摘要
针对多层各向异性奥氏体不锈钢焊缝中超声相控阵瞬态声场的仿真问题,提出应用高斯声束等效点源模型计算宽带离散化的多个单频稳态声场,通过傅里叶变换将其拓展为瞬态声场,并分析了声场转换过程的主要影响参数。该方法可快速计算焊缝内部超声相控阵聚焦声场的瞬态能量分布和任意一点的时域波形信号。在此基础上针对多层奥氏体不锈钢焊缝内部缺陷的超声相控阵成像检测问题,提出利用上述时域高斯声束法对多通道缺陷散射信号进行时间反转计算,并根据时域声场焦点确定缺陷位置。最后通过实验,验证使用此方法检测实际奥氏体不锈钢焊缝试块内部缺陷的效果。结果表明,提出的方法能够确定缺陷位置,且计算速度快、运算量小,适合作为多层介质内部缺陷实时成像的声场仿真模型。
Aiming at the simulation problem of ultrasonic phased array transient sound field in multi-layer anisotropic austenitic stainless steel welds,it is proposed to apply the Gaussian sound beam equivalent point source model to calculate the broadband discretized multiple single-frequency steady-state sound fields which will be extended to transient sound field by Fourier transform,and the main influencing parameters of the sound field conversion process are analyzed.This method can quickly calculate the transient energy distribution of the ultrasonic phased array focused sound field inside the welds and the time-domain waveform at any point.On this basis,in order to solve the problem of ultrasonic phased array imaging detection of internal defects in multi-layer austenitic stainless steel welds,it is proposed to use the abovementioned time-domain Gaussian beam method to calculate the time inversion of the multi-channel defect scattering signal and determine the defect location according to the focus of time-domain sound field.Finally,the effect of this method to detect the internal defects of the actual austenitic stainless steel welds test block is verified by experiments.The results show that the proposed method can determine the positions of the defects.The calculation speed is fast and the calculation amount is small.It is a suitable real-time imaging method for the internal defects of the multilayer medium.
作者
史慧宇
郭忠存
阎守国
张碧星
黄娟
SHI Huiyu;GUO Zhongcun;YAN Shouguo;ZHANG Bixing;HUANG Juan(Institute of Acoustics,Chinese Academy of Sciences,Beijing 100190;University of Chinese Academy of Sciences,Beijing 100049)
出处
《声学学报》
EI
CAS
CSCD
北大核心
2021年第3期447-455,共9页
Acta Acustica
基金
国家自然科学基金项目(11774377,11574343)
国家重点研发计划项目(2018YFF01012801)资助。
