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

镍高温合金疲劳过程中的裂纹扩展：随机跳跃的物理起源及其使用统计方法的预测作用

• 批准号: 1709568
• 负责人: Terence Musho
• 金额: $ 42.33万
• 依托单位: West Virginia University Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1709568&HistoricalAwards=false
• 关键词: Crack; Growth; During; Fatigue; Ni

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.

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发动机中发现的一种这样的材料被称为高温合金。高温合金具有优异的机械性能(强度、抗蠕变性能、耐腐蚀性能)。然而，有一个问题是，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。由于持续的负载条件，这种无序的影响在涡轮机的高温下变得更加明显，导致材料的微观损伤和裂缝。这些裂纹在机器的使用寿命内会加剧。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。裂纹在非均质材料中的萌生和扩展不仅是有害的，而且非常不可预测，因此需要统计方法和协议来评估构件在疲劳加载的各个阶段的可靠性。该项目将推进金属非均质材料在循环加载(疲劳)过程中的随机裂纹扩展跳跃的科学研究，特别是对镍高温合金的研究。这种微小裂纹产生的机械噪音的有用之处在于，它可能包含独特的统计特征，可以识别涡轮机部件的损坏程度。一组工程师和科学家将结合多尺度建模方法、统计方法和实验，最终开发出综合的实验和理论方案，用于表征疲劳引起的“破裂噪声”并评估机械部件的损伤程度。除了高温合金，这项研究的结果也促进了对疲劳损伤的基本认识的进步，并开发了非侵入性的结构预测方法。西弗吉尼亚州EPSCoR州的研究生、本科生和高中生以及普通公众也参与了一项教育推广计划。技术摘要该项目将促进对镍高温合金疲劳加载过程中随机跳跃的理解。将采用多尺度建模方法，将密度泛函理论(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，该模型将根据进行的实验进行训练。这项研究的结果将是对疲劳损伤的基本理解，可以用来预测灾难性的失效，特别是在统计样本有限的情况下。一个工程师和科学家团队将开发一种新的方法来预测金属高温合金疲劳加载过程中的裂纹扩展：通过在恒应力短时测试的假设下对疲劳不同阶段的噪声相关性进行统计采样，我们将使用直接的多步预测策略建立一个预测机器学习框架。在此过程中，我们将研究随机裂纹扩展跳跃的基本来源，并将开发一个概率模型，该模型将包含由密度泛函理论预测和相场模拟产生的内聚能的第一原理关系。为了验证我们的模型，我们将使用原位扫描电子显微镜进行一系列控制良好的实验，并使用直流电阻降测量来跟踪裂纹的扩展。将不同阶段裂纹扩展噪声的统计特性作为温度和环境压力的函数与多尺度模型预测进行比较。经过验证的多尺度模型随后将用于研究裂纹扩展事件的概率分布(根据裂纹长度的变化进行分类)，以预测后期阶段的裂纹扩展。结果将是一个训练有素的模型，可以基于早期疲劳事件预测故障。这项研究项目基于金属高温合金疲劳加载期间裂纹扩展跳跃的基本物理来源，具有社会影响。金属高温合金通常用于飞机涡轮机和其他硬件。其目的是开发通用方案，以促进早期、安全地预测金属合金中的裂纹扩展。除了社会影响，还计划在代表不足的西弗吉尼亚州EPSCoR州培训研究生、本科生和高中生以及普通公众。培训的重点将是使用计算模拟材料科学以及对裂纹扩展、断裂和非平衡罕见事件的基本物理属性的深入理解。PI将设计一门课程，将非平衡统计力学和断裂的基本原理介绍给多学科的本科工程环境。

项目成果

Experimental Investigation of Stochastic Jumps during Crack Initiation and Growth in IN718

• 发表时间: 2019-06
• 期刊: arXiv: Applied Physics
• 作者: Joel Lindsay;S. Papanikolaou;T. Musho

Λ -Invariant and Topological Pathways to Influence the Strength of Submicron Crystals

Î -影响亚微米晶体强度的不变和拓扑途径

• DOI: 10.1103/physrevlett.124.205502
• 发表时间: 2020
• 期刊: Physical Review Letters
• 影响因子: 8.6
• 作者: Papanikolaou, Stefanos;Po, Giacomo
• 通讯作者: Po, Giacomo

Microstructural inelastic fingerprints and data-rich predictions of plasticity and damage in solids

• DOI: 10.1007/s00466-020-01845-x
• 发表时间: 2019-05
• 期刊: Computational Mechanics
• 影响因子: 4.1
• 作者: S. Papanikolaou

Machine learning approach to transform scattering parameters to complex permittivities

• DOI: 10.1080/08327823.2021.1993046
• 发表时间: 2021-10
• 期刊: Journal of Microwave Power and Electromagnetic Energy
• 影响因子: 1.5
• 作者: Robert Tempke;Liam A Thomas;Christina Wildfire;D. Shekhawat;T. Musho