旋转换能器无损层析成像的超级计算机模拟

S. Romanov
{"title":"旋转换能器无损层析成像的超级计算机模拟","authors":"S. Romanov","doi":"10.14529/jsfi180318","DOIUrl":null,"url":null,"abstract":"A method of nondestructive ultrasound tomographic imaging employing a rotating transducer system is proposed. The rotating transducer system increases the number of emitters and detectors in a tomographic scheme by several times and makes it possible to neutralize image artifacts resulting from incomplete-data tomography. The inverse problem of tomographic reconstructing the velocity structure inside the inspected object is considered as a nonlinear coefficient inverse problem for a scalar wave equation. Scalable iterative algorithms for reconstructing the longitudinal wave velocity inside the object are discussed. The methods are based on the explicit representation for the gradient of the residual functional. The algorithms employ parallelizing the computations over emitter positions. Numerical simulations performed on the “Lomonosov-2” supercomputer showed that the tomographic methods developed can not only detect boundaries of defects, but also determine the wave velocity distribution inside the defects with high accuracy provided that both reflected and transmitted waves are registered.","PeriodicalId":338883,"journal":{"name":"Supercomput. Front. Innov.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Supercomputer Simulations of Nondestructive Tomographic Imaging with Rotating Transducers\",\"authors\":\"S. Romanov\",\"doi\":\"10.14529/jsfi180318\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A method of nondestructive ultrasound tomographic imaging employing a rotating transducer system is proposed. The rotating transducer system increases the number of emitters and detectors in a tomographic scheme by several times and makes it possible to neutralize image artifacts resulting from incomplete-data tomography. The inverse problem of tomographic reconstructing the velocity structure inside the inspected object is considered as a nonlinear coefficient inverse problem for a scalar wave equation. Scalable iterative algorithms for reconstructing the longitudinal wave velocity inside the object are discussed. The methods are based on the explicit representation for the gradient of the residual functional. The algorithms employ parallelizing the computations over emitter positions. Numerical simulations performed on the “Lomonosov-2” supercomputer showed that the tomographic methods developed can not only detect boundaries of defects, but also determine the wave velocity distribution inside the defects with high accuracy provided that both reflected and transmitted waves are registered.\",\"PeriodicalId\":338883,\"journal\":{\"name\":\"Supercomput. Front. Innov.\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Supercomput. Front. Innov.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14529/jsfi180318\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Supercomput. Front. Innov.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14529/jsfi180318","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

提出了一种利用旋转换能器系统进行无损超声层析成像的方法。旋转换能器系统将层析成像方案中的发射器和检测器的数量增加了几倍,并且可以消除由不完整数据层析成像引起的图像伪影。层析成像重建被测物体内部速度结构的反问题被认为是一个标量波动方程的非线性系数反问题。讨论了用于重建物体内部纵波速度的可扩展迭代算法。该方法基于残差泛函梯度的显式表示。该算法采用对发射器位置的并行计算。在“Lomonosov-2”超级计算机上进行的数值模拟表明,所开发的层析成像方法不仅可以检测缺陷的边界,而且在同时记录反射波和透射波的情况下,可以高精度地确定缺陷内部的波速分布。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Supercomputer Simulations of Nondestructive Tomographic Imaging with Rotating Transducers
A method of nondestructive ultrasound tomographic imaging employing a rotating transducer system is proposed. The rotating transducer system increases the number of emitters and detectors in a tomographic scheme by several times and makes it possible to neutralize image artifacts resulting from incomplete-data tomography. The inverse problem of tomographic reconstructing the velocity structure inside the inspected object is considered as a nonlinear coefficient inverse problem for a scalar wave equation. Scalable iterative algorithms for reconstructing the longitudinal wave velocity inside the object are discussed. The methods are based on the explicit representation for the gradient of the residual functional. The algorithms employ parallelizing the computations over emitter positions. Numerical simulations performed on the “Lomonosov-2” supercomputer showed that the tomographic methods developed can not only detect boundaries of defects, but also determine the wave velocity distribution inside the defects with high accuracy provided that both reflected and transmitted waves are registered.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A Supercomputer-Based Modeling System for Short-Term Prediction of Urban Surface Air Quality River Routing in the INM RAS-MSU Land Surface Model: Numerical Scheme and Parallel Implementation on Hybrid Supercomputers Data Assimilation by Neural Network for Ocean Circulation: Parallel Implementation Multistage Iterative Method to Tackle Inverse Problems of Wave Tomography Machine Learning Approaches to Extreme Weather Events Forecast in Urban Areas: Challenges and Initial Results
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1