲劳引起的“开裂噪声”和评估机械部件的损坏程度。除了高温合金，这项研究的成果是促进对疲劳损伤的基本认识的进展，并开发非侵入性结构预测方法。一个教育推广计划也计划，涉及研究生，本科生和高中生，以及一般公众，在代表不足EPSCoR状态的西Virginia.Technical Abstract这个项目将推进随机跳跃的理解在疲劳加载的镍高温合金。将采用多尺度建模方法，该方法将联合收割机密度泛函理论（DFT）预测与相场建模相结合。机器学习方法将被纳入相场模型，该模型将根据所进行的实验进行训练。这项研究的成果将是对疲劳损伤的基本理解，可用于预测灾难性故障，特别是当统计抽样有限时。一个工程师和科学家团队将开发一种新的途径来预测金属高温合金疲劳加载期间裂纹扩展的预测模型：通过在恒定应力短时试验假设下对疲劳各阶段的噪声相关性进行统计抽样，我们将使用直接多步预测策略构建预测机器学习框架。在这样做的时候，我们将调查随机裂纹扩展跳跃的基本起源，并将开发一个概率模型，将纳入第一原理的内聚能关系，产生的密度泛函理论预测和相场建模。为了验证我们的模型，我们将使用原位SEM进行一系列控制良好的实验，并使用直流电阻降测量跟踪裂纹生长。裂纹扩展噪声在不同阶段的统计特性作为温度和环境压力的函数将进行比较的多尺度模型预测。然后，将使用经验证的多尺度模型来研究前几个循环期间裂纹扩展事件（根据裂纹长度变化分类）的概率分布，以预测后期的裂纹扩展。结果将是一个经过训练的模型，可以根据早期疲劳事件预测故障。该研究项目具有社会影响力，其基础是金属高温合金疲劳加载期间裂纹扩展跳跃的基本物理起源，这些合金通常用于飞机涡轮机和其他硬件。其目的是开发通用的协议，以促进早期，安全的金属合金裂纹扩展的预测。除了社会影响外，还计划在代表性不足的西弗吉尼亚州EPSCoR州开展一项教育推广计划，包括培训研究生、本科生和高中生以及公众。培训的重点将是使用计算建模材料科学以及对裂纹扩展，断裂和非平衡稀有事件的基本物理特性的深入理解。PI将设计一门课程，将介绍非平衡统计力学和断裂的基础知识，多学科，本科工程环境。

Non-Technical AbstractStrong, durable materials are an integral part of our society. One such class of materials found in turbine engines, used in the aerospace and marine industries, are known as superalloys. Superalloys exhibit excellent mechanical properties (strength, creep resistance, corrosion resistance). However, there is a catch in that these materials involve a large degree of structural disorder as a result of the required material manufacturing process. The effects of such disorder become even more pronounced at the high temperatures of turbines, due to sustained loading conditions, leading to microscopic damage and cracks in the material. These cracks are exacerbated over the lifetime of the machinery. Therefore, it is crucial to understand the behavior of these cracks and prevent catastrophic mechanical failure.Crack initiation and growth in very heterogeneous materials not only can be detrimental but also very unpredictable, thus it requires statistical methods and protocols for assessing the reliability of components at various stages of fatigue loading. This project will advance the science of stochastic crack growth jumps during cyclic loading (fatigue) of metallic heterogeneous materials, with a particular focus on Ni superalloys. The usefulness of the mechanical noise produced by such little cracks is that it might contain distinctive statistical features that can identify the damage level in a turbine component. A team of engineers and scientists will combine multi-scale modeling approaches, statistical methods, and experiments to ultimately develop combined experiment and theory protocols for characterizing the fatigue-induced "cracking noise" and assessing the damage levels of mechanical components. Beyond superalloys, the very outcome of this research is to promote the progress of the fundamental understanding of fatigue damage and develop non-invasive structural prognosis methods. An educational outreach program is also planned that involves graduate, undergraduate, and high-school students, as well as the general public, in the under-represented EPSCoR state of West Virginia.Technical Abstract This project will advance the understanding of stochastic jumps during fatigue loading of Ni superalloys. A multi-scale modeling approach will be employed that will combine density functional theory (DFT) predictions with phase-field modeling. Machine-learning methods will be incorporated into the phase field model, which will be trained based on conducted experiments. The outcome of this research will be the fundamental understanding of fatigue damage that may be used to predict catastrophic failures, especially when there is limited statistical sampling.A team of engineers and scientists will develop a novel pathway to predictive modeling of crack growth during fatigue loading in metallic superalloys: By statistically sampling the noise correlations at various stages of fatigue under the assumption of constant-stress short-time tests, we will build a predictive machine-learning framework using a direct multi-step forecasting strategy. In doing so, we will investigate the fundamental origin of stochastic crack growth jumps and will develop a probabilistic model that will incorporate a first-principles relationship of the cohesive energy, generated by density functional theory predictions and phase-field modeling. To validate our models, we will conduct a series of well-controlled experiments using in-situ SEM and we will track crack growth using DC resistance drop measurements. The statistical properties of crack growth noise at various stages as a function of temperature and environmental pressure will be compared to the multi-scale model predictions. The validated multi-scale model will then be used to investigate the probability distributions of crack growth events (classified in terms of crack-length changes) during the first few cycles to predict crack growth at late stages. The outcome will be a trained model that can predict failure based on early fatigue events.This research project has a societal impact based on the fundamental physical origin of crack growth jumps during fatigue loading of metallic superalloys, which are commonly used on aircraft turbines and other hardware. The aim is to develop general protocols to promote early, safe prediction of crack growth in metallic alloys. In addition to societal impact, an educational outreach program is planned that involve training graduate, undergraduate, and high-school students, as well as the general public, in the under-represented EPSCoR state of West Virginia. The focus of training will be on the use of computational modeling materials science as well as the deep understanding of basic physical properties of crack growth, fracture, and non-equilibrium rare events. The PI will design a course that will introduce the fundamentals of non-equilibrium statistical mechanics and fracture to multidisciplinary, undergraduate engineering environments.