摘要
太阳能热发电换热管主要材料渗铝321钢的损伤会导致换热管的寿命缩短甚至断裂,因此必须进行损伤检测。采用声发射方法对渗铝321钢的损伤特性进行分析,实现对换热管性能的在线动态监测。通过采用声发射Ib值特征来表征渗铝321钢的损伤程度,并运用自组织映射(SOM)神经网络算法进行声发射特征参数聚类,以分析材料的损伤模式。结果表明,力学塑性阶段的声发射事件数量剧增,能量和振铃计数的峰值标志着试件的断裂。此外,在试件失效前,Ib值显著降低且密度变密集,表明Ib值的变化特征可以作为材料临界失效的预警信号。通过SOM算法对特征参数进行聚类分析得到4个簇及其对应的特征频率,并使用扫描电子显微镜(SEM)观察试件的断口形貌,得出4个簇分别对应于孔洞生长与汇合、微裂纹成核、宏观裂纹扩展和纤维状断裂4类损伤模式。这项研究旨在探索金属管材的损伤演化行为,并为管材的损伤分析和健康监测提供依据。
The damage to aluminized 321 steel,which is the main material of solar thermal power heat exchange tube,will lead to the shortening or even fracture of the life of the heat exchange tube.Therefore,the damage detection must be carried out.The damage characteristics of aluminized 321 steel are analyzed by the acoustic emission(AE)method,and the online dynamic monitoring of heat exchange tube performance is realized.The damage degree of aluminized 321 steel is characterized by using the AE Ib-value feature,and the self-organized mapping(SOM)neural network algorithm is used to cluster the AE characteristic parameters to analyze the damage mode of the material.The results show that the number of AE events in the mechanical plastic stage increases sharply,and the peak values of energy and ringing count indicate the fracture of the specimen.In addition,before the failure of the specimen,the Ib-value is significantly reduced and the density becomes dense,indicating that the variation characteristics of the Ib-value can be used as an early warning signal for the critical failure of the material.Four clusters and their corresponding characteristic frequencies are obtained by clustering analysis of the characteristic parameters through the SOM algorithm.The fracture morphology of the specimen is observed by scanning electron microscope(SEM).The four clusters correspond to four types of damage modes,including hole growth and coalescence,micro-crack nucleation,macro-crack propagation,and fibrous fracture.This study aims to explore the damage evolution behavior of metal pipes and provide a basis for damage analysis and health monitoring of pipes.
作者
廖力达
向旭宏
舒王咏
黄斌
罗晓
Liao Lida;Xiang Xuhong;Shu Wangyong;Huang Bin;Luo Xiao(School of Energy and Power Engineering,Changsha University of Science and Technology,Changsha 410114,China;UniSA STEM,University of South Australia,Adelaide,SA 5095,Australia)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2024年第1期211-220,共10页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金项目(51908064)
湖南省自然科学基金项目(2021JJ30717)资助。
关键词
渗铝321钢
声发射
Ib值
SOM神经网络
损伤演化
aluminized 321 steel
acoustic emission
Ib-value
SOM neural network
damage evolution
作者简介
廖力达,2002年于长沙交通学院获得学士学位,2005年于长沙理工大学获得硕士学位,2012年于中南大学获得博士学位,现为长沙理工大学副教授,主要研究方向为动力机械故障诊断、材料损伤检测。E-mail:lld-jdx@163.com;通信作者:向旭宏,2021年于湖南交通工程学院获得学士学位,现为长沙理工大学研究生,主要研究方向为材料损伤检测。E-mail:cslgxxh@163.com。
