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Probabilistic Guided Wave Inversion for Nondestructive Characterization of Stratified Media

用于分层介质无损表征的概率导波反演

基本信息

批准号：
1635291
负责人：
Murthy Guddati
金额：
$ 35.93万
依托单位：
North Carolina State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1635291&HistoricalAwards=false
关键词：
Probabilistic Guided Wave Inversion Nondestructive

项目摘要

The project involves fundamental research aimed at estimating unknown geometric and material properties of layered structures through non destructive evaluation (NDE). This is achieved by sending sound waves into the system and processing the resulting data using advanced computational algorithms rooted in mathematics, mechanics and statistics. Successful completion of the project will advance the state of the art in various areas of high socioeconomic impact: (a) characterizing soil layers for accurate determination of earthquake loads on buildings and bridges, (b) NDE of laminated composites used in civil, mechanical and aerospace engineering, (c) NDE of thin films on substrates, prevalent in electronics industry and materials science, (d) NDE of pavements, the largest part of the nation's infrastructure, and (e) estimation of arterial stiffness that could lead to early diagnosis of cardiovascular diseases. Given its broad nature, the project will naturally lead to interdisciplinary training of graduate students. Further, the project will include the development of education modules for recruitment of high-school students into STEM disciplines.When wave energy is imparted into stratified structures, it propagates along the layers in the form of guided waves that disperse and decay. The dispersion and attenuation characteristics have long been used to nondestructively estimate unknown geometric and material properties of the layered structures, using mathematical and computational algorithms classified as guided wave inversion (GWI). While it is important to quantify uncertainty in the estimated parameters, statistical approaches are rarely used in GWI due to the need for a large number of simulations requiring intense computational resources. The project is aimed at overcoming the bottleneck of computational expense leading to the development of practical probabilistic GWI techniques. The computational cost will be reduced by incorporating two newly developed techniques: (a) complex-length finite element method, based on linking the disparate ideas of finite element discretization and rational (Padé) approximants, and (b) approximate analytical differentiation of the dispersion curves that facilitate faster convergence of inverse iterations. Together, these ideas lead to orders of magnitude reduction in the computational cost. These techniques will be fully developed for complicated structures such as cylindrical waveguides with fluid-structure interaction and multidirectional acquisition, and then be incorporated into a Bayesian statistical inference framework to quantify uncertainty. The final algorithms, which will invert the experimental dispersion and attenuation curves and result in probabilistic reconstructions of stratified media, will be validated using multiple application problems.

该项目涉及基础研究，旨在通过无损评估（NDE）估计层状结构的未知几何和材料特性。这是通过向系统发送声波，并使用植根于数学、力学和统计学的先进计算算法处理结果数据来实现的。该项目的成功完成将在具有高度社会经济影响的各个领域推动最新技术的发展：(a)描述土层特征，以便准确确定建筑物和桥梁的地震荷载；(b)民用、机械和航空航天工程中使用的层压复合材料的无损检测；(c)电子工业和材料科学中普遍使用的基材上薄膜的无损检测；(d)路面的无损检测，这是全国最大部分的基础设施；以及(e)估计动脉硬度，这可能导致心血管疾病的早期诊断。鉴于其广泛的性质，该项目自然会导致研究生的跨学科培训。此外，该项目将包括开发用于招收高中学生进入STEM学科的教育模块。当波能进入分层结构时，它以导波的形式沿层传播，导波会分散和衰减。色散和衰减特性长期以来被用于无损估计层状结构的未知几何和材料特性，使用分类为导波反演（GWI）的数学和计算算法。虽然量化估计参数中的不确定性很重要，但由于需要大量的模拟和大量的计算资源，统计方法很少用于GWI。该项目旨在克服计算费用的瓶颈，从而开发实用的概率GWI技术。计算成本将通过结合两种新开发的技术来降低：(a)复杂长度有限元法，基于将有限元离散化和有理（pad<s:1>）近似的不同思想联系起来，以及(b)色散曲线的近似解析微分，促进逆迭代的更快收敛。总之，这些想法导致计算成本的数量级降低。这些技术将在具有流固相互作用和多向采集的圆柱波导等复杂结构中得到充分发展，然后将其纳入贝叶斯统计推断框架中以量化不确定性。最终的算法将反转实验色散和衰减曲线，并导致分层介质的概率重建，将通过多个应用问题进行验证。