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基于超声导波的在役钢轨弹性模量识别

On the Identification of Elastic Moduli of In-Service Rail by Ultrasonic Guided Waves.

作者信息

Zhu Liqiang, Duan Xiangyu, Yu Zujun

机构信息

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China.

Key Laboratory of Vehicle Advanced Manufacturing, Measuring and Control Technology (Beijing Jiaotong University), Ministry of Education, Beijing 100044, China.

出版信息

Sensors (Basel). 2020 Mar 22;20(6):1769. doi: 10.3390/s20061769.

Abstract

Non-destructive rail testing and evaluation based on guided waves need accurate information about the mode propagation characteristics, which can be obtained numerically with the exact material properties of the rails. However, for rails in service, it is difficult to accurately obtain their material properties due to temperature fluctuation, material degradation and rail profile changes caused by wear and grinding. In this study, an inverse method is proposed to identify the material elastic constants of in-service rails by minimizing the discrepancy between the phase velocities predicted by a semi-analytical finite element model and those measured using array transducers attached to the rail. By selecting guided wave modes that are sensitive to moduli but not to rail profile changes, the proposed method can make stable estimations for worn rails. Numerical experiments using a three-dimensional finite element model in ABAQUS/Explicit demonstrate that reconstruction accuracies of 0.36% for Young's modulus and 0.87% for shear modulus can be achieved.

摘要

基于导波的无损钢轨检测与评估需要有关模式传播特性的准确信息,而这些信息可通过钢轨的精确材料特性进行数值获取。然而,对于服役中的钢轨,由于温度波动、材料退化以及磨损和磨削导致的钢轨轮廓变化,很难准确获取其材料特性。在本研究中,提出了一种反演方法,通过最小化半解析有限元模型预测的相速度与使用附着在钢轨上的阵列换能器测量的相速度之间的差异,来识别服役钢轨的材料弹性常数。通过选择对模量敏感但对钢轨轮廓变化不敏感的导波模式,该方法能够对磨损的钢轨进行稳定估计。使用ABAQUS/Explicit中的三维有限元模型进行的数值实验表明,杨氏模量的重建精度可达0.36%,剪切模量的重建精度可达0.87%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30d5/7146350/f0c312f9b8e9/sensors-20-01769-g001.jpg

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