Rational Models and Dynamical Characterization of Fatigue using Phase Space Warping and Smooth Orthogonal Decomposition

使用相空间扭曲和平滑正交分解的有理模型和疲劳动态表征

• 批准号: 0758536
• 负责人: David Chelidze
• 金额: $ 30万
• 依托单位: University of Rhode Island
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-06-15 至 2012-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0758536&HistoricalAwards=false
• 关键词: Rational; Models; Dynamical; Characterization; Fatigue

An accurate prognosis of fatigue failure and crack growth is beyond current state of the art. Current research is almost exclusively focused on finding the ?correct? physical damage variable. In contrast, the aim of this work is to shift this focus to abstract, slowly evolving, dynamical fatigue damage variables?generalized fatigue damage coordinates?that obey some simple mathematical form of a governing differential equation. Carefully designed experiments will show what these observable coordinates are and how they evolve in time using new multivariate and nonlinear time series analyses (i.e., smooth orthogonal decomposition and phase space warping). Based on this evidence, rational fatigue models, which govern these abstract damage variables and have needed mathematical structure to provide experimentally observed dynamical characteristics, will be developed. Experimental characterization of fatigue and corresponding model identification will be carried out using a new experimental apparatus allowing fatigue damage accumulation in various loading environments, with natural dynamic coupling between the damage evolution and structural dynamics. From the fundamental standpoint, this work will lead to improved understanding of fatigue damage accumulation dynamics, its interaction with structural dynamics and loads, and it will provide tools for the development and/or verification predictive damage evolution models. These tools will be instrumental in developing true prognostic ability for structural health monitoring (SHM) and condition based maintenance (CBM) technologies.The experimental data analysis tools and models developed in this study will have a major impact on a wide variety of SHM and CBM applications. The same methodology can also be applied to characterize other slowly evolving hidden processes, such as physiologic fatigue, driving nonstationary forces in geophysical and oceanographical processes, disease progression, etc. This work also supports collaborations with international research groups as well as with one national laboratory. One Ph.D. level graduate and several undergraduate students will directly benefit from the proposed activities. The interdisciplinary nature of the proposed effort will enrich the learning experience of students by exposing them to modern nonlinear and multivariate data analysis techniques and their applications in mechanical engineering and other fields. A wider set of students will also acquire a better appreciation for the use of these tools in the dynamical characterization of hierarchical systems due to the planned integration of research into education activities. The development of a new undergraduate course on SHM is expected to enhance the undergraduate mechanical engineering program at the University of Rhode Island by exposing students to a new career field.

疲劳失效和裂纹扩展的准确预测超出了目前的技术水平。目前的研究几乎完全集中在寻找“正确的”？物理损伤变量。相比之下，这项工作的目的是将重点转移到抽象的，缓慢发展的动态疲劳损伤变量上。广义疲劳损伤坐标？它们服从控制微分方程的一些简单数学形式。精心设计的实验将展示这些可观察的坐标是什么，以及它们如何使用新的多元和非线性时间序列分析（即平滑正交分解和相空间翘曲）随时间演变。基于这一证据，将开发合理的疲劳模型，以控制这些抽象的损伤变量，并需要数学结构来提供实验观察到的动力特性。疲劳的实验表征和相应的模型识别将使用一种新的实验装置，允许在各种载荷环境下疲劳损伤积累，损伤演化与结构动力学之间具有自然的动态耦合。从基础的角度来看，这项工作将提高对疲劳损伤积累动力学及其与结构动力学和载荷的相互作用的理解，并将为开发和/或验证预测损伤演化模型提供工具。这些工具将有助于为结构健康监测（SHM）和基于状态的维护（CBM）技术开发真正的预测能力。本研究开发的实验数据分析工具和模型将对各种SHM和CBM应用产生重大影响。同样的方法也可用于描述其他缓慢演变的隐藏过程，如生理疲劳、地球物理和海洋学过程中的非稳态驱动力、疾病进展等。这项工作还支持与国际研究小组以及一个国家实验室的合作。一名博士研究生和几名本科生将从拟议的活动中直接受益。本研究的跨学科性质将使学生接触到现代非线性和多元数据分析技术及其在机械工程和其他领域的应用，从而丰富他们的学习经验。由于计划将研究整合到教育活动中，更多的学生也将更好地了解这些工具在分层系统动态特征中的使用。开发一门新的SHM本科课程，旨在通过让学生接触一个新的职业领域，提高罗德岛大学机械工程本科课程的水平。