Suppr超能文献

一种基于兰姆波模拟和有限实验数据的疲劳裂纹尺寸评估方法。

A Fatigue Crack Size Evaluation Method Based on Lamb Wave Simulation and Limited Experimental Data.

作者信息

He Jingjing, Ran Yunmeng, Liu Bin, Yang Jinsong, Guan Xuefei

机构信息

School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China.

China Academy of Launch Vehicle Technology, Beijing 100071, China.

出版信息

Sensors (Basel). 2017 Sep 13;17(9):2097. doi: 10.3390/s17092097.

Abstract

This paper presents a systematic and general method for Lamb wave-based crack size quantification using finite element simulations and Bayesian updating. The method consists of construction of a baseline quantification model using finite element simulation data and Bayesian updating with limited Lamb wave data from target structure. The baseline model correlates two proposed damage sensitive features, namely the normalized amplitude and phase change, with the crack length through a response surface model. The two damage sensitive features are extracted from the first received S₀ mode wave package. The model parameters of the baseline model are estimated using finite element simulation data. To account for uncertainties from numerical modeling, geometry, material and manufacturing between the baseline model and the target model, Bayesian method is employed to update the baseline model with a few measurements acquired from the actual target structure. A rigorous validation is made using in-situ fatigue testing and Lamb wave data from coupon specimens and realistic lap-joint components. The effectiveness and accuracy of the proposed method is demonstrated under different loading and damage conditions.

摘要

本文提出了一种基于兰姆波的裂纹尺寸量化系统通用方法,该方法采用有限元模拟和贝叶斯更新。该方法包括使用有限元模拟数据构建基线量化模型,并利用来自目标结构的有限兰姆波数据进行贝叶斯更新。基线模型通过响应面模型将两个提出的损伤敏感特征,即归一化振幅和相位变化,与裂纹长度关联起来。这两个损伤敏感特征是从第一个接收到的S₀模式波包中提取的。基线模型的模型参数使用有限元模拟数据进行估计。为了考虑基线模型与目标模型之间数值建模、几何形状、材料和制造方面的不确定性,采用贝叶斯方法,利用从实际目标结构获取的少量测量数据对基线模型进行更新。使用原位疲劳测试以及来自试样和实际搭接接头部件的兰姆波数据进行了严格验证。该方法在不同加载和损伤条件下的有效性和准确性得到了证明。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b921/5621154/f9e4d0b27931/sensors-17-02097-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验