Appraising Kirchhoff approximation theory for the scattering of elastic shear waves by randomly rough defects

Appraising Kirchhoff approximation theory for the scattering of elastic shear waves by randomly rough defects
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DOI:
10.1016/j.jsv.2019.114872
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发表时间:
2019-11-10
影响因子:
4.7
通讯作者:
Shi, Fan
Shi, Fan
中科院分区:
工程技术2区
文献类型:
--
作者:
Haslinger, Stewart G.;Lowe, Michael J. S.;Shi, Fan

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快速和准确的方法,基尔霍夫近似(KA)的基础上,开发来评估粗糙缺陷的剪切波的散射和量化这种近似的准确性。缺陷粗糙度对超声反射有很大的影响,并且每个粗糙缺陷具有不同的表面,因此基于光滑缺陷评估检测灵敏度的标准方法必然受到限制。在无损评价(NDE)检测中准确地分辨粗糙裂纹通常需要横波,因为横波对表面粗糙度的敏感性高于纵波。KA模型是有吸引力的,因为它们可以快速部署,但它们是近似值,重要的是要确定超声剪切波散射的有效范围;这个范围在这里找到。KA和高保真度有限元模拟之间的良好协议获得的入射/散射角的范围内,和KA的有效性的限制被发现是严格得多的比纵波入射;当粗糙表面的相关长度减小到入射剪切波长的量级时,多次散射和表面波模式转换的组合导致KA预测偏离真实漫散射场的KA预测。(C)2019爱思唯尔有限公司版权所有。
Rapid and accurate methods, based on the Kirchhoff approximation (KA), are developed to evaluate the scattering of shear waves by rough defects and quantify the accuracy of this approximation. Defect roughness has a strong effect on the reflection of ultrasound, and every rough defect has a different surface, so standard methods of assessing the sensitivity of inspection based on smooth defects are necessarily limited. Accurately resolving rough cracks in non-destructive evaluation (NDE) inspections often requires shear waves since they have higher sensitivity to surface roughness than longitudinal waves. KA models are attractive, since they are rapid to deploy, however they are an approximation and it is important to determine the range of validity for the scattering of ultrasonic shear waves; this range is found here. Good agreement between KA and high fidelity finite element simulations is obtained for a range of incident/scattering angles, and the limits of validity for KA are found to be much stricter than for longitudinal wave incidence; as the correlation length of rough surfaces is reduced to the order of the incident shear wavelength, a combination of multiple scattering and surface wave mode conversion leads to KA predictions diverging from those of the true diffuse scattered fields. (C) 2019 Elsevier Ltd. All rights reserved.